Near-real-time drought monitoring and assessment for vineyard production on a regional scale with standard precipitation and vegetation indices using Landsat and CHIRPS datasets

被引:3
|
作者
Arab, Sara Tokhi [1 ]
Ahamed, Tofael [2 ]
机构
[1] Univ Tsukuba, Tsukuba 3058572, Japan
[2] Univ Tsukuba, Fac Life & Environm Sci, Tsukuba 3058572, Japan
关键词
Drought detection; Vineyards; Landsat; 8; OLI; CHIRPS rainfall; SVI; SPI; AGRICULTURAL DROUGHT; STRESS; GRAPEVINE; BEHAVIOR;
D O I
10.1007/s41685-023-00286-7
中图分类号
F [经济];
学科分类号
02 ;
摘要
Drought is a complicated and slow-moving natural disaster that has severe impacts on plant greenness and yields by interrupting plant photosynthetic activity. These issues mostly happen due to water shortages and elevated temperatures. Grapes are sensitive to water stress during the summer, when high evapotranspiration is combined with very low precipitation. Therefore, the main aim of this research was to identify drought-affected vineyards on a regional scale by satellite remote sensing images with a standardized precipitation index (SPI) and standard vegetation index (SVI). The time-series standard vegetation index (SVI) was developed from the time-series normalized difference vegetation index (NDVI) for 2013-2021, and the time-series SPI was calculated from time-series CHIRPS rainfall using the Google Earth engine (GEE). Drought severity maps were classified based on thresholds from extremely dry to extremely wet. Validation was performed between drought indices and grape yield at the regional level using regression analysis. The results indicated that the years 2013, 2014, 2015, 2016, 2018 and 2021 were characterized by drought across the region within the berry formation and veraison growth phases of table grape before harvest. The most drought-affected years were 2018 and 2021. In 2018, 4785.03 ha, and in 2021, 1825.83 ha were extremely affected by drought. Moreover, the validation results indicated that the highest variability of table grape yield with SPI (r(2) = 0.62) was observed in June. However, table grape yield with SVI had the highest variation in July (r(2) = 0.60). The multiple linear regression between the average yield (ton/ha) and drought indices (SVI and SPI) showed the highest accuracy in June (r(2) = 0.79, MSE = 0.2) and July (r(2) = 0.71, MSE = 0.3). These findings suggest that SVI and SPI can be utilized for large-scale near-real-time drought monitoring and assessment to develop a regional subsidy program to support grape growers during a drought.
引用
收藏
页码:591 / 614
页数:24
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