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

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.

Integrasi Prediksi Musim dengan Model Simulasi Tanaman untuk Penentuan Waktu Tanam Padi

<p><strong>Abstrak.</strong> Penyesuaian waktu tanam merupakan upaya dengan biaya yang paling efisien untuk meningkatkan produktivitas, menstabilkan, bahkan meningkatkan ketahanan pangan. Integrasi prediksi curah hujan musim dengan model simulasi tanaman dapat digunakan untuk memberikan rekomendasi waktu tanam padi dengan hasil yang optimal. Dua tahap analog digunakan untuk memprediksi curah hujan harian untuk satu musim tanam. Analog tahap pertama untuk memprediksi curah hujan harian untuk 120 hari. Tahap kedua mencari satu analog terbaik prediksi sekuens curah hujan 120 hari. Basis data hasil tanaman padi periode 1982-2009 dengan interval harian dibangun menggunakan model simulasi tanaman. Rekomendasi waktu tanam ditentukan berdasarkan perubahan hasil dibandingkan dengan waktu tanam awal. Hasil penelitian menunjukkan bahwa prediksi curah hujan musim dengan lead time 6-9 bulan menggunakan metode downscaling dengan dua tahap analog dapat memperpanjang lag prediksi 2 bulan sebelum tanam sehingga dapat digunakan untuk peringatan dini. Integrasi prediksi curah hujan musim dengan model simulasi tanaman dapat memberikan informasi selang waktu tanam yang berpotensi untuk mendapatkan hasil yang lebih tinggi. Prediksi waktu tanam dalam bentuk selang waktu diperlukan petani , karena berbagai faktor non teknis yang menyebabkan penanaman tidak dapat dilakukan pada rekomendasi waktu tertentu. Informasi tersebut dapat digunakan oleh pengambil kebijakan dan penyuluh untuk rekomendasi kepada petani tentang waktu tanam dengan hasil padi yang lebih tinggi.</p><p><br /><em><strong>Abstract</strong></em>. Adapting planting time is a very cost-efficient way to increase crop productivity and stabilise or even increase food security. Linking seasonal rainfall prediction with crop simulation model is used to evaluate planting date with optimal rice yield. We used a two step analogue method. The first step is to predict 30 daily rainfall analogues for the next 120 days. The second step is to look for best analogue of 120 day rainfall prediction. Daily planting dates were simulated within 1982-2009 using crop simulation model. The second step is to determine the best analoque for the 120 day sequence. Planting time recommendation is adjusted using the difference between the earliest and later planting dates.The result concluded that 6-9 lead time seasonal rainfall prediction using two step analogue could increase lead time 2 months prior to planting time, therefore can be use for early warning. Linking season rainfall prediction with crop simulation model to adjust interval of planting time that provide higher rice yield. Farmers need that interval, due to non-technical factors are caused crop could not planted timely as recommended. In addition, the recommendation of planting time should be used by decision makers and extension workers to recommend appropriate planting time with higher yield to the farmers.</p>