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Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

1
Chulabhorn Satellite Receiving Station, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
2
Graduate School of Agricultural Science, Tohoku University, Sendai 981–8555, Japan
3
Faculty of Food and Agricultural Sciences, Fukushima University, Fukushima 960–1298, Japan
4
Institute of Industrial Science, The University of Tokyo, Tokyo 153–8505, Japan
5
Faculty of Engineering, Kyoto University of Advanced Science, Kyoto 615–8577, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2099; https://doi.org/10.3390/rs12132099
Received: 19 May 2020 / Revised: 26 June 2020 / Accepted: 26 June 2020 / Published: 30 June 2020
Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change. View Full-Text
Keywords: crop simulation; drought assessment; LAI; LST; NDVI; yield estimation crop simulation; drought assessment; LAI; LST; NDVI; yield estimation
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Raksapatcharawong, M.; Veerakachen, W.; Homma, K.; Maki, M.; Oki, K. Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS. Remote Sens. 2020, 12, 2099.

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