crop productivity assessment through remote sensing: radiation-driven and water-driven algorithms,...

CROP PRODUCTIVITY ASSESSMENT THROUGH

REMOTE SENSING

Christopher M.U. Neale

Isidro Campos

GENERAL FRAMEWORK

• Satellite imagery has been extensively used for crop monitoring and yield

forecasting for over 30 years and plays an important role in a growing number of

operational systems

• The estimation of spatially-distributed biophysical parameters is a good first step

towards the use of remote sensing for crop yield estimation, but more complete,

and complex, approaches have to be developed

• The methodologies developed to date can be described (Rembold et al., 2013)

as:

Regression modelling

Mechanistic and dynamic crop growth models

Assimilation models

Biomass production and partitioning models based on earth

observation (EO) data

GENERAL FRAMEWORK: REGRESSION MODELS

• Data-driven approaches developed with the

base of strong empirical relationships between

biophysical parameters derived from EO and

ground-yield data.

• Necessity of new calibration to be applied at

different location or meteorological conditions

and relative low accuracy for individual plots.

• The use of these approaches for yield

forecasting and global application is limited.



Assimilation models

Biomass production and partitioning models based on EO data

GENERAL FRAMEWORK: ASSIMILATION MODELS

• For these approaches, the crop growth model

inputs and initial conditions that have a large

effect on biophysical parameters are selected.

• Then, those selected inputs and initial conditions

may be ‘‘tuned’’ in order to better fit the estimated

biophysical parameters based on remote sensing

data.



Assimilation models


GENERAL FRAMEWORK: BIOMASS PRODUCTION Regression modelling


Assimilation models


• The driving forces for biomass production are

absorbed photosynthetic active radiation (APAR)

and plant transpiration. Both can be derived

from remote sensing based models

• APAR and Transpiration are related to biomass

production using light use efficiency (LUE) and

water use efficiency (WUE) parameters, with

yield estimated through a yield partitioning factor

• These approaches require the adequate

estimation of both WUE/LUE and the yield

partitioning factors, so previous knowledge about

both factors is needed

(Muñoz-Padilla et al. 2012)

LUE=2.5 g MJ−1 PAR

YPF=0.8

fPAR/APAR

estimates

Crop specific fPAR-VI

relationships and

radiative transfer

models

Yield/Grain

production

ET & water stress

estimates

Hybrid model

based on

ALEXI-DISALEXI

Site/Plot specific

Yield partitioning

Existing models on

Yield partitioning

Biomass

High to Medium resolution RS data: Landsat Constellation

MODIS Constellation, VIIRS, others

Agro-meteorological data Existing ground-networks

and satellite based estimates

OUR PROPOSAL: GENERAL OVERVIEW

Water productivity

Site/Plot specific

LUE

Existing

models on

LUE

Site/Plot specific

WUE

Existing

models on

WUE

Crop coefficient and reference ET: • Reflectance-based crop coefficient models where

vegetation indices are related to ET crop coefficients. Relationships are typically crop specific. Uses shortwave (Visible, NIR) bands of satellite instruments.

Energy balance models: • One layer models examples: empirical models (OLEM),

SEBS, SEBAL, METRIC, SSEBop

• Two-source models, ALEXI-DisALEXI

• Detailed Process models

Energy balance models require the use of both the thermal infrared and the visible/near-infrared bands

Hybrid Methodologies

fPAR/APAR

estimates


relationships and radiative

transfer models

Yield/Grain

production

ET & water stress

estimates

Hybrid model based on

ALEXI-DISALEXI

Site/Plot specific

Yield partitioning

Existing models on

Yield partitioning

Biomass

Site/Plot specific

WUE/LUE

Existing models on

WUE & LUE

High to Medium resolution EO data: Landsat Constellation

MODIS Constellation




BIOMASS PRODUCTION

• Estimation of Biomass production based on the estimates of crop transpiration

and PAR absorption based on remote sensing models

Biomass=WUE•ΣTr … or … Biomass=LUE•ΣAPAR

o Crop canopy transpiration with the summation over the time period in which the biomass is produced (ΣTr)

o Crop canopy absorbtion of photosyntetic active radiation with the summation over the time period in which the

biomass is produced (ΣAPAR)

o Water use efficiency (WUE)—the slope of the relationship between biomass produced and water transpired by the

crop canopy

o Light use efficiency (LUE)—the slope of the relationship between biomass produced and photosyntetic active

radiation absorbed by the crop canopy



Assimilation models


Analyzing the relationship between transpiration & crop biomass

production

Effect of ETo in the Water

Productivity

• Different WP for C4 and C3

crops

• Lower WP under increasing

atmospheric demand

Conclusion:

• Need to normalized for

atmospheric demand

Experimental analysis in

corn (C4) and soybeans (C3) in

eastern Nebraska

Normalization of Water Productivity for

atmospheric demand

Strengths

• Do not depend on crop specie (but

different for C3 and C4 crops)

• Narrow range of variability with respect

to CO2 concentration, phenology and

soil fertility

• Normalized for atmospheric demand

Weaknesses

• Does not consider further reductions of

biomass production depending on

incoming radiation

(Raes et al. 2012)

Analyzing the relationship between transpiration & crop biomass

production

Analyzing the relationship between SAVI (crop transpiration) &

crop production

Experimental analysis in corn (C4) and soybeans (C3) in eastern Nebraska

ESTIMATION OF WUE-LUE

(Sadaña & Pinochet 2014)

Preliminary analysis of LUE based on

HybridMaize & CERES family models

Strengths

• Allows the consideration of the effect of

crop water stress (opportunity to

assimilate RS based stress estimates)

• Differential analysis of carbon fixation

and crop respiration

Weaknesses

• Strong variability depending on crop

type and environment

• Additional effects of the light

compensation and saturation

phenomena

• Difficulty to estimate daily fPAR from

instantaneous measurements-estimates

Wheat

Pea

BIOMASS PRODUCTION

Considering that the driving force for biomass production could

vary depending on the location, environmental conditions and the crop

phenology an integrated analysis of biomass production based on both

WUE and LUE approaches should be considered.

• Ideally, LUE and WUE approaches should provide similar estimates under non-

limiting conditions, this is, non-limiting radiation and water availability. Thus both

approaches can be integrated by using assimilation procedures.

• Under limiting conditions, the most restrictive approach could provide the better

approach.

fPAR/APAR

estimates


relationships and radiative

transfer models

Yield/Grain

production

ET & water stress

estimates

Hybrid model based on

ALEXI-DISALEXI

Site/Plot specific

Yield partitioning

Existing models on

Yield partitioning

Biomass

Site/Plot specific

WUE/LUE

Existing models on

WUE & LUE


MODIS Constellation




Yield/Grain

production (Y)

ET & water stress

estimates

Based on soil water balance

remote sensing driven and hybrid

model

Site specific

Harvest index (HI)

Existing models on

Yield partitioning and

experimental data

Biomass (B)

Normalized water

productivity (WP*)

Normalization of WUE for

atmospheric demand


MODIS Constellation



GENERAL OVERVIEW: SIMPLIFIED APPROACH

Y=B*HI

• Theoretical approach to Biomass production at canopy scale and plant

transpiration or transpiration coefficient (Kt) (Steduto et al. 2007)

Ktmin=0 (for bare soil) and Ktmax≈mid season Kcb (Raes et al. 2012)

• Well established relationship between SAVI and plant transpiration

This rrelationship is generally established in terms of basal crop coefficient (Kcb),

Kcb=0.15 for bare soil and Kcb=max for SAVI max. These approaches may

overestimate Biomass production for low coverage canopies.

But, González-Dugo and Mateos (2008) proposed a non-linear relationship with

Kcbmin=0 and Kcbmax=mid season Kcb

Analyzing the relationship between SAVI & crop biomass

production

K=0.47; K´=0.55

K=0.55; K´=0.57

Re-analyzing the analytical approach to convert VI in crop

coefficients for irrigation management.

• Non-linear relationships

for both crops

• General good agreement

with moderate differences for

minimum SAVI values

• Need to consider the role of

bare soil in ET rates also in the

absence of plant development

Yield/Grain

production (Y)

ET & water stress

estimates

Based on soil water balance

remote sensing driven and hybrid

model

Site specific

Harvest index (HI)

Existing models on

Yield partitioning and

experimental data

Biomass (B)

Normalized water

productivity (WP*)

Normalization of WUE for

atmospheric demand


MODIS Constellation



GENERAL OVERVIEW: SIMPLIFIED APPROACH

Y=B*HI

Analyzing the relationship between SAVI (crop transpiration) &

crop production

Experimental analysis in corn (C4) and soybeans (C3) in eastern Nebraska

Analyzing the relationship between SAVI & crop production

Experimental values of

Harvest Index

for corn and Nebraska

• Mean values around 0.5 for

both crops and irrigation

managements

• Strong variability depending on

meteorological conditions

• Difficulty to determine

empirically because of the

dynamic of biomass

accumulation

FINAL COMMENTS

• Use of remote sensing to estimate crop biomass appears straightforward for the

case of maize and soybeans. Needs to be further examined for multiple crops

• Remote sensing allows for spatial estimates of biomass and yield

• With concurrent estimates of ET, crop water productivity can be estimated spatially

• For the NENA region, downscaled values of ET and biomass would be used

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