Optical and Thermal Remote Sensing for Monitoring Agricultural Drought

Abstract

By effectively observing the land surface and obtaining farmland conditions, satellite remote sensing has played an essential role in agricultural drought monitoring over past decades. Among all remote sensing techniques, optical and thermal remote sensing have the most extended history of being utilized in drought monitoring. The primary goal of this paper is to illustrate how optical and thermal remote sensing have been and will be applied in the monitoring, assessment, and prediction of agricultural drought. We group the methods into four categories: optical, thermal, optical and thermal, and multi-source. For each category, a concise explanation is given to show the inherent mechanisms. We pay special attention to solar-induced chlorophyll fluorescence, which has great potential in early drought detection. Finally, we look at the future directions of agricultural drought monitoring, including (1) early detection; (2) spatio-temporal resolution; (3) organic combination of multi-source data; and (4) smart prediction and assessment based on deep learning and cloud computing.

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

“…For h and k, the respective calculation formula can be defined as (11) and (12). The difference between P i and PET i for the month i is obtained using (13). The probability density function of a three-parameter Log-logistic distributed variable has been defined as (14).…”

“…It is measurable by evaluating stream flow, snowpack, and lake and groundwater levels. To understand the association between agricultural and meteorological droughts, it is necessary to examine the correlation between vegetation indices and climate variability [5], [13], [14], [15], [16].…”

A Modified Temperature Vegetation Dryness Index (mTVDI) for Agricultural Drought Assessment Based on MODIS Data: A Case Study in Northeast China

“…Considering the application of the HVDI, we transform it into a multispectral form that can be applied to the MODIS. The MODIS has been widely used for drought monitoring, due to its advantages of large imaging width, wide spectral range, and high updating frequency [1,9,40,41]. Therefore, MODIS imagery was selected in this study, such that the HVDI in Equation (2), by changing the narrow band channel to the wide band channel, as shown in Equation (3): Considering the application of the HVDI, we transform it into a multispectral form that can be applied to the MODIS.…”

“…2023, 15,1915 2 of 22 widely used vegetation index, which uses non-linear stretching to enhance the reflectance contrast in the NIR and red spectral regions [8]. When the fractional vegetation cover (FVC) is higher than 80%, the increase in the NDVI is delayed, showing a saturation state, which leads to decreased sensitivity of vegetation detection under high FVC [9]. To overcome the saturation phenomenon of the NDVI, further vegetation indices have been established, such as the enhanced vegetation index (EVI) [9][10][11].…”

“…When the fractional vegetation cover (FVC) is higher than 80%, the increase in the NDVI is delayed, showing a saturation state, which leads to decreased sensitivity of vegetation detection under high FVC [9]. To overcome the saturation phenomenon of the NDVI, further vegetation indices have been established, such as the enhanced vegetation index (EVI) [9][10][11]. These indices are often referred to as greenness indices because they reflect the greenness of the vegetation [12,13].…”