# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 4 Sensors
### Satellite
This is an excellent example of the use of blended technology, where the authors trained a convolutional neural network using high-resolution images from RPA and then derived the NDVI to interpret the low- or moderate-resolution satellite images of vineyard vigor maps. Among other uses of this RS platform, one is the construction of historical maps of crops, as these play a significant role in crop prediction estimation and related simulations. Recently, a study established the relationship between MODIS-derived NDVI and the grain yields of wheat, barley, and all cereals for 20 European countries (including Austria, Belgium, Denmark, France, Germany, Ireland, Sweden, the United Kingdom, etc.). After analyzing the data from 2010 to 2018, a higher percentage of cereals was reported; 35% for arable land. The efficacy of yield prediction in relation to NDVI and cereals was reported as trustworthy over a good time period of 4 months [100]. More studies of this nature on a global scale are needed to strengthen the agriculture of today. Implementation of existing technological tools is also very important. For instance, Google Earth Engine (GEE) offers diverse services in this domain for the agricultural industry. GEE facilitates product downloading of various satellites imagery (Landsat, Sentinels, MODIS, etc.) along with cloud-based management, by granting access either through an academic email or simply a standard Gmail account to their servers. Processing very large geospatial datasets often makes the process slow and difficult. In this regard, GEE, a cloud-based platform, facilitates access to high-performance computing resources without any cost. Systematic data products and interactive applications can be developed based on GEE once an algorithm is developed--thereby reducing the complexities of programming and application development [45]. A stepwise illustration for product downloads from GEE is presented in Figure 2, although the final exported product can be different from this illustration, based on the needs and preferred format. A practical example of benefiting from free services GEE is presented as the construction of vegetation indices (VIs) maps for olive grove trees located at Guadahortuna (Granada, Spain) (Figure 3). Sentinel-2 images, between July 2020 and February 2021, were accessed through GEE, at a resolution of 10 \(\times\) 10, and were processed to generate NDVI (as indicated in the below formula) vector tiles, followed by their export into GeoJSON format. The GeoJSON format is user friendly and generates data in the form of a table where each pixel represents a file with its NDVI value, which is quite handy to work with. Likewise, another practical example of this nature is the data acquisition, via GEE, for a parcel of vineyard crop situated at Reggio Emilia (Emilia-Romagna, Italy) (Figure 4). In both of these examples, NDVI was calculated using the following formula:  
\[\text{NDVI}=\frac{\text{NIR}-\text{VIS}}{\text{NIR}+\text{VIS}}=[\text{ Sentinel2}\text{Bands}]\frac{B08-B04}{B08+B04}\]  
Figure 3: Sentinel-2 images accessed through Google Earth Engine (GEE). (**A**) Area of interest, (**B**) RGB field composition, (**C**) NDVI raster tile, (**D**) NDVI vector tile, (**E**) RGB field composition, (**F**) NDVI raster tile (Images facilitated by Graniot). Figure 2: A stepwise illustration (1 to 7) of processing and downloading satellite imagery products from Google Earth Engine (GEE). where NIR is the near-infrared band and the VIS is the visible red. Bands 8 and 4 of Sentinel-2 were used given their desired spatial resolution of 10 m as well as their spectral range (e.g., bands 8 and 4 measure NIR and red spectral bands, respectively) to calculate the NDVI. These serve as an example of free data acquisition and processing; the future promises enormous possibilities. The only need is to make use of such freely available resources for academic, as well as real-world, applications.