## 3 Remote Sensing Challenges and Solutions to Increase Agricultural Productivity
### Data Issues
#### 3.1.2 Atmospheric and Reflectance Biases in Earth Observation Data in ASA Ecosystems  
In general, careful choice of appropriate satellite sensor data and remotely sensed techniques should be made for monitoring agricultural productivity in ASA regions. [PERSON] and [PERSON] [171] stated that in ASA regions, the frequent occurrence of dust storms needs to be considered and treated carefully in image pre-processing. The dust storm phenomenon can affect image quality by altering spectral reflectance posing major challenges to RS applications in agriculture. Since the information recorded by remote sensors represents both atmospheric and surface interactions, accurate translation of sensor radiance to estimates of surface radiance (and reflectance) by subtracting atmospheric noise is crucial for the effective use of remote sensing data in this context [172]. Interaction of the reflected signal with atmospheric particles such as aerosols can affect measurements. Therefore, careful parameterisation of aerosol characteristics over time and space is needed to derive surface reflectance accurately, for estimating vegetation biophysical properties [173]. Inaccurate estimation of vegetation biophysical properties could deleteriously affect RS-based prediction models [174].  
For a given atmospheric condition, several atmospheric radiative transfer scripts have been developed to model gaseous absorption, and molecule and aerosol scattering [175; 176]. [PERSON] and [PERSON] [172] combined satellite and Aerosol Robotic Network (AERONET) data to parameterise aerosol properties and atmospheric state parameters, and the NDVI was computed using the corrected reflectance data with the smallest errors (3-8% mean absolute deviation). The approximate aerosol retrieval algorithm errors of different sensors such as MODIS, MERIS and MISR can range between \(\pm\)0.05 + 0.2T550 [177; 178; 179]. The quality of the retrieval process depends mainly on adopting suitable techniques for making use of multi-angular and multi-sensor data streams [180]. Several factors can impact retrievals such as bright desert conditions [181] and high aerosol loadings [172]. Therefore, attention needs to be paid to selecting the most appropriate aerosol model [176]: a fixed aerosol model should be avoided [182].  
The above early pre-processing of satellite sensor images is particularly important in agricultural applications since several vegetation indices are applied commonly for monitoring purposes, in which multi-temporal images are used [183]. In ASA ecosystems, generally the spatial density of vegetation is low [184; 185] and this presents another challenge to applying RS in these areas. The sparse coverage of vegetation can reduce the contribution of the area-averaged reflectance of a pixel [183]. In addition, due to the lack of organic matter content, particularly in desert areas, soils in these regions tend to be bright and heterogenous mineralogically [184]. All these factors can increase the possibility of excluding the spectral contribution of vegetation within individual pixels [186; 187; 188], particularly if an adequate spatial and spectral resolution was not selected. In addition, vegetation in dry environments is different to its humid counterparts, mainly due to a stronger red edge [184; 189]. Moreover, rapid phenological change due to climatic conditions can have a significant impact on the overall brightness and spatial and temporal interspecies spectral variability [184; 189]. Therefore, achieving accurate RS-based estimates in these regions requires a good understanding of regional constraints and the selection of appropriate satellite sensor data types and RS techniques that suit the condition.