Abstract
Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.
Highlights
The clean-up of parcels based on crop types, geometries, and surface areas resulted in the retention of 66,665 parcels distributed among 173 planned crops for 2020
The corresponding number of parcels covered by each image is shown in Figure 6b, along with the remaining parcels after an normalised difference vegetation index (NDVI) threshold of 0.3
Powerful approaches to detect bare soil have been already developed for Landsat, Sentinel-2, and a combination of sensors [11,54,60], and have successfully mapped large areas to analyse soil characteristics
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Agriculture is constantly in need of timely information that is key to various land management decisions. Soil mapping of temporal and spatial variability is essential for sustainable management of cropland and for the assessment and remediation of impacts of climate change [1,2]. In many parts of the world, it can be challenging to manage agricultural parcels without appropriate updates of digital agricultural databases [3,4]
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