## 3 Remote Sensing Challenges and Solutions to Increase Agricultural Productivity
### Remote Sensing Solutions and Future Applications
#### 3.2.2 New Freely Available RS Data to Improve Monitoring Agricultural Crop Status  
It has been noted that the majority of efforts to map and monitor crop diseases and pests are conducted in small plots, or at least over small areas [204, 205, 206]. This is because such monitoring often relies on commercial satellite sensor images with the necessary spectral and spatial/temporal resolution to capture the detailed variation of interest. Thus, upscaling or adopting these approaches for other areas is challenging due to the large cost constraint. In addition, in other research where freely available coarse spatial resolution images were used, low accuracies were produced. Therefore, future research must investigate the utility of freely available data, such as from Landsat 8 and Sentinel, which provide adequate spatial and temporal resolution images across the world. For example, a new spectral index for detecting wheat yellow rust using Sentinel 2 was developed by [PERSON] et al. [207], and the overall identification accuracy for the index was 84.1%. It is also important to stress that ecologists, botanists and remote sensing scientists should come together and agree on standardised variables that are critical for mapping and monitoring crop disease and pests using remote sensing data. [PERSON] et al. [208] indicated that Sentinel 2-based VIs can be used for the detection of twister disease in the field since it gives greater discrimination and high accuracies. 2.3 Utilising the Improved RS Sensing Capabilities to Increase the Accuracy of Crop Yield Forecasting  
Over the past decades, most research in RS on crop yield modelling and forecasting has emphasised the use of greenness and biomass as the basis for prediction [209,210]. Since the environment in ASA regions is highly variable, an alternative way to increase the accuracy of yield forecasts based on RS is to use vegetation biochemical and biophysical parameters as a surrogate for crop yield. Chlorophyll is a key biochemical parameter, which has a large correlation with crop productivity [211]. Many studies revealed the close relationship between chlorophyll content and Gross Primary Yield (GPP) [211; 212]. Thus, compared to leaf area index (LAI) or biomass, the chlorophyll content might be expected to be more associated with crop yield. The chlorophyll content of vegetation, which is a function of the biochemical variables of chlorophyll concentration and the biophysical variable of LAI, can be surrogated by the MERIS Terrestrial Chlorophyll Index (MTCI) [213]. [PERSON] and [PERSON] [214] assessed the potential of an MTCI-based model for crop yield forecasting compared to the NDVI in Henan Province, China from 2003 to 2011. Their results revealed several advantages of the MTCI-based model compared to the NDVI-based model, such as (i) larger significant correlation coefficient and smaller error; (ii) crop yield could be forecasted 30 days earlier than using the NDVI-based model. Although the results were not compared to other VIs, a significant correlation between MTCI and crop yield was found at regional scales for the state of South Dakota, USA [213]. Thus, considering its consistently large correlation with final crop yield, satellite-derived chlorophyll content should be further adapted for crop yield forecasting in ASA regions. Until now, we have focused on reflected light in the solar spectrum as the main source of information about vegetation conditions. However, there is an extra source of information in the spectral range of the optical and near-infrared, providing information about vegetation productivity. This source of information is associated with the emission of fluorescence from plant leaf chlorophyll; re-emitted energy because this part of the spectrum cannot be utilised in carbon fixation [215]. In addition, observational evidence in many studies revealed that chlorophyll fluorescence provides information independent of reflectance-based spectral VIs [216].