# 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  
There has been tremendous progress in remote sensing, enabling substantial spatial resolution, temporal frequency, and spectral availability of satellites. Experts in the agricultural field are committed to the evolution of traditional agriculture into precision agriculture (PA) driven by data. Deep Learning (DL) has been employed extensively in tasks regarding identification, classification, detection, quantification, and prediction. At the field scale, it can be used for predicting crop yield, as well as for land cover mapping and crop identification at the land scale. Neural Network architectures, such as Convolutional Neural Networks (CNNs), and more recently techniques including Transfer Learning (TL), make up a powerful framework that allows real-time crop production prediction from RGB or spectral images. We further note that Transfer Learning is important, since it can be applied in order to transfer information gained from tasks where there is an abundance of large labeled datasets to other tasks where training data are scarce [6]. When the domains share many similarities, TL offers a quick and affordable solution to training data scarcity. The ability of CNN-based frameworks to classify multispectral remote sensing data from SAT4 and SAT6 datasets has been evaluated in [7]. The proposed architectures achieved a classification accuracy of more than 99%, indicating the potential of deep layer architectures to design operational remote sensing classification tools back in 2016. Since then, the rapid advances in network architectures, and the computational power making the use of more deep layers feasible along with benchmark framework improvements, have managed to eliminate the effects of overfitting (i.e., the inability of a model to predict 'unseen' data) and to increase classification accuracy. One such example is Residual Neural Networks. Ref. [8] uses WorldView-3 and PlanetScope imagery to derive raw multispectral and temporal satellite data to predict corn and soybean yield. Specifically, 4 and 25 sets of WorldView-3 and PlanetScope cloud-free images were acquired and fed into a 2D and 3D model that explained roughly 90% variance in field-scale yield. The 3D CNN performed better accuracy-wise on PlanetScope data than the 2D CNN due to its ability to digest temporal features extracted from said data. However, most freely available satellite data originate from satellites with medium spatial and spectral resolution and may not be suitable for several scenarios of classification and monitoring [9; 10]. In [11], a deep neural network model is developed to predict the output of wheat crops using MODIS data with spatial resolutions of 250 m, 500 m, and 1000 m, significantly coarser than Worldview-3. The proposed CNN-LSTM model works without the need for dimensionality reduction or feature extraction due to its ability to 'digest' raw satellite images  
The paper by [12] focuses on quantifying the increase in precision that could be achieved by using NDVI derived from higher-resolution images, specifically masked for cropland. This study, based on the NDVI data of three different resolutions (250 m, 500 m, 1 km) in US states for over 11 years, monitors soybeans, corn, spring, and winter wheat. The developed regression models for each crop type showed improved R-squared scores as the spatial resolution increased. Other studies [13; 14; 15] compare freely accessible satellite data (i.e., Sentinel-2, Landsat-8) with paid ones (i.e., Worldview) in different applications. Ref. [13] compares and evaluates Sentinel-2 satellite vegetation and spectral data with those of Dove nanosatellites, such asPlanetscope, in the detection and mapping of Striga hernothica weed. While Ps performed 5% better with a 92% accuracy in detecting the weed, the study demonstrates the ability of S2 to provide near-real-time field-level detection, a task that was quite difficult with previous multispectral sensors.