## 3 Remote Sensing Challenges and Solutions to Increase Agricultural Productivity
### Remote Sensing Solutions and Future Applications  
#### 3.2.1 New RS Techniques and Data to Increase the Accuracy of Crop Area Estimation  
To overcome the issue of low classification accuracy, previous approaches used fusion approaches to combine the fine spatial resolution of data from the Landsat series of sensors with the high temporal frequency of data from coarse resolution sensors such as MODIS ([PERSON] et al., 2006; [PERSON] et al., 2013), to provide fused datasets suitable for application to ASA regions. Such products could have added value for a wide range of applications which need both fine spatial and temporal resolutions, such as land cover classification and forecasting crop yield. One approach, called the spatial and temporal adaptive reflectance fusion model (STARFM) is based on a spatial relationship between Landsat and MODIS spectral reflectance. The MODIS spectral reflectance can be downscaled to the spatial resolution of Landsat obtained at the same dates ([PERSON] et al., 2017). An extension of STARFM was  
Figure 3: Three fine spatial resolution ArcGIS basemap images (<1 m) of agricultural areas selected from the Middle East to illustrate the agricultural detail that is lost within MODIS and Landsat images. Grid outlines representing 250 m pixels from MODIS and 30 m pixels from Landsat are shown overlaid on the base images. (**a**) North-East Aleppo, Syria, (**b**) South-East Mosul, Iraq and (**c**) South Sanaa, Yemen. (Service layer credits: Esri, DigitalGlobe, GeoEye, Earthstar, Geographics, CNES/Airbus DS, USDA, USGS, AiroGRID, IGN, and the GIS User community). developed to increase the accuracy of predicting the surface reflectance of heterogeneous landscapes (ESTARFM) [198]. Linear mixture models were also used to downscale MERIS data to a Landsat-like spatial resolution and results indicated that vegetation dynamics, discrimination of crop types, phenology and rapid land cover types could be monitored effectively [199,200]. [PERSON] et al. [201] developed a new fusion approach called STAIR and claimed that based on intensive experiments the approach not only captures useful texture patterns but also predicts reflectance accurately in the generated images, with a significant improvement over the classic STARFM algorithm. In addition, new products such as Sentinel data provided by the European Space Agency (ESA) have become accessible to the RS community, which increases the spatial and spectral resolutions available for applications such as complex land cover/land use mapping, forest monitoring and change detection. Data extracted from Sentinel imagery, such as from Sentinel-2 and 3, can be used in isolation or combined, or both can be combined with MODIS and Landsat 8 through data fusion techniques. Recently, although agricultural field sizes are generally small in Sub-Saharan Africa, with a fine spatial and temporal resolution achieved by integrating Sentinel-2 data it was possible to disentangle agricultural land into the cropped and fallowed classes [44]. Such discrimination can help capture crop fallow rotation which can provide key information on how cropland expansion and intensification affects environmental parameters such as crop yield, soil fertility and local livelihoods in low-income regions such as the Sahel [44]. Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) was also developed recently, producing Landsat 8 consistent and atmospherically corrected surface reflectance, but at the spatial scale and temporal frequency of the CubeSat observations [202]. In their paper, application of this approach to an agricultural dryland system in Saudi Arabia was demonstrated in which CubeSat-based reproduction of Landsat 8 consistent VNIR data was achieved with an overall relative mean absolute deviation of 1.6% or better, even when the Landsat 8 and CubeSat acquisitions were temporally displaced by >32 days. [PERSON] and [PERSON] [203] also developed a new fusion method using Sentinel datasets in which more accurate results were produced compared to the existing STARFM.