## 3 Results
### Focus of the Studies
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study Focus** & **Category** & **Main Findings and Remote Sensing Potential** & **Challenges** & **Sources** \\ \hline \multirow{4}{*}{Climate Impacts} & Evaportranspiration & Data fusion for daily field-scale LST and & Lack of bias-free validation and & [23, 169, 170] \\  & Evaportranspiration & ET estimates & high-resolution thermal data & [23, 169, 170] \\ \cline{2-5}  & Drought and Dry Spills & Vulnerability to climate change & Limitations of RS indices & [171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185] \\ \cline{2-5}  & & Impact analysis on drivers of trends in & Downscaling coarse NDVI data of & [35, 170, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 262, 269, 260, 262, 264, 266, 267, 269, 268, 269, 261, 262, 265, 266, 267, 269, 262, 268, 269, 260, 269, 261, 262, 263, 265, 266, 267, 269, 268, 269, 260, 267, 269, 261, 262, 263, 264, 265, 266, 267, 268, 269, 262, 269, 260, 261, 263, 267, 262, 269, 264, 268, 269, 262, 261, 265, 267, 263, 268, 269, 262, 269, 260, 262, 263, 264, 265, 266, 267, 268, 269, 269, 260, 261, 262, 267, 268, 269, 262, 269, 263, 265, 266, 267, 268, 269, 269, 260, 261, 262, 263, 267, 269, 262, 264, 265, 266, 267, 268, 269, 269, 260, 262, 261, 263, 262, 264, 267, 268, 269, 260, 263, 269, 261, 262, 265, 266, 267, 268, 269, 262, 269, 261, 263, 264, 267, 269, 262, 265, 266, 268, 269, 267, 269, 268, 269, 260, 269, 261, 262, 263, 267, 269, 262, 264, 268, 269, 260, 263, 265, 267, 269, 261, 262, 266, 267, 268, 269, 262, 269, 260, 263, 267, 269, 261, 262, 268, 262, 269, 262, 263, 264, 265, 267, 269, 268, 269, 260, 269, 262, 26, 261, 263, 264, 267, 26, 265, 266, 267, 268, 269, 269, 26, 262, 26 Several studies (19 in this review) have explored the use of RS data for predicting crop yields and assessing agricultural productivity in West Africa. [PERSON] et al. (2000) [80] pioneered a cost-effective approach using AVHRR NDVI data to forecast millet and sorghum yields in Niger. [PERSON] and [PERSON] [104] (2004) focused on evaluating the capacity of RS methods to assess long-term trends in crop productivity in Senegal's Groundnut Basin using similar time series data. [PERSON] et al. (2006) [103] examined the relationship between satellite vegetation measurements and millet prices in Burkina Faso. In 2016, [PERSON] et al. [67] combined vegetation and thermal indices to estimate crop yields in the Sahel, while [PERSON] et al. [102] aimed to predict sorghum yields and uncertainty across Burkina Faso's variable environmental conditions. [PERSON] et al. [101] addressed the challenge of assessing crop productivity in Mali's cotton belt due to spatial variability in farming practices, soil fertility and rainfall, using Sentinel-2 data and ground information to achieve 80% accuracy in crop type classification and improve yield predictions through incorporating parcel boundaries retrieved from very high-resolution imagery and leaf area index modeling. [PERSON] et al. (2019) [33] advanced a novel approach that integrated RS, crop modeling, and statistical methods, explaining 46% of maize yield variability two months before harvest in Burkina Faso. The Random Forest model incorporating soil moisture and canopy temperature data demonstrated the method's potential for regions with limited field data. In 2020, [PERSON] et al. [7] utilized high-resolution Sentinel-2 data to predict household-level crop yields for various crops in Burkina Faso and assessed the suitability of different crop types using multiple linear regression. [PERSON] et al.