Integrations Remote Sensing Mapping with the

Environmental Model to Quantify Emissions from

Rice Paddies in Thailand Tanita Suepa

Jiaguo Qi Siam Lawawirojwong

Department of Geography Center for Global Change and Earth Observations

Michigan State University East Lansing, Michigan, USA

suepatan@msu.edu

Abstract-Wetland rice soils have been identified as an

important source of GHG emissions at the global scale,

particularly methane emissions. As paddy rice cropland in

Thailand accounts for 52% of all cultivated land in the country

and 6% of the world's rice paddies, accurately estimating

emissions from rice paddies has become important in this

country for GHG inventories or mitigation policies. This

research integrated biogeochemical models with remote sensing

technology to advance the DNDC regional application. This

research identified spatio-temporal patterns of GHG emissions

(COz, CH4, and NzO) from rice fields in Thailand (Lopburi

province). New Method and database system were developed to

increase accuracy of DNDC model; moreover, spatial and

temporal characteristics of phenological information derived

from remote sensing data (MODIS) were used in DNDC to

quantify emissions. The results demonstrate the influence of

human management, climate variation, and physical geography

on the change of GHG emissions. Phenology of rice and human

management were the major factors effecting the changes of CH4

emissions. The change of COz emissions showed rapid changes in

extreme climate years. NzO emission was strongly related to

climate variation, especially rainfall changes. Rice intensification

with longer length of flooding period and high application rates

of fertilizer extremely enhanced CH4 production. Light soil

texture produces higher emission than heavy soil texture. The

results suggest that practical mitigation options should be

carefully regulated to more efficiently balance among the

emission types as well as to maintain or improve grain yields.

Keywords-Remote Sensing; Phenology; DNDC; GHG emissions; agroecosystem; rice; Thailand

I. INTRODUCTION

Agricultural activItIes are considered to be an important source of the increase in GHG emissions into the atmosphere [1]. Increasing demand for food and industrial crops has led to increases in agricultural land conversion and intensification, generating considerable environmental problems. Numerous observations indicated that advanced farming practices (e.g., irrigation, tillage, fertilizers) to increase yields and agricultural production resulting in increasing GHG emissions (Carbon

dioxide (C02), methane (CH4), and nitrous oxide (N20)) [2][3][4][5].

The Chaopraya River delta in the central plains of Thailand is one of the major rice producing regions in the world. Paddy rice cropland in Thailand accounts for 52% of all cultivated land in the country and 6% of the world's rice paddies [6][7]. Additionally, Thailand is the world's largest exporter of rice [6]. Wetland rice soils have been identified as an important source of GHG emissions at the global scale, particularly methane emissions [8]. Therefore, accurately estimating emissions from rice paddies in this country has become important for GHG inventories or mitigation policies at country and regional levels.

This research applied remote sensing technology to develop the new database based on a grid-based system (250 m x 250 m) for a process-based biogeochemical model, the Denitrification and Decomposition Model (DNDC), in order to identify spatio-temporal patterns of GHG emissions (C02, CH4, and NP) from rice fields in Thailand (Lopburi province). Spatially differentiated input information such as soil, climate, and management practices were created in site mode of the DNDC model with grid-based unit to provide the output on a regional scale. Additionally, spatial and temporal characteristics of phenological information derived from remote sensing data (MODIS) were used in DNDC to quantify emissions.

A. Study Area

II. METHODOLOGY

Lopburi province was selected for quantifying emission in this research. Lopburi is located in the Chaopraya River delta in the central plains of Thailand. Most of rice fields in this site are irrigated rice and double rice cropping systems. Single rice cropping systems are found in the rest of the province. Modem cultivation managements are extensively practiced in this location.

B. Data Sources

The following input data are used in this research (Table1).

TABLE 1 INPUT PARAMETERS FOR DNDC MODEL

Parameters Data Environmental Factors Climate 2002-2010: Daily climate data (Max-

Min Temperature, rainfall)

Soil pH, Density, SOC, % Clay

Agricultural Management Rice area Land use data

2002,2010

Rice cultivation Phenological Data (Planting/Harvest Dates) 2002-2010

Tillage 15 DBP"/ plow depth of 20 cm

Fertilization (kg/ha) 2002 #1 20 DApb; Ape = 90 #2 60 DAP; Urea = 30 2010 #1 20 DAP ; AP= 150 #2 60 DAP; Urea = 60

Residues Management 10% (Fraction of residues left in fields)

Flooding Start: 7 DAP End: 15 DBHd Continuously flooding

a DBP = days before planting;; b DAP = days after planting; C AP = Ammonium phosphate; d DBH = days before harvest;

C. Database Development and Input Parameters

This research used the DNDC model version 9.4 [9] with the site mode to simulate GHG emissions at the site level with grid-based unit to provide the output at regional level. All the spatially differentiated input information, climate data, spatial soil databases, rice fields, and agricultural management practices was constructed in a grid-based system with a cell size of 250 m x 250 m. Spatial and temporal characteristics of phenology (the start and end date of growing season) derived from MODIS images were used in DNDC to quantify emissions. In this way, the simulation at the site level with a grid-based unit can provide more detailed and accurate agroecosystem information because this approach could reflect the spatial diversity of crop growth environments. All dataset was segmented to rice areas and ignored other croplands. Then, the input text files were generated for DNDC model by using

Source

Rainfall data acquired from meteorological stations (Thai Meteorological Department )

(Department of Land Development)

- National Land Use Dataset of Thailand (Land Development Department, Ministry of Agriculture)

Phenological data acquired from MODIS EVI dataset on board NASA's Terra spacecraft (MOD13Q1 product) with 250 m spatial resolution from 2001-2010, every 16-day EVI composite period (23 dates per year with 460 tiles per year) (htt12.:/lreverb.echo.nasa.govlreverbl)

- Local rice research center,

- Rice Department, - Office of Agricultural Economics, Ministry of Agriculture and Cooperatives, Thailand

ERDAS and Python programing to derive data in each grid cell into text files. The model simulated the emission cell-by-cell across the entire rice fields in the study site and provided the annual emission rate per pixel (kg C/ha/year).

D. Spatio-temporal Patterns and Changes ofGHG Emissions

under Different Scenarios

In order to identify the effect of the input parameters on the model regional emission, this research conducted four scenarios for a selected site. The first scenario is the baseline scenario for 2002 and 2010, which was constructed based on the actual climate, phenology, land use, and farming management (fertilization, flooding period, and yield). The second scenario tested the effect of fertilization on GHG emissions by using different set of fertilization for 2002 and 2010 and keeping all other input parameters constant (apply input parameters of 2002 for 2010, except for fertilizer rate). The third scenario conducted multiyear simulations from 2002 to 2010 to investigate the impacts of interannual variations in

climate condition. This scenario used actual nine-year climate data (temperature and rainfall) for each simulated years. The last scenario was also set for multiyear simulations from 2002 to 2010. This scenario examined the effects of phenological changes by using actual phenology (start and end of the growing season) for nine years and all other inputs were held constant. The results of these scenarios could represent the change of GHG emissions with the effect of human management as well as climate variation.

E. Comparison of DNDC Results to IPCC Approach and

Thailand Research

Due to the unavailable field data for validation, this research validated the results of DNDC model by using IPCC guidelines [10] and the emission rates from the papers in Thailand [11].

III. RESULTS

The results of four scenarios estimating GHG emissions from rice fields in Thailand demonstrate the influence of human management, climate variation, and physical geography

2002

on the change of GHG emissions (Fig. 1, Fig. 2, Fig. 3, and Fig. 4). The results of GHG estimations indicate that phenology of rice and human management were the major factors effecting the changes of CH4 emissions (Fig. 2 and Fig. 4). The change of CO2 emissions was relatively smaller than CH4 and N20 in all scenarios but showed rapid changes in extreme climate years (Fig. 3). N20 emission was strongly related to climate variation, especially rainfall changes (Fig. 3). Soil texture plays an important role on CH4 emissions with high CH4 emissions in light texture soil. Rice intensification with longer length of flooding period (longer length of growing season) and high application rates of fertilizer extremely enhance CH4 production (Fig. 2 and Fig. 4) due to the change in the chemical and biophysical properties of the plant-soil system. Additionally, the results indicated the closet agreement between modeled emission rate and standard emission rate of IPCC approach under 200-day continuously flooded; moreover, the modeled emission results were also in good agreement with scaling factors from Thailand research.

CH4 Emission Rate C02 Emission Rate N20 Emission Rate

2002 2010

2010 CH4 Emission Rate

Fig. 1. GHG emission rate in scenario 1

2002 2010

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C02 Emission Rate

2002 2010

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N20 Emission Rate

2002

2010

CH4 Emission Rate

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C02 Emission Rate

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N20 Emission Rate

Fig. 2. GHG emission rate in scenario 2

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230

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170

1000

800

.. 600 ..c: lJ � 400

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Emission Rate

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Climate Input Data

Mean Max Temperature

- ./

30 +--.--,,--,--,--,---,--,--,--, 2002 2003 2004 2005 2005 2007 2008 2009 2010

200 Lenght of Rainy Season

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120

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Fig. 3. Comparison between climate input data and GHG emission rate in scenario 3

.. ..c:

Emission Rate

250 .-------::C",.H.,....4,----------230 +------------------------------1

0'210 ........ � � / Y 190 """'-- ,/ � ��-------". -

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Phenology Input Data

350 ,..-----------------------------Length of Growing Season 300 +-,�----------------------____:.,.____

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0040 r-:::.�Z s::�� __ �;:���S�::;;;::::;;-� 0.35 +----------=-----------------------

0.30 +--------------------------------

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0.20 +---.---,-----,------,---,-----,----,-----,-----, 2002 2003 2004 2005 2006 2007 2008. 2009 2010

Fig. 4. Comparison between phenology input data and GHG emission rate in scenario 4

IV. DISCUSSION AND CONCLUSIONS

The method introduced in this research, DNDC at the site mode with grid-based unit to provide the output on a regional scale, could play an important role in linking management changes to biogeochemical cycles based on spatially differentiated information. This approach is able to reserve the advantages of site-based modeling but also meet the demand for large-region estimation. Additionally, satellite derived phenology is alternative information to apply in DNDC. This method would greatly improve the estimation comparing to the IPCC method based on the baseline emission factor.

The results of this research indicate that GHG emission not only vary spatially but also change significantly over time.

Climate variation has both direct and indirect impacts on GHG emissions. Climate variation alters plant-soil system resulting in GHG emission change. This research demonstrates that extreme climate events (flood and drought) have a significant impact on CH4 and CO2. It is also found that the most sensitive factor for N20 emissions from rice fields is the change in rainfall. In addition, climate change indirectly affects GHG emISSIOns by influencing changes in phenology and management practices. Moreover, a change in phenology is strongly related to a management practice leading to a change of GHG emissions. For example, extreme climate events causes a change of integral and length of growing season, resulting in a change of emissions due to a crop production change. Human management in farming practices also produces huge environmental impacts. Two major

management practices-flooding period and fertilization­affect emissions from paddy soils by altering the chemical and biophysical properties of the plant-soil system. The continuously flooding practice increases CH4 but leads to lower N20. An increase in rates and applications of fertilizer enhances CH4 and N20 emissions. Additionally, soil properties also influence GHG emissions, particularly for CH4. Light soil texture produces higher emission than heavy soil texture. Low soil Eh tends to increase CH4 emission, but decreases N20 emission.

The results imply that mitigation of CH4 emission should be emphasized for rice field because its emission is much higher than CO2 and N20. Additionally, these gas emissions not only represent a negative impact on the environmental quality but could also lead to potential economic losses. Therefore, balancing food production and environmental protection, and predicting the impacts of climate change are important for agroecosystem.

ACKNOWLEDGMENT

This research is supported by Center for Global Change and Earth Observations, Michigan State University. The authors express their thanks to Dr. Jiaguo Qi for his professional guidance and valuable comments on this research.

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