# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
###### Abstract
In the 20 th century, [PERSON] [14] was among the first to assess agricultural drought using precipitation data collected by meteorological stations. The major problem is that drought occurrence at different growth stages has different effects on crop growth and the final grain yield [12]. Moreover, there are differences in drought vulnerability and tolerance even among crops within the same field block, depending on species and genotypes. Therefore, there is a high demand for temporal and spatial resolutions of agricultural drought monitoring [15], which cannot be satisfied by traditional meteorological stations or the re-analysis of data based on station observations, especially on large scales. The rising of satellite remote sensing has provided a new solution to this issue. In past decades, remote sensing datasets on precipitation, snow, soil moisture, land surface temperature, evapotranspiration, vegetation, and land cover have become increasingly abundant with the development of satellite remote sensing [16; 17], thereby creating a solid  
Figure 1: Distribution of major drought events since 1960.  
foundation for the timely and effective implementation of agricultural drought monitoring. According to the sensor type, remote sensing can be further divided into optical remote sensing, thermal remote sensing, microwave remote sensing (including active and passive), and other categories such as LiDAR or gravity remote sensing. They each offer unique and complementary information for drought detection. 1. Optical and thermal data are the most widely used in identifying vegetation conditions, soil water status, and evapotranspiration [17];
2. Microwave remote sensing has a direct and solid link to soil moisture, which is a crucial indicator of agricultural drought [18];
3. LiDAR is the best approach to obtaining structural information of vegetation, and it can also be used to retrieve various biochemical variables such as leaf water content [19];
4. Gravity measurement is essential for monitoring groundwater; thus, it can be utilized to monitor those regions where groundwater is massively used for irrigation, especially when drought occurs [20]. We will not cover all these aspects in this review; instead, we will focus on optical and thermal remote sensing, considering their data abundance, availability and applicability. It should be noted that microwave remote sensing, especially passive microwave remote sensing, is the most direct and reliable way to estimate soil moisture from space [18], and it has a significant advantage over optical and thermal remote sensing in that it is less affected by clouds and weather conditions [21; 22]. However, there are several issues when microwave remote sensing is applied to agricultural drought monitoring: (1) the variability in penetration depth due to soil types, temperature and soil moisture makes it hard to compare the soil moisture estimations of microwave remote sensing with field measurements, especially in the case of drought monitoring [23]; (2) the effects of vegetation on microwave signals are not easy to handle [24; 25; 26; 27], whereas cropland is the primary concern in the monitoring of agricultural drought; (3) the coarse spatial resolution of passive microwaves (active microwaves generally have much better spatial resolution than passive microwaves) cannot meet the requirements of agriculture. For further information about microwave remote sensing of soil moisture, one can refer to [PERSON] et al. [28; 29] for both algorithms and products, and [PERSON] et al. [15] for downscaling techniques used to enhance the spatial resolution of passive microwave-derived soil moisture. For the usage of gravity data in groundwater storage monitoring, one can turn to [PERSON] and Ramillien [20]. Moreover, for LiDAR, research articles such as [PERSON] et al. [19] can be a good reference.