Satellite-based drought impact assessment on rice yield in Thailand with SIMRIW-RS

Mongkol Raksapatcharawong, Watcharee Veerakachen, Koki Homma, Masayasu Maki, Kazuo Oki

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Article number2099
JournalRemote Sensing
Issue number13
Publication statusPublished - 2020 Jul 1


  • Crop simulation
  • Drought assessment
  • LAI
  • LST
  • NDVI
  • Yield estimation


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