# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 4 Combination of Optical and Thermal Remote Sensing
### Applications of the Temperature-Vegetation Space in Drought Monitoring  
First utilized in evapotranspiration estimation in the 1990s [209], the Temperature-Vegetation space began to be used in drought monitoring soon after. Based on the triangular Temperature-Vegetation space, [PERSON] et al. [194] developed the Vegetation Temperature Condition Index (VTCI):  
\[\text{VTCI}=\frac{\text{LST}_{\text{NDVI}_{i}}^{\text{max}}-\text{LST}_{\text {NDVI}_{i}}}{\text{LST}_{\text{NDVI}_{i}}^{\text{max}}-\text{LST}_{\text{NDVI}_ {i}}^{\text{min}}} \tag{16}\]  
\[\text{LST}_{\text{NDVI}_{i}}^{\text{max}}=a_{1}+b_{1}\text{NDVI}_{i} \tag{17}\]  
\[\text{LST}_{\text{NDVI}_{i}}^{\text{min}}=a_{2}+b_{2}\text{NDVI}_{i} \tag{18}\]  
where \(\text{LST}_{\text{NDVI}_{i}}^{\text{max}}\) and \(\text{LST}_{\text{NDVI}_{i}}^{\text{min}}\) represent the maximum and minimum LST of pixels with the same NDVI\({}_{i}\) value in the study area, respectively. \(a_{1}\), \(a_{2}\), \(b_{1}\), and \(b_{2}\) are the coefficients of the fitting equation for the LST and NDVI of the dry and wet edges. From the discussion above, it can be seen that VTCI is equivalent to the soil moisture availability defined in the conventional trapezoid. [PERSON] et al. [197] verified the feasibility of applying VTCI in monitoring drought threats in India by using MODIS/Terra NDVI and LST 8-day composition products.  
Similarly, [PERSON] et al. [218] established the Temperature Vegetation Dryness Index (TVDI):  
\[\text{TVDI}=\frac{\text{LST}-\text{LST}_{\text{min}}}{a+b\cdot\text{NDVI}- \text{LST}_{\text{min}}} \tag{19}\]  
TVDI is almost equivalent to VTCI (ideally, VTCI \(=1-\text{TVDI}\)); however, the wet edge is assumed to have a constant temperature \(\text{LST}_{\text{min}}\) instead of a linear function of NDVI. Due to its simplicity and clarity, TVDI is one of the most widely used remote-sensing-based drought indices [219].  
Besides LST and FVC (or by proxy NDVI), other physical variables have also been exploited in constructing the Temperature-Vegetation space. [PERSON] et al. [220] made a comprehensive review of different variable combinations. LST has been substituted with surface-air temperature difference [221] and day-night temperature difference [222], while FVC/NDVI has been substituted with surface albedo [223]. The improved TVDI (iTVDI) proposed by [PERSON] et al. [224] used the surface-air temperature difference \(\Delta T=T_{\text{s}}-T_{\text{a}}\) for the temperature axis and was found to have stronger relationships with precipitation and soil moisture than the traditional TVDI; thus, it was considered to be a better indicator of water stress.  
Such attempts have continued in recent decades. For instance, [PERSON] et al. [225] assessed a long-term drought across China using the Microwave TVDI (MTVDI), which has the same form as TVDI but uses the microwave brightness temperature instead of thermal infrared LST, and Microwave NDVI (MNDVI), which is based on the Microwave Polarization Difference Index (MPDI) instead of optical NDVI. [PERSON] et al. [226] proposed the Temperature Fluorescence Drought Index (TFDI), which replaces NDVI with SIF. Applications in Henan Province, China, showed that this new drought index could effectively reflect the severity of the agricultural drought. It should be noted here that such replacements still need to be evaluated and validated by physical models (e.g., the SVAT models) to be better calibrated and illustrated.  
A potential improvement lies in the usage of the two-stage trapezoid in drought monitoring. Currently, the two-stage trapezoid is mainly used for evapotranspiration estimation [227], while the drought monitoring community has not widely adopted this concept.