Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs

By Clement Atzberger. Remote Sensing, 2013

943 citations recorded by the source corpus.

Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue "Advances in Remote Sensing of Agriculture". To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations.

Subfields: Atmospheric Science, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/029c5841b3fc010f55b604d7d1c541adb0e4a782

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Citations in the corpus

A review on the practice of big data analysis in agriculture (2017, methodology): “…agricultural problems by employing image analysis (Karmas, et al., 2014), (Teke, et al., 2013), using images originating from remote sensing (Atzberger, 2013), either from airborne (Gutiérrez, et al., 2008), (Barrett, et al., 2014), (Wardlow, et al., 2007) or satellites (Schnase, et al.,…”
Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images (2015, background): “It is interesting to note that some environmental issues (e.g., agricultural drought, irrigation management, and grassland) require high spatial (e.g., 30 m) and high temporal (e.g., weekly) resolutions 6 – 8 due to the rapid changes in these applications.”
Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt (2020, background): “Remote sensing and statistical models are descriptive (Atzberger, 2013; Lobell et al., 2015) and offer limited insight on belowground processes that are key to environmental performance.”
High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “PHENOLOGICAL PARAMETERS Crop phenology provides important information for simulating and monitoring crop growth and development [322], estimating crop yields [323], optimizing production management and decision-making [324], and analyzing crops’ response to climate change [289].”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, methodology): “For this reason, RS techniques are widely used in agriculture and agronomy [152].”
Big Data for Remote Sensing: Challenges and Opportunities (2016, background): “In this case, remote sensing has proved to be of great help for monitoring crops in a large area, such as mapping the bioenergy potential of maize crops [43] by incorporating the effects of climate and soils on yields [44].”