Satellite characterization of drought over cultivated areas in Central America

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Abstract

Effects of drought on crop production have been a prevailing challenge confronting farmers in Central America. Information on drought associated with cropping areas is deemed vital for officials to evaluate issues of crop production losses and food insecurity in the region. This study aimed to develop an approach for agricultural drought assessment in Central America, using Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) and enhanced vegetation index (EVI) data during December 2000 to December 2016. The data processing comprised three steps: (1) LST and EVI reconstruction using random forest, (2) drought delineation using the temperature vegetation dryness index (derived from LST and EVI), and (3) error verification. The reconstructed results of LST and EVI data verified with the cloud-free reference data indicated coefficient of determination (R 2 ) values higher than 0.84, while the root mean square error values achieved for LST and EVI were smaller than 1.5°C and 0.07, respectively. The spatially aggregated results of drought delineation compared with the soil moisture active passive data showed that the correlation coefficient (r) values obtained for the apante season (December−March) were between −0.4 and −0.8, while the values for the primera (April−August) and postrera (September−November) seasons were in ranges of 0 to −0.7 and −0.1 to −0.6, respectively. The larger area of very dry moisture condition was especially observed for the 2002 and 2014 primera seasons. Eventually, the probability of drought occurrence calculated every 8 days over a 16 years period was spatially aggregated with the cropping areas at the municipality level for crop management purposes in the region.

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Son, N. T., Chen, C. F., Chen, C. R., Castellón, A., Masferrer, M. G. M., & Recinos, L. E. M. (2018). Satellite characterization of drought over cultivated areas in Central America. International Journal of Remote Sensing, 39(23), 8505–8526. https://doi.org/10.1080/01431161.2018.1488287

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