Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil

Abstract

Drought is a natural disaster that affects a country’s economy and food security. The monitoring of droughts assists in planning assertive actions to mitigate the resulting environmental and economic impacts. This work aimed to evaluate the performance of the standardized precipitation index (SPI) using rainfall data estimated by orbital remote sensing in the monitoring of meteorological drought in the Cerrado–Amazon transition region, Brazil. Historical series from 34 rain gauge stations, in addition to indirect measurements of monthly precipitation obtained by remote sensing using the products CHIRPS-2.0, PERSIANN-CDR, PERSIANN-CCS, PERSIANN, GPM-3IMERGMv6, and GPM-3IMERGDLv6, were used in this study. Drought events detected by SPI were related to a reduction in soybean production. The SPI calculated from the historical rain series estimated by remote sensing allowed monitoring droughts, enabling a high detailing of the spatial variability of droughts in the region, mainly during the soybean development cycle. Indirect precipitation measures associated with SPI that have adequate performance for detecting droughts in the study region were PERSIANN-CCS (January), CHIRPS-2.0 (February and November), and GPM-3IMERGMv6 (March, September, and December). The SPI and the use of precipitation data estimated by remote sensing are effective for characterizing and monitoring meteorological drought in the study region.

Meteorological Drought Monitoring Based on Satellite CHIRPS Product over Gamo Zone, Southern Ethiopia

“…Drought indices are important instruments for defining and monitoring drought because they simplify complex meteorological functions and can quantify climatic abnormalities in terms of severity, length, and frequency [38,39]. Moreover, over the last few decades, many studies have been conducted to monitor the drought in Ethiopia, and most of these studies are SPI-based drought [40,41] analyses based on location-specific observed or stationed rainfall data. Reconnaissance drought index (RDI) [42], normalized difference vegetation index (NDVI), land surface temperature (LST), vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI) [43] methods were used to measure drought, and the Mann-Kendall and Sen's slope test to detect rainfall and temperature trend over the central Rift Valley and Gamo Zone regions of Ethiopia, which depend on weather station data and vegetation condition.…”

The Role of Machine Learning in Remote Sensing for Agriculture Drought Monitoring: A Systematic Review

“…One of the problems of rainfed agriculture productivity is prolonged drought, lack of rainfall, and lack of water supply in the soil during the vegetative growth phase [1], [2], [3], [4]. In addition, high temperatures during the ripening phase can reduce the conversion yield of sucrose to fructose and glucose [5].…”

Evaluation of Seven Near-Real-Time Satellite-Based Precipitation Products for Wet Seasons in the Nierji Basin, China

“…NRT SPPs are evaluated at various regions, such as China [24][25][26], Europe [27,28], South America [29], Pakistan [30], the United Arab Emirates [31], Peruvian Andes [32], and Peninsular Malaysia [33]; various basins, such as the Yellow River Source Basin in China [34], Kinu Basin in Japan [35], Lower Colorado River Basin in Texas [36], Ottawa Basin in Canada [37], and Chindwin Basin in Myanmar [38]; analyzed insight of NRT SPP performance at various aspects, such as climatology [39], seasons [40], extreme events [29,41], and utilization purposes (e.g., flood simulation [32,42], crop forecasting [43,44], drought monitoring [45], and landslides [46]).…”