## 3 Remote Sensing Challenges and Solutions to Increase Agricultural Productivity
### Data Issues
#### 3.1.3 Small Agriculture Field Size and Insufficient Spatial Resolution  
A challenge for estimating crop area and type in some ASA regions, or parts of these regions, is the heterogeneity of the landscape and small agricultural field size [39; 190]. To monitor crops efficiently, a high temporal revisit frequency over large geographic areas is required [135]. Meanwhile, this limits the spatial resolution of the data. A coarse spatial resolution (e.g., <1 km) is problematic where pixels are mixed, meaning that several signals corresponding to different land cover types occur within a single pixel, for example, due to small agricultural field sizes [39]. In small agricultural field sizes, this variability is especially problematic as the spectral reflectance in gridded moderate resolution products such as from the MODIS and Medium Resolution Imagine Spectrometer (MERIS) sensors may represent a mix of different land covers and heterogeneous cropping patterns [191; 192]. In addition, due to the similar spectral signature and phenological characteristics between natural vegetation and crops, or among different crop types (particularly wheat and barley), their discrimination in these regions is often challenging. Although, hard classification (i.e., the allocation of whole pixels to single crop classes) of moderate spatial resolution (30 m) images produced accurate results for commercial farming, it could not deal with mixed pixels in Ethiopia because of the small agricultural field size [193]. For countries located in semi-arid zones such as Zambia, Niger and Cameroon in Africa, croplands were mostly confused with savannas and grasslands, followed by shrulands and woodlands [194]. This might be due to the relatively coarse spatial resolution of the data used, as such data have intrinsic limitations in highly heterogeneous and intermixed land uses [39; 194]. Part of this confusion might be related to differences in crop calendars; classification accuracy can be affected where agricultural practices are advanced or delayed in some areas. In addition, the spatial and spectral resolutions of satellite sensor data also play a vital role in controlling the level of detail at which land cover can be classified. In Iraq, previous phenological classification [39; 195] produced low accuracy in discriminating between wheat and barley due to the large spatial and spectral resolution of the input data. Due to the relatively small field size with respect to MODIS pixels (250 m), it was challenging to discriminate between the two crop types [39; 195]. In contrast, greater accuracy was achieved in Iran with higher spatial resolution data [196].  
Figure 3 depicts the differences in spatial coverage between gridded Landsat (30 m) and MODIS (250 m) data over fine spatial resolution ArcGIS basemap imagery for some agricultural lands in (a) North-East Aleppo, Syria, (b) South-East Mosul, Iraq and (c) South Sanaa, Yemen. The figure shows clearly that the individual MODIS pixels cover several agricultural fields (Figure 3a-c), which may be used to plant different crops. However, Landsat could be more representative of the small agricultural parcels. Even if the same crop is planted in several fields covered by a MODIS pixel, within-pixel variability in crop  
-phenotype timing may exist if different agricultural management practices are applied. The mismatch in spatial resolution between remotely sensed data and the small agricultural field sizes makes it challenging to derive crop phenology, identify crop types and estimate crop yield accurately.