## 2 Enhancing Food Production through RS Applications in Agriculture
### Modelling and Forecasting Future Crop Yields  
The need to model and forecast crop yields in ASA regions is increasing in parallel with recent and projected changes in land use and climate and with recent crises in food security ([PERSON] et al., 2018). Forecasting crop yield is essential for the agricultural and economic stability of regions and is vital to sustaining global food security [29]. Farmers, policymakers, investors and hedgers need accurate and timely information on crop quality and supply. This information can help governments and local agencies to ensure strategic contingency plans to redistribute food during times of crisis [29; 104]. Different seasonal yield forecast methods have been highlighted in the literature. Traditionally, crop yield forecasts are conducted by farmers through the within-season assessment of crop growth. However, this approach is uncertain and inconsistent due to subjective expectations of crop yield [105]. For decades, statistical modelling to forecast crop yield has been used globally. Statistical regression-type models applied to agorten-orological data provide a common approach to seasonal yield forecasting [106; 107]. The simplicity of the approach has resulted in wide application. However, low accuracy in other areas outside the boundaries of the observed data is a real concern [108]. Considering the climatic variabilities and frequent extreme events that occur in ASA regions, such an approach might be insufficiently robust for crop yield forecasting. Numerous seasonal yields forecasting approaches were also developed based on statistical models using RS data [29; 109; 110]. In addition, crop growth simulation models have been used for crop yield forecasting. Such models can incorporate soil, crop, management and weather information as inputs to simulating plant growth [111], and the models can incorporate RS derivative datasets to forecast crop yield [112]. However, the high data demands and computational costs of these models make them generally challenging to employ in some regions for which data are sparse. Therefore, in model selection, regional constraints should be considered carefully. It is important to consider which spatial, temporal, and spectral characteristics of RS data meet ASA regional requirements for monitoring food security. For example, medium-to-coarse spatial resolution satellite sensor data (e.g., MODIS) were commonly used in crop early warning systems by many aid organisations (e.g., FAO) to mitigate food insecurity [113]. In contrast, moderate spatial resolution imagery such as Landsat and Sentinel are required if the study aims are localised at the farm level. Several researchers have evidenced the advantage of RS approaches for forecasting crop yield in these regions. For example, a simple remote sensing vegetation index approach can produce more accurate crop yield maps compared to the agro-meteorological Simple Algorithm for Yield (SAFY) model [114]. Beyond the generally utility of RS-derived _vegetation_ indices to estimate and forecast crop yield, other RS indices can also be used to forecast crop yield more accurately in ASA regions. For example, MODIS-derived NDVI, the two-band enhanced vegetation index (EVI2) and the Normalised Difference Water Index (NDWI), in association with county-level data, were used to develop empirical models predicting soybean and maize yield in the central United States [36]. Although, in general, large correlations ranging from R\({}^{2}\) = 0.69 to 0.73 were found between the forecasted and ground crop yield for all indices, NDWI was more accurate in semi-arid regions due to its sensitivity to low-density agriculture, irrigation and variation in the liquid water content of vegetation canopies [36]. Therefore, prior consideration should be given to determine the best RS index to use for each specific regional climatic condition. In some ASA regions where geographical access and availability of data are limited, RS can also provide a vital alternative source of information.