# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products
### Peer Review Literature
#### 3.1.1 Satellite-Data-Driven Agriculture
It is also worth mentioning that image quality acquired from multispectral, hyperspectral, and RGB cameras depends on weather conditions, making the Synthetic Aperture Radar (SAR) images an essential tools for RS in agriculture. However, backscatter noise for vegetation dynamics often results in difficulties in image interpretation. Deep Learning frameworks for object detection have been proposed to overcome this problem [16]. Ref. [17] suggests an improved MCNN-Seq model to forecast optical time series using SAR data even when optical data are not available. They used Sentinel-1 SAR and Sentinel-2 optical images collected over a period of two months each. However, the coarse spatial resolution and low temporal resolution of satellite imagery make it difficult to obtain multitemporal, large-volume, and high-quality datasets, which impedes the application of Deep Learning in low-resolution satellite-borne remote sensing, making them insufficient for small-scaled detailed observations. Thus, grape yield prediction and vineyard monitoring are important to maintain quality while not impeding the supply chain, providing farmers with the ability to better manage their fields and obtain higher income. Yield prediction maps also allow them to view spatial variations across their field and determine the best harvesting time and marketing strategy, which are greatly affected by the grape's growth stages. Different methods are employed to estimate yield; nevertheless, because of constraints related to time and labor, large-scale estimation is problematic. Machine learning and satellite remote sensing can provide quick and accurate assessments over wide areas for less money and in less time. Specifically, ref. [18] attempts to monitor vine growth in a Protected Design of Origin (PDO) zone using freely available and high-temporal-resolution Sentinel-2 imagery. They selected 27 vineyards for their study and calculated vegetation indices (i.e., NDVI, EVI) for each one. The results indicate a high negative correlation with the elevation topographic parameter during the flowering stage of the vines. The performed ANOVA between the vegetation indices of each subregion also showed that they have statistically significant differences, with most of them being able to detect the fruit at the flowering and harvest stage but only NDVI and Red-Edge band Vis during the veraison period (the onset of the ripening of the grapes). These data proved to be useful for monitoring on a regional scale, since the S2 imagery captured all vineyards at the same time and under the same atmospheric conditions. Machine learning models for yield prediction using vegetation index time series are proposed in [19]. They used Landsat 8 surface reflectance products during 2017-2019 and built a regression analysis model to map NDVI, LAI, and NDWI. The exponential smoothing methods and moving averages used on the satellite images detected different stages of growth. The indices were highly correlated at the time that canopy expansion reached its maximum. The ANN approach indicated the superiority of NDVI, which had the highest accuracy across all years, with R equal to 0.94, 0.95, and 0.92, respectively. The models were evaluated with ground-truth yield datasets. The results of this research showcase that Landsat vegetation indicators can be used to calculate site-specific vineyard management and forecast yields. Ref. [20] examines the capability of Sentinel-2 to infer field-size dryland wheat yields, as well as how using a simulated crop water stress index could improve predictability (SI). The S2-derived VIs observed from 103 study fields over the period between the 2016 and 2017 cropping seasons explained approximately 70% of the variance, showing fairly high accuracy in predicting yield. The best model with RMSE = 0.54 t/ha featured a combination of OSAVI, CI, and SI. For the administration of cotton agriculture and international trade, accurate and prompt distribution monitoring is mandatory. Ref.