Abstract
One of the most challenging aspects of obtaining detailed and accurate land-use and land-cover (LULC) maps is the availability of representative field data for training and validation. In this manuscript, we evaluate the use of the Eurostat Land Use and Coverage Area frame Survey (LUCAS) 2018 data to generate a detailed LULC map with 19 crop type classes and two broad categories for woodland and shrubland, and grassland. The field data were used in combination with Copernicus Sentinel-2 (S2) satellite data covering Europe. First, spatially and temporally consistent S2 image composites of (1) spectral reflectances, (2) a selection of spectral indices, and (3) several bio-geophysical indicators were created for the year 2018. From the large number of features, the most important were selected for classification using two machine-learning algorithms (support vector machine and random forest). Results indicated that the 19 crop type classes and the two broad categories could be classified with an overall accuracy (OA) of 77.6%, using independent data for validation. Our analysis of three methods to select optimum training data showed that by selecting the most spectrally different pixels for training data, the best OA could be achieved, and this already using only 11% of the total training data. Comparing our results to a similar study using Sentinel-1 (S1) data indicated that S2 can achieve slightly better results, although the spatial coverage was slightly reduced due to gaps in S2 data. Further analysis is ongoing to leverage synergies between optical and microwave data.
Highlights
Information on land use and land cover (LULC) is crucial for spatial modeling and monitoring of the global hydrological and carbon cycle, energy balance, and status of natural resources [1,2,3]
The at the point locations of the Land Use and Coverage Area frame Survey (LUCAS) data, followed by the support vector machine (SVM) and random forest (RF) hyperparamete main steps shown in Figure 1 included the extraction of S2 spectral and temporal features tuning, training, and assessment of data, the results
The most effective outcome was obtained with the ‘Dissimilar’ subset that reached an overall accuracy (OA) of 77.6%, compared to 77.8% when using all data for training
Summary
Information on land use and land cover (LULC) is crucial for spatial modeling and monitoring of the global hydrological and carbon cycle, energy balance, and status of natural resources [1,2,3]. A repeated, transparent and precise LULC monitoring is essential for addressing rapid changes in land use such as soil sealing and agricultural production [4]. The most needed are LULC maps that can display the dynamic land-use changes, such as in arable land. Satellite-based remote sensing is the most appropriate tool for LULC mapping [5] because of its global continuous and regular coverage and cost-efficiency [6]. The abundance of freely available remote-sensing data offers unprecedented opportunities to produce land cover, and land cover change maps over large-scale areas [7].
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