# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 4 Sensors
### Satellite  
One of the oldest and main platforms of RS technology is the satellite. In 1957, the first ever satellite: Sputnik 1, weighing 183 pounds and the size of a basketball, was launched into space by the Soviet Union [94]. It has been over 40 years since satellite systems became operational, but a milestone in RS was achieved with the creation of normalized difference vegetation index (NDVI) maps, providing information about land cover, phenology, and vegetation activity, using advanced very high resolution radiometer (AVHRR) [35]. In terms of improved performance, considering geometric, spectral, and radiometric properties, the moderate resolution imaging spectroradiometer (MODIS) significantly helped with remote sensing. MODIS instruments laid the foundation of a new era in remote sensing after their launch in 1999, providing 1-km spatial resolution GPP products [35]. Numerous satellites had been launched in the past two decades with various spatial and temporal resolutions [95], which proved to be the basis of the 5 th technological revolution. Today, various privately-held and government-managed satellites harboring versatile sensors are an efficient foundation to RS, thereby providing huge sets of interpretable data and information. For example, Spain started its \"National Remote Sensing Plan (PNT)\" in 2004, aimed at data provisioning for various Spanish public administrations [96]. Although, it is important to bear in mind that this domain was envisaged with a little boost in 2008, when Landsat (US operated satellites) images were set freely accessible (under open license), and the launch of Sentinel 1A and Sentinel 2A satellites, in 2014 and 2015, respectively, under the Copernicus Program, further increased the momentum of RS. The Sentinel constellation is the part of Copernicus program that was launched by the European Commission and the European Space Agency (ESA). As more than 30 satellites provide data for this program, it is of great use to scientists to analyze the freely available data regarding agriculture. In addition, a separate database, titled \"Copernicus Academic\", of the Copernicus program also facilitates the training and better utilization of satellite data [97]. Similarly, the Landsat program, launched by the United States as a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), also offers free access to high-resolution images. The present needs are to wisely utilize these free accessible data to achieve ambitious agricultural goals. Furthermore, space-borne hyperspectral missions, such as _ZY1_-AHSI, PRISMA, DESIS, and GF5-AHSI, which launched recently, or are planned to be launched in upcoming years, such as SBG, CHIME, and EnMAP, are expected to augment agricultural monitoring in terms of vegetation functions and traits. It is imperative to address recurring limitations, such as soil background and canopy structure interference, signal variations in optical vegetation properties, and poor discrimination of functional traits due to lesser narrow spectral regions [2]. Recently, a cloud-free crop map service utilizing radar and optical data from Sentinel-1 and -2, respectively, was announced under a joint venture by two private companies i.e., VanderSat and BASF Digital Farming GmbH, thereby providing daily maps of field-scale crop biomass [98]. This advancement is a remarkable breakthrough in RS from satellites, as it has been a great challenge since the beginning of this technology. Recent advances in this technology are making it a fine and efficient technology through the use of machine learning. A study conducted by [PERSON] et al. [99] reported a novel strategy for refining the freely available photos from satellites to minimize the errors often encountered by their low or moderate resolutions.