Remote Sensing and Cropping Practices: A Review

Abstract

For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment

“…To date, the majority of research on cropland classification was done using multiparametric SAR data [10]. This includes mostly polarimetric and multitemporal SAR data [8,[11][12][13][14][15][16][17][18][19][20][21][22], as well as multi-frequency SAR and fusion of satellite optical and SAR data [7,23].…”

The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

“…There is a long history in remote sensing of mapping agricultural characteristics. For example, at regional and global scales, satellite data have been used to map the extent of croplands (Waldner et al, 2016), crop management practices (Bégu et al, 2018), biomass and yield Jain et al, 2016), crop phenology (Duncan et al, 2015), and crop stress (Kannan et al, 2017;Paliwal et al, 2019). Recent advancements in remote sensing, including cloud computing, the increased use of machine learning, and finer spatial, temporal, and spectral resolution data have only increased what is possible to map over the last decade (Ma et al, 2019).…”

Acquiring Plant Features with Optical Sensing Devices in an Organic Strip-Cropping System

“…Regardless of the type of supporting structure, a perception system to register the current application area is key, here referring to the state of the (individual) plants, while not excluding possible expansion to soil characteristics or any other parameter of interest. As summarized in References [14,15] there are many (visual) ways of plant detection being investigated. In this paper, the optical sensing is performed using a Light Detection and Ranging sensor, also known as lidar, following References [16][17][18], among others.…”