## 2 Enhancing Food Production through RS Applications in Agriculture
### Identifying and Estimating Crop Area  
Information on crop area and crop type are vital for land management and trading. Even in well-developed and well-organised countries, ground-based crop area estimates are often not available until a few months after harvest [39]. Obtaining a reliable figure before harvest is a major challenge and, at the same time, vital for the formulation of policymaking and decision-making [39; 59]. Figure 2 shows the processing steps typically applied to remote sensing data to predict crop type. Compared to a humid environment, ASA regions typically have specific climatic and regional characteristics which pose many challenges to accurately identifying and estimating crop area [39; 59]. Due to considerable changes in the spatial and temporal distribution of rainfall, and the fact that the majority of smallholder farms in ASA regions rely on rainfall to start planting, identifying and estimating crop area can be difficult [39]. Spatial and temporal variation in rainfall could result in variation in agricultural management, crop choice, heterogeneity in the cropping system, crop phenology and productivity [60; 61]. Often phenological variables are used as a critical base to identify and estimate crop area. However, in ASA regions, these variables could vary substantially among the countries and inter-annually [39; 61; 62]. Therefore, attempting a single classification approach at a regional or global scale could result in inaccurate outputs. For example, [PERSON] et al. [39] developed a phenological approach in Iraq which produced a coefficient of determination with the official statistical crop data at the governorate level twice that of the global MODIS land cover classification. At the global scale [63] and in particular for Africa, large discrepancies were observed between current continental and global landcover maps in terms of the overall and spatial distribution of croplands. Thus, current global crop maps might not perform well in these regions and a customised approach should be considered to derive a sufficiently accurate crop map. \begin{table}
\begin{tabular}{c c c} \hline \hline
**RS Applications** & \multicolumn{2}{c}{**Activities**} \\ \hline Identifying and & \(\bullet\) & Cropland mapping and crop type classification [41; 42; 43]. \\ estimating crop area & \(\bullet\) & Estimate and forecast crop area before harvest time [39; 44]. \\  & \(\bullet\) & Obtain sufficient data in insecure regions [45; 46]. \\ \hline Monitoring agriculture & \(\bullet\) & Differentiate among diseases and pests, infectious severities and map their spatial distribution [47; 48; 49; 50]. \\ crop status & \(\bullet\) & Assess before and after intervention crop condition, water and nutrient requirement [51; 52; 53]. \\ \hline Modelling and & \(\bullet\) & Fine resolution local and regional yield map and advance yield prediction [29; 36; 54]. \\ forecasting future & \(\bullet\) & Generate yield gap map and underperforming croplands [55; 56]. \\ crop yield & \(\bullet\) & Obtain sufficient crop production data in insecure regions [57; 58]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The role of remote sensing for monitoring agricultural productivity and increasing food security. Figure 2: Schematic diagram illustrating the general methodology for using RS data to forecast crop yield. Step 1: processing steps typically applied to remote sensing data to predict crop type and Step 2: processing steps typically applied to forecast crop yield. Another challenge for accurate identification and estimation of crop area in ASA regions is the common practice of the agriculture fallow system [39; 44]. Depleted soil, lack of adequate water distribution and the existence of pests and diseases have led smallholder farmers in ASA regions to practice a biennial fallow system from which many fields remain uncultivated annually. For example, the extent of fallow fields in the cropland class was estimated to be 63% (403,617 km\({}^{2}\)) for the Sahel region in 2017 [44].