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Commercialization prospects for advanced Low

Altitude Remote Sensing Systems in Precision

Agriculture

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This text is a bit simplified adaptation of my paper prepared for the Master of Agriculture capstone project.

The original paper was supervised by Professor Henry An (Faculty of Agricultural, Life and Environmental Sciences)

and Professor Joel Gehman (School of Business) at the University of Alberta during May-June 2015. The original

paper received attention from industry professionals from sensors technology sector and Ag consultants but was

never prepared for publishing due to other priorities. I put in onto LinkedIn/Twitter for those people I have

promised it earlier and to show what I was interested in as a student. If you are a sensor technology specialist, go

only to the “Adoption discussion” part, if you are an Ag/Forestry/Environment remote sensing specialist, also visit

the “Precision Agriculture applications”. If you are a general Ag professional, it is better to start from the end and

move to the beginning of the text.

It is now 7 months from the day when the original paper was prepared and a lot of things have changed in the

rapidly evolving industry. Nevertheless, I would keep the main predictions the same, even with the bio

commodities prices being under pressure compared to the last decade performance. The adoption is slow but

happening: mostly at the Ag, Forestry and Environmental professionals’ level and at specialized imagery

acquisition firms rather than primary producers. We are still far-far away from commercial hyperspectral sensing,

and reliable interpretation algorithms rather than investment cost may become a long-term concern.

On the other hand, costs associated with thermal sensing and LIDAR are decreasing and maybe their

advancement will become faster than I have expected. LIDAR has become very interesting for Forestry

professionals while large livestock feedlots and cow/calf operators are getting more interested in simple thermal

sensing. It also seems that timing convenience (temporal resolution) is more important than I previously thought

and may be a definite competitive edge over the traditional manned aircraft systems.

Mykhailo (Mike) Vorona

January 31st, 2016

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Contents Introduction ............................................................................................................................................................................................... 4

Adoption discussion ............................................................................................................................................................................... 6

Technology readiness and probable adoption sequence ................................................................................................... 6

Market trends and economics principles guiding commercialization ......................................................................... 7

Available market information and case estimation ............................................................................................................. 9

Precision Agriculture applications ................................................................................................................................................. 11

Traditional multispectral sensing and its data interpretations .................................................................................... 11

The UAS platform: introduction to traditional multi-spectral sensing ...................................................................... 13

Hyperspectral single bands and indices superiority and their processing complexity ...................................... 15

Hyperspectral sensing: vegetation discrimination opportunities and weed control .......................................... 17

Hyperspectral sensing: advanced crop disease analyses................................................................................................. 19

Hyperspectral and thermal sensing: pastures management .......................................................................................... 20

Hyperspectral and thermal sensing: water content analyses ........................................................................................ 21

Hyperspectral sensing: advanced macro-nutrients content analyses ........................................................................ 22

Hyperspectral and thermal sensing: advanced soil analyses ......................................................................................... 23

The UAS platform for hyperspectral and thermal sensing and integration with active sensing technology

(LIDAR) ................................................................................................................................................................................................. 24

Remote sensing technology overview .......................................................................................................................................... 25

Spectral resolution ........................................................................................................................................................................... 25

Spatial resolution .............................................................................................................................................................................. 27

Temporal resolution ........................................................................................................................................................................ 27

Radiometric resolution .................................................................................................................................................................. 27

Active / Passive sensors ................................................................................................................................................................ 28

Platforms .............................................................................................................................................................................................. 28

Remote sensing underlying physics .............................................................................................................................................. 29

References ................................................................................................................................................................................................ 32

Images are from Bigstock unless stated otherwise

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Introduction

In this capstone project, I analyze the prospects of the Low Altitude Remote Sensing Systems (LARS) as the newest remote sensing system in precision agriculture. I primarily focus on its possible cost advantage over standard satellite-based sensors and land machinery-mounted sensors in nitrogen management, irrigation, yield prediction, crop disease stress and soil mapping [2]. In addition, I will consider new applications brought by advances in hyperspectral sensing technology, such as other-than nitrogen nutrient management (i.e., potassium, sulphur and phosphorous [6]). I will also pay attention to new opportunities brought by the flexibility of LARS application in time, space and sensor modification as compared to traditional satellite and vehicle platforms.

Basically, this is a detailed technical literature review resulting in making predictions about technology commercialization timing and sequence. It is accompanied with the brief introductory into the remote sensing technology itself and its history of application primarily in agriculture and partially in forestry.

The Unmanned Aerial Systems (UAS) technology presents itself with significant benefits for precision agriculture purposes as it significantly reduces the costs associated with farm-level remote sensing [11], [15] when compared to other non-proximal available alternatives such as satellites, helicopters or other airborne methods. Furthermore, it significantly increases flexibility of usage in terms of time, as there is no need to wait up to 44 days for a satellite returning to the exact point [4] or request a helicopter or airborne engagement. Because it operates on the low altitude, it also allows us to increase significantly the spatial resolution of sensing (e.g., pixel resolution in acquired images). An increase in spatial resolution provides us with abundant opportunities to increase the analysis precision [5].

Furthermore, the adoption of the advanced remote sensing technologies such as hyperspectral and thermal sensing in precision agriculture provides us with great opportunities as well. Hyperspectral sensing significantly increases the accuracy of vegetation and soil analysis when compared to traditional multispectral sensing [4], [20], [21], [26], [27], and [31]. It will help us to monitor the level of macronutrients in plants, soil conditions, weed and disease stresses much more precisely. It may even provide services currently not possible. For instance, hyperspectral sensing gives us a possibility to monitor other than nitrogen macronutrient content [6]. In turn, it may enable us to manage the usage of resources such as fertilizers, pesticides and water much more efficiently.

I will start right from the discussion of possible commercialization paths for the most probable adoption scenarios. I will use the information provided by industry specialists, work in existing research papers, publicly available pricing information and interviews with current technology users.

I will then start building my arguments first with identifying the most probable technical solutions for adoption, solely based on reported research progress, and range them in time, based on the level of research accumulated to date. Then I will continue with considering general market trends pertaining to adoption, such as precision agriculture uptake in North America, estimation of crop services industry growth and profitability surveys of UAS distributors. I will also discuss how economics principles such as comparative advantage, risk aversion and the law of flat pay-off functions applicable to many agricultural inputs may affect the adoption rate and scale for new precision agriculture technology in North America.

Further, I will continue with an overview of the historical adoption of remote sensing technologies in precision agriculture, such as satellites imagery, fertilizer sensors mounted on tractors, etc. I will also summarize the on-going research in remote sensing pertinent to agriculture focusing on hyperspectral and thermal-type sensors and use of the Unmanned Aerial Systems (UAS) as a new platform. UAS, which are usually called drones or Unmanned Aerial Vehicles (UAV), constitute the most recent and possibly most promising platform suitable for the LARS adoption.

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In the last part of the paper, I will give a brief physics overview behind the idea of remote sensing and will go further discussing background details about the technology and finally ending up with practical sensors classification. Then I will continue with providing a brief explanation on how sensor data can be transformed into information applicable for a decision-making agent in agriculture via different vegetation indices.

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Adoption discussion

The growing body of literature suggests that both the platform and advanced types of remote sensing propose superior alternatives to existing applications and create new opportunities in precision agriculture.

Both the UAS as platform and advanced remote sensing are at the infant stages in precision agriculture adoption now. In this part of the paper, I will focus on summarizing the research conducted to date and predict how it affects adoption sequence in terms of technology and precision agriculture applications. I will then consider market trends and economics principles to understand better the landscape of the industry and how it may direct the uptake. Finally, I will present the market and pricing information found throughout published papers, industry reports, publicly available information and discussions with industry agents. I will try to connect the market and pricing information to most probable adoption cases.

An important assumption I will make is that favourable aerospace regulations for the industry will continue or be introduced. While this holds true for many countries, there are still major concerns for the United States, which may hinder the UAS adoption by precision agriculture agents in North America.

Technology readiness and probable adoption sequence

Based on the conclusions in individual and meta-studies research reviews presented in this paper, I expect the UAS platform and hyperspectral sensing to be the technologies most ready for adoption in precision agriculture. The UAS platform is already largely available for use in precision agriculture with many studies conducted using it as a platform of choice and numerous UAS manufacturers exhibiting their products for precision agriculture purposes during the Precision Aerial Ag Conference 2014 [47].

The research community has also accumulated a significant body of knowledge on hyperspectral sensing in precision agriculture, which can be ready for commercialization. Furthermore, leading sensors manufacturers have already established hyperspectral product lines and mention agriculture as one of the applications areas in their promotion materials. Many of them designed new sublines specifically for the UAS platform, such as µVNIR-1920 by Itres or Micro-Hyperspec by Headwall. These smaller solutions enable hyperspectral sensing for hundreds of bands across all spectrum regions up to and including 2500 nm in SWIR part.

The multi-bands sensing in thermal part of the spectrum is probably will be adopted next, but not in any near future. As research presented in this paper indicates, for precision agriculture purposes alone, hyperspectral sensing provides similar or better results than thermal sensing now and while thermal sensing is a promising area for future research, it is in no position for adoption now.

LIDAR technology commercialization is an even more remote opportunity. With LIDAR usage being studied only in forestry, it definitely needs much more time and effort before considering it as a viable technology for precision agriculture purposes. Microwave sensing adoption, in turn, is tightly connected with LIDAR as powerful active sensors are required for its successful application. Therefore, it is probably the most remote commercialization opportunity for precision agriculture purposes. In summary, this is the order in which each technology will likely be adopted:

Figure 6: Expected advanced LARS adoption sequence

UAS HyperspectralMulti-band

thermalLIDAR Microwave

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It is more difficult to predict the technology adoption in terms of applications as the same application may utilize both hyperspectral and multi-band thermal sensing simultaneously, which is most likely to happen in irrigation management for instance. Another example might be an application that combines hyperspectral and LIDAR technology, which seems to be the most beneficial tool for weed sensing and herbicide variable rate management. Nevertheless, it is easy to establish several major drivers guiding an adoption for specific applications. Pastures and soils will require the most sophisticated and advanced sensing and data management tools for successful application, while more accurate yield predictions and nitrogen management using UAS and hyperspectral sensing are the most easy to implement. There is also an already on-going adoption of the UAS with multi-band sensing present for general crop scouting that is transformed into yield prediction, nitrogen management, lodging and wildlife damages detection.

There are also irrigation management, disease and weed control, other-than-nitrogen nutrients management applications whose adoption rates and timeliness are vague. The adoption of these applications will depend on the results of further research, technology advances, and technology and data management costs. As of now, irrigation and other-than-nitrogen nutrient management look as more close opportunities while disease and weed control being more remote. The latter applications are often mentioned in research papers as those requiring higher spatial resolutions achievable only with proximal sensing at the moment and more advanced data mining and analyses techniques. Both these factors result in much higher associated costs and therefore I expect their adoption to occur gradually in a more distant future.

Market trends and economics principles guiding commercialization

There is an ongoing debate around the business cases of UAS adoption in precision agriculture concerning potential growth rate and eventual size of this application. The technology has significantly improved over the last decade and the 2013 AUVSI study in the United States identified precision agriculture as the largest near term commercial market [14]. On the other hand, there are concerns regarding the AUVSI report quality [49] and underlying business case. A Canadian report on UAS adoption summarizes concerns for precision agriculture: “The two primary areas of uncertainty are the business case and the relationship between the various categories of information that can be collected and their perceived value to a very pragmatic and diverse end -user community” [14].

Regarding the business case, the majority of concerns are related to slower than expected adoption rate of diagnostic tools in precision agriculture [50]. The precision agriculture industry as a whole looks promising with expected growth at 5.7% during 2015-2020 [44], especially when compared to traditional crop services industry expected decline of -0.3% for the same period [43]. However, there are concerns regarding precision agriculture several product groups: diagnostic tools that include aerial imagery, general farm management tools and variable rate technology for a range of inputs [50]. The general reason for their lagging growth is the struggle in making their adoption value obvious to farmers [50].

The Drone Analyst article provides two more quantifiable examples quoting surveys of the Iowa farmers 25% of whom only use aerial imagery for nitrogen management decisions and only 21% of aerial imagery providers for precision agriculture in the combined Purdue/Croplife survey said the service was profitable [48].

The forces for and against the adoption of the advanced remote sensing solutions in precision agriculture represent three major economics principles pertinent to the situation: comparative advantage, risk-aversion of farmers and flat payoff functions often found in nutrients and pesticides applications.

Many UAS studies and business cases such as AUVSI 2013 report [49] and MarketLine UAV case [46] used Japanese UAS uptake in agriculture to model the possible uptake scale and economic effects in North American

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environment. Japanese agriculture is largely rice-focused with a different size, capacity and set of agricultural practices. Rice production is carried out by millions of smallholdings with less than 7.5 acres per holding that cooperate extensively. It experiences significant labour shortages because small production scale limits drive people away from farming. Furthermore, it is difficult to scout and apply inputs in this production systems as it is almost exclusively situated in the specially laid out rice paddies with extensive irrigation system. In addition, rice production is heavily regulated with the extremely complex incentive system that supports very high producer prices, up to ten times higher than the world rice price [51]. This combination of factors provides the very different adoption environment for the UAS in Japanese agriculture, which started in 1980s. The US and Canada have very different comparative advantage for agricultural production that does not create nearly as strong incentive for the adoption as it did in Japan.

The high risk aversion level of farmers compared to other participants in the supply chain is also a widely accepted assumption for two main reasons. First, because producers operate at a smaller scale than the majority of the input companies or marketing and food processing agents downstream. Second, because as mentioned in OECD 2009 report on agricultural risk, these is the long-standing issue of farmers lacking to adjust their quasi-fixed inputs as market conditions changes [54]. The same report mentions the study conducted by Musshoff and Hirschauer concluding in 2008 that asset fixity has slowed the adoption of organic production in Germany and Austria. At the same time, the report stresses that the relationship between asset fixity and risk management is somewhat more tenuous and there is little literature addressing the riskiness of farm versus non-farm firms [54].

The risk aversion generally slows down the adoption rate of new technologies, as they are associated with higher variability and thus uncertainty. Farmers usually perceive new technology with a high level of uncertainty and the adoption process may take a lot of time and non-trial and trial evaluation phases with many social factors influencing it [41]. Industry participants express their individual concerns regarding timelines and actual applications as well. Dr. Dave Franzen, an extension soil specialist at North Dakota State University, expects gradual adoption of new significant site-specific movements forward to take about 15 years at the producer level [10]. Overview of the professional farmers’ forums, discussion boards and private conversations may further reveal the producers’ preference for waiting when it comes to new diagnostic tools, which the remote sensing represents.

The industry realizes this problem and puts additional effort into visualizing the UAS benefits to farmers. For example, the drone services company Measure is doing tests over the US cornfields in partnership with PrecisionHawk and the American Farm Bureau in order to develop an online tool, a return-on-investment calculator, to help farmers understand if the UAS makes sense for them. The American Farm Bureau wants to quantify expected returns for farmers using the UAS. Justin Oberman, the president of Measure, says that “there are cases where it may not make sense”. Will Rodger, the American Farm Bureau's director of policy communications, thinks that “It seems almost axiomatic that it [the UAS] will be successful … The question is when and where is this going to be something that farmers should use”. [56]

Finally, in agricultural economics the flat payoff functions patterns may be found for many agricultural inputs, including nitrogen. These patterns suggests that there may exist a significant flat region of input application after entering which precision agriculture loses its value to a producer. This theory suggests that precision agriculture services might be more expensive to a farmer than using more of an input to make sure that the flat payoff region is reached [41].

While the arguments presented in this discussion part of the paper do not question the probable adoption of advanced remote sensing in precision agriculture in the long-term, they show that actual commercialization paths may be very vague in terms of scale and time.

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Available market information and case estimation

In this final part of the paper, I will focus on the most probable commercialization paths for advanced LARS in precision agriculture applications. In previous parts of the paper, I came to the conclusion that the UAS platform and hyperspectral sensors would be the most probable technologies for adoption in preliminary crops scouting, yield prediction, nitrogen management, lodging and wildlife damages detection and possibly in irrigation and other-than-nitrogen nutrient management. Other technologies and applications are probably too remote in time and therefore I have omitted any discussion of them from this final part of the paper.

A review of the literature suggests that the UAS platform is significantly superior in terms of cost when compared to satellites and airborne platforms. However, in one of the papers I found an interesting case on comparing the three platforms’ economic performance in the wine grape industry. The viticulture industry is one of the obvious beneficiaries of new technology because the quality of the grapes is very dependent on the time of harvest, temperature, water stress and pest infestations. In such circumstances, research conducted by Matese et al. providing comparison between UAS, airborne and satellites platforms looks especially interesting. Their cost analysis showed that the adoption of UAS is advantageous for small areas and that a break-even point exists above five hectares; after this threshold airborne and then satellite have lower imagery costs [13]. It is important to note that this study used multi-rotor UAS platform and that recent advances in fixed-wing UAS could change the outcome. Fixed-wing systems are much more efficient in covering larger areas [36] and it was specifically the cost of images acquisition rather than data processing, which significantly decreased UAS efficiency for larger areas [13]. This study is another indicator that even for high-value produce such as wine grape, the UAS platform might still be a worse option and a decision-maker should approach the UAS cost-benefit analyses cautiously.

Felton-Taylor reports the recent conversation with the Australian owner of the UAS company claiming that the UAS running costs for precision agriculture purposes are in the range of 30-50 dollars per hour. Rob Gilmore, the owner of the company, said that it was complicated to make a firm estimation about Australian farmers widely adopting the technology. His expectations were about its adoption in small-scale operations: “where it is cost effective is in the small acreage like today, the 80 hectares of horticulture, and also in applications where timeliness is very important” [57].

The new generation of fixed-wing UAS like AgEagle supported by real-time cloud-based mosaic creation by services such as Drone Deploy and with easy flights set-up providing NDVI, real colours 2D and oblique imagery can cost around US$ 0.8 – 2.5 per acre with the cost increasing with volume [36]. At the same time, standardized and high-volume airborne sensing provided by services such as TerrAvion with overnight delivery may cost US$0.25 – 5.00 per acre with the price significantly decreasing with volume. The latter also includes false-coloured NIR and single band thermal imaging in addition to NDVI, real colour and oblique imagery, and automatic images sticking as well [52].

As of now, there probably still exists a constantly changing break-even point affecting the choice preference between the three remote sensing platforms. This point is likely to depend on volume of sensing required. The latter example tends to change in favor of airborne platforms after more than 300 acres requiring sensing at a time, but the service availability may be an issue. Nevertheless, the scale of standard grain farms in North America suggests that the airborne platform might be more viable than the UAS in many cases as opposed to small Japanese farms environment. At least for preliminary scouting, yield prediction and nitrogen management for which existing multi-band sensing is already established with reasonable accuracy.

Taking into account the discussed problems with aerial sensing adoption in previous parts of the paper, it makes the UAS platform successful adoption more questionable. At least in regions where airborne service is well-established or cloud-free environment for satellites sensing is present.

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According to CB Insights data, in 2014 venture capital investments into the UAS industry (manufacturing, operating and data solutions) doubled and reached US $104 million across 29 deals [55]. My brief review of the UAS operators identified in the Canadian Civil UAS 2014 report and a consequent follow-up on their existence in 2015 revealed that about 22% of the service providers in the operating segment of the industry either changed the business completely or disappeared. It does not mean that 22% of the enterprises focusing on utilizing UAS services to different industries have gone bankrupt, but it indicates extremely high volatility of this emerging market in Canada during 2014 [14].

For all of the reasons outlined above, I therefore do not expect successful UAS adoption in regions with established standardized airborne services, such as TerrAvion, and low clouds coverage. Where these conditions are not present, I expect very careful and slow adoption that will depend on the crops prices.

However, the picture may significantly change with hyperspectral sensing adoption. It is likely that the amount of potentially redundant bands, targeted accuracy levels and specifics of the applications will require sensor’s calibration before applying. For instance, with different farms requiring very different composition of bands and different processing algorithms in the area, on-site sensing calibration before application will make sense. In this case, the UAS as a platform will flourish. In addition, the hyperspectral sensing might require lower altitudes than airborne in order to avoid sacrificing spectral resolution for spatial and radiometric. For these two reasons, the UAS platform may become a clearly preferred option in most circumstances of hyperspectral sensing. Before hyperspectral sensing wide adoption, the UAS commercialization should probably be approached cautiously, especially in the areas covered by established airborne sensing and rare cloud coverage.

Hyperspectral sensing adoption itself in precision agriculture will depend on level of technology and skilled labour cost. As of now, there are two main options to start hyperspectral sensing: use of the hyperspectral sensors systems such as Micro-Hyperspec by Headwall, Resonon or lower cost Rikola cameras or usage of specially designed prisms and filters attached to standard cameras such as those developed by Imec or StreamTechnologies. The latter solution will add up to US$ 8,000 to standard UAS multi-band sensing systems [38] but will operate only with 10 narrow bands, which will probably not be enough for the majority of intended applications in precision agriculture as most of papers mentioned in the technical part of this paper indicate. Raw ordinary hyperspectral sensor’s cost starts with about US$ 6,000 [53] but will increase into the US$ 40,000 – 65,000 range for a lightweight and all-ready UAS system implementation [59]. Based on the discussion with industry professionals, the investment may double for an opportunity to extend the hyperspectral sensing into the SWIR region of the spectrum.

Apart from the technology costs, labour expenses will probably be higher as the work with multidimensional hyperspectral cubes and processing algorithms will require more sophisticated skills and education. Because of the increased costs associated with hyperspectral sensing, its adoption is unlikely on the producer level and will probably happen at the specialized agricultural consulting firms’ level only.

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Precision Agriculture applications

In this section of the paper, I will consider the application of remote sensing technology in farm-level agriculture. I will start with the analysis of its established applications mainly associated with multispectral sensors and summarize the field research currently conducted on introduction and application of the UAS platform. I will then continue with discussing the developments in the area of hyperspectral and thermal sensing. Because preliminary research showed no possible application of microwave sensing with the UAS systems yet, I will omit this type of sensing for the purposes of the paper. I will end up discussing the UAS platform development and providing summary conclusions over the LARS most promising applications in agriculture associated with hyperspectral and thermal sensing.

Traditional remote multispectral sensing adoption in agriculture dates back to 1973 with the Landsat 1 satellite launch that recorded waves in green, red and two infrared bands. One of the first papers was published in 1973 on classifying Midwestern US fields into maize or soybeans [4]. From that time on, the body of research on remote sensing in agriculture has started to accumulate. However, it was not until early 1990s when the remote sensing in agriculture reached farm level rather than national or regional research scale [4] and became associated with what we know now as precision agriculture.

Spectral sensing in precision agriculture is not used directly to sense a specific disease or identify a particular crop but rather to sense biophysical and biochemical properties of the crop and soil and then derive disease or specie information from that data. These biophysical and biochemical properties are used as indicators for agricultural crop management. Major biophysical properties, which can be found in the studies mentioned in this paper, are: Biomass (kg m−1); Leaf Area Index (LAI) or Crop cover (No units/%); Crop height (m); Canopy volume (m3); Yield (kg m−1); Stomata conductance (mmol s−1); Leaf/stem water potential (MPa); Flowering intensity (relative units). Major biochemical properties: Nitrogen content (%N); Chlorophyll content (μg cm−2); Salinity (mg L−1); Leaf water content (%); Canopy water content (%); Leaf macro elements like phosphorus (P) and potassium (K) (mg kg−1) [30].

Traditional multispectral sensing and its data interpretations

The traditional applications for multispectral remote sensing are nitrogen management, irrigation management, yield prediction, overall crop disease stress, soil mapping [2] and weed control [3]. Because of the reflectance specifics, researchers soon found that the most valuable information about vegetation and soils in remote sensing can be interpreted using indices rather than single bands measurements. The reason is that multi-band combinations are more sensitive to changes in vegetation amount than information from single bands [3].

Table 1 below presents multi-spectral broadband vegetation indices available for use in precision agriculture. G refers to green reflectance, NIR to near infrared, SWIR to short-wave infrared and R to red reflectance. L is the SAVI indices’ family control parameter for vegetation density set up by a user ranging from -1 to 1.

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Index Description Definition using broad bands Most widely used

application

NG Normalised Green G / (NIR + R + G) Specific

NR Normalised Red R / (NIR + R + G) Specific

RVI Ratio Vegetation Index NIR / R Specific

GRVI Green Red Vegetation Index NIR / G Specific

DVI Difference Vegetation Index NIR - R Specific

GDVI Green Difference Vegetation Index

NIR - G Specific

NDVI Normalized Difference Vegetation Index

(NIR - R) / (NIR + R) Yield prediction, N management

GNDVI Green Normalized Difference Vegetation Index

(NIR - G) / (NIR + G) Yield prediction, N management

SAVI Soil Adjusted Vegetation Index (1+L) x [(NIR - R) / (NIR + R +L)] Soil mapping

GSAVI Green Soil Adjusted Vegetation Index

(1+L) x [(NIR - G) / (NIR + G + L)] Soil mapping

OSAVI Optimised Soil Adjusted Vegetation Index

(NIR - R) / (NIR + R + 0.16) Soil mapping

GOSAVI Green Optimised Soil Adjusted Vegetation Index

(NIR - G) / (NIR + G + 0.16) Soil mapping

MSAVI2 Modified Soil Adjusted Vegetation Index

0.5 x [2 x (NIR + 1) - SQRT((2 x NIR + 1)2 - 8 x (NIR - R))]

Soil mapping

WBI Water Band Index R970 / R900 Irrigation

NDWI Normalized Difference Water Index

(NIR - SWIR) / (NIR + SWIR) Irrigation

ARI Anthocyanin Content Index G / NIR Disease

RGRI Red Green Ratio Index R / G Specific

Table 1: Traditional broadband multispectral indices. Sources: [2], [3], [4], [7], [15], [21], [22], and [31]

Researchers generally prefer normalized or optimized indices over absolute measurements as these provide more comparable results between studies conducted using differently calibrated sensors [3].

Although there are 17 indices presented in the table, only NDVI has become widely used and at present is the only operational globally vegetation index [3]. It has two identified main weaknesses: extreme sensitivity to the optical properties of certain background materials such as soil and an insensitivity to changes in leaf chlorophyll content in mature canopies [3], [4]. Next to NDVI, GNDVI and SAVI are the most usable indices [15]. SAVI mitigates background materials insensitivity of NDVI by introducing manually set parameter L to represent the vegetation density of the area under the study. GNDVI mitigates insensitivity to changes in leaf chlorophyll content of NDVI [15]. It may be prudent to use a set of three indices rather than NDVI alone.

Traditionally, the remote sensing in precision agriculture was associated with two main applications. First is the indices-type information acquired from satellites for soil mapping. It allowed us to detect variations in soil related to soil properties such as texture, organic matter, clay minerals and calcium carbonate or iron oxides before seeding and adjusting nitrogen intake [2], [4].

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Another application in precision agriculture relates to the development of on-tractor sensors such as GreenSeeker or Yara-N, which enables on-the-go nitrogen variable rate management [10]. While the soil mapping application may require significant agronomic expertise and past data for a specific field and crop to establish different nitrogen zones, the on-tractor sensors rely mainly on computer algorithms. Specific algorithms were developed for different crops and regions in North America [10]. As these sensors also mainly rely on broad-band NDVI index, they can produce misleading results in case of high weed canopies or other-than nitrogen stresses, such as sulphur deficiency or crop disease [10]. Another major problem with using these algorithm-based applications is the requirement to establish nitrogen-rich reference strips for each crop planted under sufficiently different circumstances for a sensor to use it as a comparison metric for the rest of the field [4], [10]. Although other non-NDVI-based on-tractor sensors were developed, such as Crop Circle using a special index, (NIR880/VIS590)-1 [4], the underlying problems remained the same. Therefore, the application of sensors relying on algorithms still may require agronomic expertise with subsequent appropriate adjustments to the algorithm. The application of more accurate remote sensing might be able to solve such problems.

The analysis of visible range images, the ordinary Red Green Blue part of the spectrum, did not become popular in precision agriculture [37]. The two main reasons for the failed uptake were the low spatial resolution and prohibitive costs of information with the satellite imagery expenses being around $400 for a quarter section [37].

The UAS platform: introduction to traditional multi-spectral sensing

The UAS technology may become a great update of an aerial platform for precision agriculture purposes. The

aerial platform has significantly better spatial and temporal resolution when compared to satellites-based or

proximal (tractors and hand-held devices) sensing [4]. The UAS technology increases spatial and temporal

resolution even further while significantly reducing costs [11], [15]. The lower altitude and higher spatial resolution

also help to avoid or decrease the severity of traditional atmospheric errors and the need for respective adjustments.

The low altitude of UAS systems still does not prevent all measurement errors from occurring. Although cloud

cover is no longer the issue as it is with satellites, researchers still report camera lenses, filters, atmospheric

absorption and scattering, spectral variability of the surface materials of the scene, and viewing angle as sensing

problems. Flight instability is mentioned as another new source of error resulting in changes to the illumination and

geometry of the image [8]. Although new post-processing techniques and automated adjustments are available [36],

normalized indices may remain a preferred option [3].

In evaluating multispectral images and vegetation indices for precision farming applications from UAV images,

Candiago et al. [15] summarize the benefits and drawbacks of the non-proximal platforms (table 2).

Platform Spatial

resolution Field of

view Usability Payload Mass Cost for Data Acquisition

UAV 0.5 - 10 cm 50 - 500 m very good/easy limited very low

Helicopter 5 - 50 cm 0.2 - 2 km pilot mandatory can be unlimited

medium

Airborne 0.1 - 2 m 0.5 - 5 km pilot mandatory unlimited high

Satellite 1 - 25 m 10 - 50 km n/a n/a very high, particularly for high-res stereo imagery

Table 2: Platforms comparison. Source: [15]

Another advantage of the UAS platform identified by researchers is the flexibility of the payload. Salami et al. reports: “The capability of UAVs for flying much closer to the ground than satellites or full-scale manned aircraft increases the variety of sensors that can be used as payload, and they are not limited to spectral imaging”. The

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one study performed by Techy et al. even used biochemical sensing with spore-sampling devices mounted on the UAS, although this type of sensing is not considered to be remote [8].

Several recent research projects of the traditional multispectral LARS applications in precision agriculture produced promising results. For instance, in classical nitrogen-management application the UAS platform proved to be a great low cost alternative for satellites and airborne methods with as good or better quality as proximal sensing with a spectroradiometer [9].

The other example might be the 2014 spring study of the LARS application based on farmers’ requests in northeastern Ontario. It clearly showed the superiority of the UAS as a platform of choice in identifying field tile drainage networks, areas of lodging and insect infestations and in assessing fertilizer treatments. In all cases, the UAS-based solution producing NDVI and usual RGB images showed its superiority in terms of cost, availability and flexibility [5].

In general, the platform proved its high reliability in collecting data for both vegetation indices information [15] and 3D modelling of barley crop surface model to estimate plant height [16]. Although both methods are used mainly to derive biomass information for precise application of nitrogen and field inspections, they provide very different input. The platform exhibited its excellent flexibility in creating 3D barley field model providing coefficient of determination of 0.92 with actual plant height in the field [16]. Such modelling was hardly possible with other than proximal sensing. While proximal sensing experiments in other crops showed even greater accuracy with coefficient of determination being in 0.93-0.99 range, their data acquisition cost alone is about 10 times higher [16].

The other advantage associated with the low altitude opportunities and respective higher spatial resolution is that it allows us to mitigate sensitivity to the optical properties of the background materials, such as soil often found in traditional multispectral sensing [3]. In particular, Salami et al. reports Johnson and Herwitz and Herwitz et al. applying a thresholding algorithm to mask pixels associated with cloud, soil, and shadow, and obtain only directly illuminated canopy pixels. The other notice relates to Calderon et al. using automatic object-based crown detection applied to multispectral imagery to identify pure olive crowns [8].

Industry practitioners also report the benefits of using low-cost rotor-type UAS in the field overview to identify potential areas of lodging and wildlife damage helping producers with insurance claims [37].

Salami et al. (2014) provides an overview of the UAS experiments recently conducted around the world for precision agriculture and forestry purposes:

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Figure 4: Summary of the recent UAS experiments. Source: [8].

Hyperspectral single bands and indices superiority and their processing complexity

The main idea behind the introduction of hyperspectral measurements in precision agriculture is that they enable us to detect important information available only in very narrow bands, which becomes easily obscured in the broad multispectral bands and indices relying on them. An increasing body of research and respective evidence suggests that this is probably true [4].

Thenkabail et al. list the range of studies focused on arriving at optimal quantitative or qualitative information on crop or vegetation characteristics, which indicated the advantages of discrete narrow band data from specific portions of the spectrum when compared with traditional broadband data. They claim: “agricultural crops are significantly better characterized, classified, modeled, and mapped using hyperspectral data” [24].

Thenkabail et al. reported 90% accuracy in classifying five agricultural crops compared to 60% using traditional broadband indices [24]. Testing 71 broadband and narrowband indices, Agapiou et al. reported 20% increase in accuracy in studies of archeological crop marks using narrow band indices [21].

Thenkabail et al. suggested a table of 28 generic optimal narrow bands suitable for agricultural crops and vegetation studies in the 400 – 2500 nm part of the spectrum based on exhaustive literature review as of 2012. These 28 narrow bands are deemed to be best suited for (a) biophysical and biochemical properties modelling; (b) distinct separation of crops based on their species type, structure, and composition; (c) accurate classification of crop types, crop dominance, and crop species. They also suggest 5-10 nm width range as the optimal for all 28 generic bands [24].

Thenkabail et al. also assessed the indices’ accuracy. They provided the example of NDVI computed using a targeted narrowband (<5 nm range) at 682 and 910 nm having greater dynamic range than a broadband NDVI [24].

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Based on an extensive literature review, Roberts et al. listed 32 the most commonly used and best-adopted hyperspectral indices grouping them based on best-fit measurement purpose: structure, pigments, chlorophyll, anthocyanin, carotenoids, water, lignin and cellulose/residues, nitrogen, light use efficiency, stress. They also stressed that while these generic hyperspectral indices are usually more accurate than their broadband alternatives (if any), they may not result in better accuracy all the time. However, their fine-tuning for specific situation significantly improves performance [31].

Yao et al. proposed a different generic approach in automation of the index selection and generation process with hyperspectral data. Their study first listed a collection of established vegetation indices. A generic algorithm–based method was then used to select the best vegetation index and spectral band combination for a particular

application [34].

The complexity associated with using hyperspectral data drives researchers to use advanced statistical methods including partial least squares (PLS), principal components analysis (PCA), and pattern classification and recognition techniques including object oriented and decision tree classification techniques [4]. Stepwise discriminant analysis (SDA) is another approach often involved in developing hyperspectral algorithms [7]. In practice, it means that the methods used in coming up or adjusting of the existing indices and algorithms that rely on them may become extremely sophisticated and may require extensive training for in-field application if their generic accuracy proves to be insufficient. New hyperspectral indices are continuously being tested and developed using techniques involving lambda-lambda plots where reflectance signatures are compared for all possible combinations of two reflectance bands [4].

Thenkabail et al. also agreed that the amount of data produced with hyperspectral sensing poses significant challenges. They argued that using an “optimal bands” approach and reducing band redundancy is crucial even though caution should be taken in band elimination. They proposed Lambda (λ1) by Lambda (λ2) Plots, PCA, Uniform Feature Design, Wavelet Transforms and Artificial Neural Networks (ANN) as five primary methods and approaches for hyperspectral data mining and analysis [24].

Bajwa et al. in their comprehensive chapter on determining useful hyperspectral data mining methods proposed and discussed a list of nine distinct feature selection/extraction methods and nine information extraction methods. Features are definite useful bands or combination of bands. For example, NDVI as a vegetation index is a feature [26]. Feature selection means removing the least effective features (bands, indices) and selecting the most effective. Feature extraction involves transforming the pixel vector into a new set of coordinates in which the basis for feature selection is more evident, for example – arithmetic calculation of an index [34]. Plaza et al. complements the chapter of Bajwa et al. by discussing algorithms used in hyperspectral data processing [27].

In general, the growing body of evidence shows that prediction accuracy for biophysical and biochemical parameters increases from traditional multispectral vegetation indices to individual hyperspectral narrow bands and hyperspectral vegetation indices and increases further using complex statistical models built on multitude hyperspectral narrow bands and indices. Throughout this paper you may find examples justifying this statement. Alchanatis and Cohen also provide additional examples of accuracy improvements [30].

Sahoo et al. conclude that there are many analytical challenges in hyperspectral sensing application mainly associated with the amount of data and difficulty to differentiate redundant bands information from relevant bands. They argue that well-characterized and well-understood spectral libraries of the features of interest should be established [7]. This gap may significantly postpone the commercialization of hyperspectral sensing research achievements and impede further research accumulation.

Another gap Sahoo et al. highlighted concerns the acquisition and understanding of the basic spectral signatures of plants in the TIR part of the spectrum. They wrote “new technological developments in the TIR sensor

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designs make it worthwhile to investigate plant TIR emissivity characteristics and explore the potential use of TIR remote sensing information in vegetation studies” [7]. For the purposes of this paper, this statement clearly shows how far from commercialization the microwave sensing and the narrow-band thermal sensing are in precision agriculture.

Mulla argues that “more emphasis is needed on chemometric or spectral decomposition/derivative methods of analysis since spatial and spectral resolution of hyperspectral sensing systems are now adequate for most precision agriculture applications” [4]. This indicates that the problem with commercialization of hyperspectral technology might lie not in the lack of hardware but with the absence of analytical tools that are both easy to use and readily available.

Hyperspectral sensing: vegetation discrimination opportunities and weed control

Research suggests that hyperspectral sensing may discriminate vegetation, including crops, with significant accuracy [7], [24], [32], [34]. This unique opportunity was not possible with traditional multi-spectral sensing. If we can use sensors to discriminate accurately between different types of vegetation, we may be able to automate further sensing applications and routine precision agriculture operations. Based on the literature and discussions earlier in this paper, it is clear that while hyperspectral sensing is a superior technological opportunity, it requires site-, time-, specie- and specie growth stage specific calibration. Elimination of one or two of these variables may significantly improve and ease the application and help with its adoption by the industry.

The other significant opportunity is the possibility of accurate weed sensing, which may help in reducing herbicide usage such as glyphosate (RoundUpTM) by applying variable rates and introducing process automation. Because an increase in glyphosate resistance by weeds due to its excessive and frequent usage is considered to be one of the key long-term agricultural challenges, its better management may be associated with food security as well [41]. Finally, it may significantly help in pastures management, as will be discussed later in this paper.

Yao et al. [34] mention the study conducted by Vrindts et al. on classifying sugar beet, maize and seven weed species. They reached 97% accuracy under laboratory conditions and 90% accuracy under in-field conditions but had to adjust the model to prevailing light conditions. Another study they considered was carried out by Nieuwenhuizen et al. in differentiating volunteer potato plants from sugar beet. They found best accuracies for two species classification using ten narrow bands processed by ANN algorithm [34].

Galvão et al. argued that any selected set of narrow bands for crop type discrimination is not universally applicable and should be changed based on location. They used hyperspectral sensing on a satellite platform to compare discrimination between coffee, sugarcane, flooded rice, common bean, corn and soybean. Hyperspectral discrimination provided results with about 77% in overall classification accuracy in low NIR and low SWIR parts of the spectrum [32].

Thenkabail et al. provided a great picture exhibiting how the discrimination between barley and wheat drastically improves with using only two distinct most-suited narrow bands compared to the most suited set of two broad bands [24].

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Figure 5: Differentiation between Wheat and Barley. Most suited broadband results are on the left, the most suited narrowband results are on the right. Source [24]

They also reported that discrimination using more than two narrow bands is more accurate but requires a statistical approach in selecting appropriate bands. Otherwise, picking up of additional bands from redundant category becomes highly probable that will increase processing complexity but will not increase discrimination accuracy. They show how the utilization of an SDA technique allows to come up with 17 hyperspectral narrow bands to accurately separate vegetation types such as grass, shrubs and weeds. In another example showing discrimination of two weeds, the combination of 11 narrow bands was found to be optimal. They also argue that while in both cases further addition of bands increases accuracy but those increases become very small and statistically insignificant [24].

Sahoo et al. mention a number of research projects conducted during the last decade that showed accurate results in discriminating vegetation types or species and even their genotype-determined clusters using hyperspectral remote sensing data. This discrimination is impossible with traditional multispectral sensing. With the help of SDA and PCA methods researchers were able to discriminate among pulses, cole crops and ornamental plants [7]. Furthermore, Sahoo et al. conducted an experiment discriminating 70 wheat genotypes using hyperspectral sensing alone. Although all 70 genotypes were statistically different at all ranges of hyperspectral bands at the 1% level in ANOVA testing, not all of them were found separable using the selection feature designed by Sahoo et al. They continued the experiment trying to establish clearly separable clusters for wheat. At the end, Sahoo et al. developed six main wheat genotype-determined clusters that are clearly discriminable using already tested hyperspectral narrow band sensing [7].

Another wheat discrimination study mentioned in the paper refers to that of Antony et al. who were successful as well in clearly discriminating wheat growth stages [7]. The same paper also mentions the research conducted by Kumar et al. that successfully discriminated tea plantations in terms of tea type, plantation age and growth stage using SDA and PCA techniques utilizing narrow bands exclusively from VIS and NIR regions of electromagnetic spectrum [7].

The other research project mentioned by Sahoo et al. refers to the discrimination of wheat phenostages along with sugarcane, mustard, sorghum and potato using PCA and band-to-band correlation analysis. It utilized SWIR part of the spectrum in addition to VIS and NIR. They showed that successful discrimination is possible with both specific narrow bands analysis and the whole 450 – 2350 nm range [7] when appropriate analysis techniques are put in place.

Thenkabail et al. reported 90% accuracy in classifying five agricultural crops compared to 60% using traditional broadband indices [24].

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The other interesting study conducted by Sahoo et al. examined the usage of bidirectional reflectance distribution functions (BRDF) for crop discrimination purposes. The main idea behind this discrimination is based on plant’s geometry rather than one-way reflectance, although bidirectional reflectance is involved in identifying the geometry. In short, it was easy to discriminate wheat being electrophile in nature (vertically placed leafs) versus canola being planophile in nature (horizontal leafs). The better spectral and spatial resolutions are, the more potential BRDF has for vegetation discrimination. Hyperspectral sensing provides means for better spectral resolution while proximal and low altitude platforms can bring the necessary spatial resolution. Although the proximal sensing was deployed in this study, it can be applied for aerial platforms as well and the low altitude remote sensing. In fact, it may be especially beneficial as a comparable study carried by Antony et al. revealed that off-nadir view angles (other than 90⁰ angle right beneath a sensor) perform better for BRDF discrimination [7] and it is easier to do off-nadir angle sensing at lower altitude.

BRDF provides a complicated but very promising alternative to empirical approaches mentioned elsewhere throughout this paper. Empirical approaches are associated with spectral retrieval of biophysical and biochemical parameters and then applying them in site-, time- and crop-specific manner. BRDF allows us to develop canopy radiative transfer models (RTMs), which describe the transfer and interaction of radiation inside the canopy based on canopy architecture. In turn, it allows us to establish an explicit connection between the biophysical variables and canopy reflectance. This alternative requires significant computational resources [7].

Hyperspectral sensing: advanced crop disease analyses

If improved remote sensing associated with the hyperspectral opportunities helps us to ease crop scouting, increase its frequency and accuracy, we may become much more efficient in our pesticides usage resulting in direct costs savings and decreases in environmental pollution associated with several insecticides and fungicides [41].

To date hyperspectral sensing has been capable of identifying many stresses based on assumptions that stress interferes with photosynthesis and physical structure of the plants. Moreover, advanced sensing provides much more precise means to objectively quantify these stresses than visual or traditional multi-spectral sensing methods [7]. Historically, the proximal sensing proved to be the better option in studying crop stresses [7] and it is therefore likely that LARS may become a great new alternative for the stresses monitoring.

Sahoo et al. mentions these studies conducted for hyperspectral sensing of crop stresses: brown soft scale insect in citrus, strawberry spider mite in cotton, net blotch in barley, glume blotch in winter wheat, greenbug stress in wheat, leafhopper in cotton, aphid-infestations in mustard, brown plant hopper (BPH) in rice, late blight disease in potato, yellow mosaic virus (YMV) in soybeans, yellow rust disease in wheat [7], verticillium wilt in olive [8], and late blight disease in tomatoes [34].

Mahlein et al. looked at different sugar beet diseases: Cercospora leaf spot, powdery mildew and leaf rust at different development stages. They emphasize that spatial resolution was crucial with 0.2 mm being the best, in particular for the detection of leaf diseases with discrete, roundish symptoms. A spatial resolution of 3.1 mm was identified as a main threshold level, after which characteristic symptoms were not detectable anymore. They mentioned the rule of thumb established among researchers ruling that depending on the shape of the symptoms, pixel size should be smaller than the object of interest by a factor of 2 to 5. This finding may restrict the sensing of plant diseases to proximal sensing technologies only. They identified disease-specific spectral signatures and stated that hyperspectral sensing clarified various stages of sugar beet diseases as a continuum rather than discrete classes [23].

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Yao et al. mention studies performed for different diseases with reported accuracies as high as R2 of 0.93 for fungal disease in spring wheat and 0.91 for yellow rust in wheat. The latter example is especially interesting, as the data were collected using airborne platform rather than proximal sensing [34].

Hyperspectral and thermal sensing: pastures management

Pastures analysis is considered separately in this paper because it is of greater importance in Alberta and it involves many specific features not found in agricultural crops analysis. For instance, disease stress is less important while plant type discrimination among grass, shrubs and other vegetation becomes critical. Nutrients composition is also important and according to Numata determining of pasture nutritional quality is a priority for pasture characterization via remote sensing [28]. Deciding to put a separate topic on pastures management I also intended to search for any remote sensing applications, which could ease the phenotype data collection in order to improve genotype-phenotype prediction accuracies and increase breeding efficiency. Furthermore, pastures management involves livestock and thermal sensing could become more important for this application.

Indeed, thermal sensing may help to monitor herd’s health, breeding activity, and its numbers and track down escaped cattle. Moreover, there are researchers who think the UAS system with thermal sensor may become a viable solution [39]. However, there is a number of other applications, which may be much more practical and cost effective as of now.

Regarding pastures analysis, Numata claims that one of the key challenges with remote sensing for pastures management is the heterogeneity of plants that makes it very difficult to predict available biomass. The landscape heterogeneity usually associated with pastures makes it even more challenging [28]. The other challenge is the difficulty in discriminating dry plants from bare soil in traditional multispectral broad bands sensing. As Thenkabail et al. reports, it is very difficult to differentiate nonphotosynthetic vegetation (NPV) such as litter, wood, senesced leaves, and other dry vegetation from bare soil in traditional VIS and NIR parts of the spectrum. Hyperspectral sensing in SWIR region is much more accurate for this purpose [24].

Numata states that the use of traditional NDVI and SAVI indices can be very misleading in pastures management and hyperspectral sensing in SWIR 2000 – 2400 nm part of the spectrum may be required. He claims that NPV plays a critical role, especially in dry regions or dry seasons [28].

However, if modified NDVI still may produce good results. Numata mentions the study conducted by Mutanga and Skidmore in predicting dense biomass of tall grass showed the coefficient of determination being R2 = 0.25 only. After narrowband modification, they were able to reach R2 = 0.78 for hyperspectral NDVI. He also mentions four other studies in estimating pastures biomass comparing traditional NDVI being with R2 in the range of 0.31-0.4 to narrowband alternatives showing 0.56 – 0.86 results [28].

Numata continues with describing processing problems. For an ecosystem such as grazing land, at least four components are considered in remote sensing: green vegetation, NPV, soil and shade. He states that for this reason complex technique such as SMA is required to produce meaningful analyses for pastures management [28].

These problems make it very difficult to design a simple remote sensing solution for pastures management. Handcock et al. support this idea in their extensive study of using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing in Australian pastures management. They claim that remote sensing may become a valuable source of information in pastures management for detecting cattle behavior, prevent overgrazing and collecting phenotype data. However, they also state “short-wave infrared data to observe soils or dry vegetation,

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NIR data for green vegetation, or thermal infrared data for water and land temperatures” is critical for observing landscape conditions and matching them with animal behavior [40].

In regards to phenotype data collection, a new solution that could potentially ease the collection of livestock methane pollution data has been developed by Telops, a Quebec-based company. It recently launched a hyperspectral camera designed specifically for methane detection [42]. It has potential to bring down costs associated with detecting volume of methane produced by cattle. In turn, it can enhance breeding accuracies and enhance farm policy regulations in respect of this greenhouse gas. However, no research papers were found on this technology application testing.

Hyperspectral and thermal sensing: water content analyses

Hyperspectral and thermal sensing bring significant accuracy in estimating plants’ and soils’ water stress. It may help in irrigation management resulting in savings in direct costs, water usage per yield measurement, increase in the variety of crops grown in water-stressed areas and decrease in farming risks. Because the water supply is considered to be one of the key global long-term agricultural constraints, better irrigation management may also be associated with improved food security [41].

For example, the hyperspectral sensing proved to be more useful than well-established Leaf Area Index (LAI) even when used solely in traditional 400 – 1000 nm part of the spectrum. It means that less complex, UAS-mounted systems such as microVNIR1920 are likely more accurate in irrigation management than traditional multispectral methods. Sahoo et al. cite the study carried by Ray et al. comparing moisture-sensitive narrowband LAI in discriminating different irrigation systems. They came to conclusion that hyperspectral sensing indices were more efficient than LAI in detecting the differences among crops under different irrigation treatments [7].

Furthermore, spectrum ranges closer to the SWIR part show great accuracy results. For instance, Pargal et al. used five indices in MIR and SWIR regions of the spectrum and band depth analysis in rice studies developed a model capable to estimate the relative rice water content with a coefficient of determination of 0.9 [7].

Sahoo et al. also mention the research conducted by Bandyopadhyay et al. covering the optimum growth stage of wheat crop and suitable water stress indices using hyperspectral sensing. Validation of their empirical models based on water stress indices could account up to 87.5% and 89.2% variation in the observed grain and biomass yield of wheat respectively [7].

Several vegetation indices were developed specifically for thermal sensing. Most widely used are Crop Water Stress Index (CWSI) and Ig, I3 (Stomatal Conductance indices). CWSI is based on the difference between actual canopy temperature and non-water stressed standard. Salami et al. mentions the research endeavors proving high correlation of all three indices with coefficients of determination R2 ranging from 0.54 up to 0.70 [8].

Hassan-Esfahani et al. conducted an interesting study in Utah on soil moisture sensing in irrigated farms management. Using thermal and traditional multi-spectral sensing they developed an artificial neural network (ANN) model to quantify their effectiveness. Thermal sensing alone provided the coefficient of determination R2 of 0.41. After adding multi-spectral bands, field capacity and several vegetation indices into the model, they reached R2 of 0.77. Nevertheless, the thermal sensing proved itself as being the most relevant information in surface soil moisture estimations compared to traditional multi-spectral sensing features [17].

As reported by Salami et al., today hyperspectral crop water stress sensing provides “similar or even better results” compared to thermal sensing. However, there is a significant gap in thermal sensing analysis [8] and therefore, it is possible to expect further sharp increase in its accuracy. Thenkabail et al. argue that thermal sensing

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complements sensing in other parts of the spectrum for this purpose claiming that “moisture and plant stress properties are best quantified by including TIR bands along with SWIR, VIS and NIR” [24].

Roberto et al. report that thermal and microwave water content experiments are rare compared to research done in VIS, NIR and SWIR regions of the spectrum. They argue that hyperspectral sensing in NIR region is better for prediction of water content at canopy level while SWIR region is better suited to estimate water content at leaf level. They also propose 16 narrowband indices designed specifically for vegetation water content analyses [29].

Hyperspectral sensing: advanced macro-nutrients content analyses

There may be great benefits found in hyperspectral sensing applications if they would allow us to better predict macro-nutrients content in plants. It may allow us to enhance variable rate fertilizer management resulting in decreased direct costs and indirect environmental contamination costs associated with excessive fertilizer application. Furthermore, the preliminary review shows opportunities to estimate not only nitrogen but also to extend remote sensing for other nutrients, including phosphorous, and thus enhance their management. As phosphorous supply limits and its geographical concentration are considered to be one of the key long-term global agricultural constraints, it may help with food security as well [41].

The other practical in-field enhancement relates to Mulla D.J. expectations that the further developments in hyperspectral sensing should allow us to abandon the usage of reference strips in nitrogen management [4] associated with GreenSeeker and Yara-N sensors mentioned earlier in this paper [10].

Mahajan et al. argue that research has demonstrated the ability to monitor nitrogen (N) in many crops, phosphorus (P) and potassium (K) in very few crops and none so far to monitor sulphur (S) [6]. Their recent research project showed promising results in terms of using hyperspectral sensing in detecting phosphorous and sulphur macronutrients in wheat. They experimented with narrow bands in usual VIS and NIR regions as well as in SWIR region and proposed new spectral algorithms specific to P and S. A proposed index (P_1080_1460) predicted P content with high and significant accuracy (correlation coefficient (r) 0.42 and root means square error (RMSE) 0.180 g m-2). Performance of the proposed sulphur index (S_660_1080) for S concentration and content retrieval was similar [6]. Although these newly developed indices constitute great results, their accuracy might be questioned for commercialization purposes.

Mahajan et al. also mention other studies conducted for corn and wheat, which produced robust results in detecting P and K using new narrowband hyperspectral indices, namely N_1645_1715 and N_870_1450 for wheat. They summarize with “proposed VIs reached significant levels of accuracy in retrieving P and K levels in comparison to traditional VIs such as NDVI, Green NDVI (GNDVI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI) and Optimized SAVI (OSAVI)” [6]. The other notice made in their paper relates to the research conducted by Ferwerda and Skidmore in 2007 demonstrating the potential of hyperspectral remote sensing in predicting concentration of essential nutrients such as N, P, Ca, K, Na and Mg in four wood plants: willow, mopane, olive and heather [6].

Numata mentions two studies performed for pasture grasses species utilizing the continuum-removal derivative reflectance (CRDR). In a study by Mutanga et al., N, P, K, Ca, and Mg concentrations were predicted with coefficients of determination of 0.7, 0.8, 0.64, 0.5, and 0.68, with low errors, respectively. Higher values were obtained with R2>0.80 when data were grouped by species types. In the second study performed by Kawamura et al. N, P, K, and S were predicted with R2 being 0.895, 0.943, 0.809, and 0.943, respectively [28].

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These research projects reveal the potential of further research in hyperspectral remote sensing in regards to other than nitrogen macronutrients management. However, the major body of research continues focusing on the N management.

Mulla states in his paper that around the world and across different crops, the research shows the superiority of nitrogen management using hyperspectral bands rather than multispectral. He mentions three research endeavors on potatoes in India, flax, chestnuts, corn, bamboo, potato, pine, saccharose, maize and tea in China [4]. Sahoo et al. further mention the study carried out by Ranjan et al. on wheat in India [7].

Many researchers have already established precise optimal narrow bands for N-detection in specific crops. For example, Jain et al. reported 560, 650, 730 and 760 nm as being suitable for discriminating different N rate-treated potato crops [7].

Kaivosoja et al. in their wheat research study comparing hyperspectral data with broadband NDVI showed the superiority of hyperspectral classification leading to improved decisions resulted in 520 kg/ha better yield [11].

Yao et al. mention the study conducted by Christensen et al. resulted in 81% accuracy in nitrogen prediction in barley. Other research endeavors noticed report prediction accuracies from 62.4% in cotton to 93.8% in rice with the latter based on a 3-year regression model [34].

Hyperspectral and thermal sensing: advanced soil analyses

Hyperspectral sensing potentially allows us to extract some valuable qualitative and quantitative information on soil properties without labor-intensive wet-chemistry analyses. Even with such low surface penetration as 0.05 mm, remote sensing in VIS, NIR and SWIR regions of the spectrum can provide information on soils primary and secondary minerals, organic matter, iron oxides, water, salt. Although it is definitely will not provide a sufficient soil analyses for many purposes where information on a whole soil profile is required [33].

Ben Dor argues that hyperspectral sensing can bring information about soil properties that are strongly correlated to soil minerals, organic matter, iron oxides, water, and salt. He continues stating that the soil particles size differences along with changes in the aggregate soil size due to field tillage, soil erosion, accumulation of dust and soil crust and other processes significantly affect spectral reflectance. Ben Dor mentions the study conducted by Hunt and Salisbury, which quantified changes in soil particles size affecting around 5% in absolute reflectance. Changes in aggregate soil size are expected to have a larger effect and play a major role in reflectance changes. Ideally, a particular field should be measured at “standard” and then reevaluated using the identically calibrated sensor and angle. In practice, such measurements are hard and expensive to make. Ben Dor summarizes that “it is postulated that more effort should be expended in the more precise accounting of physical effects under field conditions” and suggests that using of BRDF may be helpful in resolving this problem [33].

The other problem with soil analyses using hyperspectral data is that different combinations of chemical and physical parameters change spectral reflectance of soils in a very complex, non-linear way with many significant anomalies. For example, changes in moisture levels significantly impact reflectance in many regions where other than water soil parameters are analyzed. Ben Dor suggests that soil spectra aggregate analysis should be judged and examined with caution. Nevertheless, the hyperspectral remote sensing of soils remains a promising research field [33].

Yao et al. mention a different study conducted by Ben Dor and Banin on predicting six soil properties. Their results showed that the best accuracy for properties required a different number of narrow bands ranging from 25 to 3113. They also mention the research performed by Ge and Thomasson, which found that Ca, Mg, P, and Zn may be predicted with coefficient of determination R2 values >0.5. The other research mentioned in the paper is

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that of DeTar et al. that found the best regression R2 (0.806) for percentage of sand. Other properties in their study, silt, clay, chlorides, electric conductivity, and P had lower R2 in the range of 0.66–0.76 [34].

In a different paper, Yao et al. used two geostatistical approaches in creating in-field pH map with coefficient of determination R2 being 0.58. Such maps may be useful for lime application management performed to reduce soils salinity [34].

Ben Dor summarizes his comprehensive chapter on hyperspectral soils sensing with conclusion that while promising, the amount of relevant research on soils is limited compared to that of vegetation and water and it remains at its infancy stage. Among key reasons, he names costs, complexity of soil matter and a need for very skilled personnel. He also thinks that involvement of active microwave sensing such as ground-penetrating radars and multi-bands thermal sensing may become very beneficial. He finishes with: “We strongly feel that if all spectral domains (point and image) are thoroughly researched and other active remote sensing methods will be merged and combined, the applications are nearly unlimited” [33].

The UAS platform for hyperspectral and thermal sensing and integration with active sensing technology (LIDAR)

Pölönen et al. and many other researchers showed that the concept of small hyperspectral imager, UAS platform and data analysis is ready for operational use [12]. Hassan-Esfahani et al. and Berni et al. showed that the same holds true for thermal sensing [17], [35]. I have not found any papers on the UAS platform combined with LIDAR application for precision agriculture purposes. Salami et al. in their meta-study reported only three papers were published where the LIDAR sensing applied from the UAS was present and all these papers related to forestry [8].

Ortenberg stresses that emerging hyperspectral sensing applications are focused on the airborne or satellite platforms with multiple sensors and LIDAR integration. In his opinion, the fusion of LIDAR data and hyperspectral optical sensing classification results is a valuable tool and should be explored extensively. He reported successful combination of LIDAR and hyperspectral optical instruments were put together as an integrated unique sensor operated from helicopter platform [25].

Roberto et al. argue that simultaneous LIDAR measurements can be used to measure and minimize the influence of canopy structure and architecture on the acquired spectra increasing the basic hyperspectral sensing accuracy [29]. Regarding thermal sensing and LIDAR technology on UAS platform, Sahoo et al. finish their paper concluding that LIDAR and thermal data need to be considered in future [7].

The research mentioned further shows that UAS platform has chances to become the best platform for thermal sensing, as the altitude becomes very important for measurements accuracy and it is much more cost effective than proximal sensing. One of the information problems with thermal sensing is that it requires additional adjustments. Salami et al. mention the study done by Thomson et al. that revealed significant difficulties in using thermal remote sensing in humid subtropical climate. They stated that the altitude of sensing was accountable for 58% of the variability in canopy temperature [8].

Berni et al. showed that even given the low altitude of UAS platforms, the errors higher than 4⁰ K occur if atmospheric transmittance effect and atmospheric thermal path radiation are not considered. It requires additional radiative transfer models such as MODTRAN for data adjustments [35].

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Remote sensing technology overview

In this section, I will first provide a brief overview of the four remote sensors characteristics: spectral, spatial, temporal and radiometric resolutions [1], [2], [3]. I will then continue with differentiating between active and passive sensors and specifying the carrying platforms available.

Spectral resolution

Sensors are capable of grouping wavelengths into definite ranges. Such ranges are called bands, a sensor records and presents not separate single waves but these bands of waves [1]. For example, a sensor with low spectral resolution might be able to distinguish only several broad bands usually focusing in the Red, Green, Blue and Near Infrared regions of the spectrum [4]. These multi-spectral sensors record waves grouped into several spectral bands. The width of such bands is usually more than 40 nm and they operate within the 400-1000 nm part of the electromagnetic spectrum [2]. The finer the spectral resolution, the narrower the wavelength ranges for a particular channel or band [1].

For a visualization on bands and their operating range in multi-spectral sensors, please refer to the Figure 3 below. We may then see, that with traditional multi-spectral sensor having 40 nm band window we can use it to set up maximum 4 “red” sub-bands approximately from 620 nm to 760 nm range. Above that, we enter the near infrared area where we will be able to set up about 5 sub-bands for Near Infrared (1000 nm – 760 nm and then divided by 40 nm band window) for a regular multi-spectral sensor. While this is a possibility in theory, in practice most of the traditional sensors use just 4 – 7 bands with wider than 40 nm bands’ limits [2], [4].

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Figure 3: Detailed Visible region of electromagnetic spectrum with nm scale. Source: [18].

Hyperspectral sensors are advanced versions of traditional multi-spectral sensors capable of detecting hundreds of very narrow spectral bands throughout the electromagnetic spectrum [1]. They usually use bands of 10 nm and operate across a much wider range of the electromagnetic spectrum. The first hyperspectral sensor

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utilized the range of 380 – 2500 nm [4]. Some of the newer larger-scale hyperspectral sensors installed by the government on the satellites platform enjoy similar range of 400 – 2500 nm capable to record 220 separate bands simultaneously [19]. Therefore, with hyperspectral bands we can set up now 16 definite bands instead of 4 in the “red” spectrum alone and make more precise and varied measurements. Taking into account wider electromagnetic range, we may also research interdependencies between plants’ and soil conditions and electromagnetic waves in medium infrared range as well.

This is how such sensors enable researchers to investigate and identify patterns and interconnections, which can be captured only with the thinner bands or within wider spectrum limits. One of the breakthroughs in remote sensing technology has been bringing opportunities for establishing accurate predictions between electromagnetic radiation reflection/emittance and moisture status, organic matter, nutrients, chlorophyll, carotenoids, cellulose, leaf area index and crop biomass [4].

Spatial resolution

This type of resolution refers to the size of the smallest possible feature that a sensor can detect [1]. Essentially, spatial resolution refers to a sensor pixel size at full resolution [2]. If the feature is smaller than this, it may not be detectable as the average characteristic (such as brightness in case of color) of all features in that resolution cell will be recorded [1]. Spatial resolution is affected not only by sensor but also hugely by the distance from a sensor to an object. In case of regular passive sensors, it is usually associated with the Instantaneous Field of View (IFOV), which is the angular cone of visibility of the sensor that determines the area on the Earth's surface that is "seen" from a given altitude at one particular moment in time [1].

Temporal resolution

This characteristic refers to the length in time when the next sensing of the exact same area may take place. It is very important for satellite platforms as it depends on how often it appears at the same point of its orbit. Obviously, this resolution is almost useless for the rest of the platforms as UAS, airplanes and helicopters can return to the same location within a single day. The resolution number usually represents the amount of days it takes a particular satellite to repeat its cycle. The actual temporal resolution depends on different factors, including the satellite/sensor technical capabilities, the swath overlap, and latitude [1].

Radiometric resolution

Radiometric resolution refers to the sensor’s sensitivity to distinguish differences in electromagnetic energy intensity [2]. This resolution describes its ability to discriminate very slight differences in energy. Better resolution allows detecting smaller differences in reflected or emitted electromagnetic radiation [2]. It represents the intensity of spectral bands, the number of individual waves in them.

There is always a balance between radiometric and spectral or spatial resolutions. Higher spatial resolution means fewer waves are detected and thus radiometric resolution decreases. Having many narrow bands with higher spectral resolution also decreases radiometric resolution as the number of waves within narrower band decreases [1]. It might become a significant problem for passive sensors, which do not emit electromagnetic energy illuminating the objects.

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Active / Passive sensors

Electromagnetic radiation must have a source. The most obvious and the most often used source is the Sun. Sensors using it are called passive because they do not emit their own electromagnetic radiation but capture and analyze objects’ reflection of the sun energy. On the contrary, the sensors that utilize their own source of energy are characterized as “active” [2].

Active sensors illuminating objects with their own energy are superior in terms of daytime or weather working flexibility, lesser adjustments required for external environment differences [2] and better waves intensity. The latter is especially important for microwave capturing sensors because these waves are not sufficiently provided by the sun [1]. They also usually require much more energy to operate. Some examples of active sensors are a LiDAR (including laser fluorosensor) and a synthetic aperture radar (SAR) [1].

Platforms

Among platforms usually used for remote sensors mounting, researchers distinguish separately satellites, aerial, ground-based platforms [4], and, most recently, the UAS [14].

The platform affects mainly the spatial resolution, but it can also be critical in terms of flexibility for sensors’ adjustments and application cost. This paper will mainly focus on the UAS platform comparing its application to other more mature platforms as the preliminary research showed significant potential specifically for LARS [13], [14], [15].

It is also worth mentioning in this part of the paper that now in practice it is better to differentiate between sensors working within the:

1) visible to medium infrared part of spectrum. I will refer to these as regular sensors. 2) far infrared part of the spectrum. I will refer to these as thermal sensors. 3) microwave part of the spectrum. I will refer to these as microwave sensors.

I want to distinguish these three groups because their wavelengths’ diapasons are so different (refer to the Figure 3 presented earlier in this section) that they require different capturing and recording mechanisms from sensors. Regular sensors mostly deal with reflectance of waves and some correction for atmosphere and angle adjustments.

Thermal sensors usually deal with the matter emitting the waves itself and adjusting it to the surrounding environment. These sensors usually operate within just one very wide band such as 3,700 – 4,800 nm or even wider like 7,000 – 14,000 nm [2] representing heat emittance from soil or plants. Although new systems are being developed as well applying hyperspectral sensing in thermal diapason with as many as 83 narrow bands.

Microwave sensors usually require very different combination of spectral and spatial resolution and an active source of waves because there are not enough such long waves coming from the sun for intensive measurements. Waves in this part of the electromagnetic spectrum also are much less susceptible to atmospheric and other environmental disturbances (such as surrounding temperature fluctuations) and thus have very different approach to platform assessment.

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Remote sensing underlying physics

Our human remote sensing related to surroundings usually involves light, smell and sound. Although being “remote” and having many physics concepts in common, such as waves and frequencies, these sensing types are quite different in nature. In technology and in this paper in particular, remote sensing usually means what we humans consider as light and its invisible extensions, generally referred to as electromagnetic radiation.

The concept of electromagnetic radiation describes the wave-type movement of two interconnected fields: electric and magnetic, which move together at the speed of light. These waves are characterized by wavelength and frequency. The longer the wave, the lesser frequency the movement has (wave cycles per second). We usually measure waves in adjusted meters while their movement frequency - in hertz (Hz).

These waves interact with a matter such as air, water or leaves using three interaction forms: absorption, transmission, and reflectance. Typically, remote sensing focuses on reflectance only [4]. The matter itself emits electromagnetic radiation as well and it can become an input for sensors. For example, plant leaves can emit energy by fluorescence or thermal emission [4].

Waves with different lengths interact differently with different substances. In short, this is how we can use them to derive conclusions about the state of the matter, such as plants or soil, remotely observing the interaction of these electromagnetic waves with the matter. In some circumstances, we may also study remotely the emittance of the electromagnetic radiation by the matter itself.

Depending on the wavelength or frequency, we can classify the electromagnetic radiation into different groups and subgroups. We call such classification scale an electromagnetic spectrum. For the purpose of its technology applications, the scale is subdivided into seven major groups: gamma rays, x-rays, ultraviolet range, visible range, infrared range, microwaves, radiowaves. On this scale, among the most interesting groups in terms of remote sensing technology are: the visible range (this is what we can see with a human eye), ultraviolet range, infrared range and microwave range [1].

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Figure 1: Detailed Visible and Ultraviolet regions of electromagnetic spectrum. Source: [1].

Figure 2: Detailed Infared and Microwave regions of electromagnetic spectrum. Source: [1].

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Using our own eyes as remote sensors and indentifying a plant’s leaves as “green” we actually see that these particular leaves reflect more waves in the “green” length than in red and blue wavelengths. From that we may conclude that their matter has a lot of the chemical compound chlorophyll and from that we may plausibly conclude that it is more “healthy” than a nearby plant with “yellow” leaves. While our eyes can only detect waves in the visible wavelength, we may use more advanced remote sensors to capture and analyze waves from other lengths. We can further analyze connections between plants or soils conditions and their changing form (absorption/transmission/reflectance) and intensity of intercations with different waves. At the end, we may be able to establish the system of sufficiently accurate predictions about plants and soils conditions based on their interactions with different waves of electromagnetic radiation. This is the key idea behind remote sensing in agriculture.

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