Abstract
The Sentinel-2 mission of the European Space Agency (ESA) Copernicus program provides multispectral remote sensing data at decametric spatial resolution and high temporal resolution. The objective of this work is to evaluate the ability of Sentinel-2 time-series data to enable classification of an inherent biophysical property, in terms of accuracy and uncertainty estimation. The tested inherent biophysical property was the soil texture. Soil texture classification was performed on each individual Sentinel-2 image with a linear support vector machine. Two sources of uncertainty were studied: uncertainties due to the Sentinel-2 acquisition date and uncertainties due to the soil sample selection in the training dataset. The first uncertainty analysis was achieved by analyzing the diversity of classification results obtained from the time series of soil texture classifications, considering that the temporal resolution is akin to a repetition of spectral measurements. The second uncertainty analysis was achieved from each individual Sentinel-2 image, based on a bootstrapping procedure corresponding to 100 independent classifications obtained with different training data. The Simpson index was used to compute this diversity in the classification results. This work was carried out in an Indian cultivated region (84 km2, part of Berambadi catchment, in the Karnataka state). It used a time-series of six Sentinel-2 images acquired from February to April 2017 and 130 soil surface samples, collected over the study area and characterized in terms of texture. The classification analysis showed the following: (i) each single-date image analysis resulted in moderate performances for soil texture classification, and (ii) high confusion was obtained between neighboring textural classes, and low confusion was obtained between remote textural classes. The uncertainty analysis showed that (i) the classification of remote textural classes (clay and sandy loam) was more certain than classifications of intermediate classes (sandy clay and sandy clay loam), (ii) a final soil textural map can be produced depending on the allowed uncertainty, and iii) a higher level of allowed uncertainty leads to increased bare soil coverage. These results illustrate the potential of Sentinel-2 for providing input for modeling environmental processes and crop management.
Highlights
Soil provides key environmental functions such as food production, water storage and redistribution, pollutant filtering and carbon storage
The classification corresponding to the five other images resulted in lower performances than the classification obtained with the image acquired on the 24th of April 2017, with the lowest performances obtained when using the image acquired on the 4th of April 2017 (Figure 4)
Soil texture classification maps produced in our study showed good discrimination ability for (i) hillslopes and uplands, which correspond to coarse soil texture due to erosion processes, and (ii) valleys, which correspond to finer soil texture due to deposition processes
Summary
Soil provides key environmental functions such as food production, water storage and redistribution, pollutant filtering and carbon storage. The accurate characterization of soil properties over cultivated areas, including soil organic matter, soil texture or iron content, is essential for agricultural engineering work such as land consolidation, drainage management, soil erosion limitation and irrigation systems. This characterization provides valuable information to improve soil use and management actions over cultivated areas [1,2]. Environmental modeling increasingly contributes to decision making on soil management from local to global scales, but its operational use requires accurate and highly spatially referenced soil information as inputs. The most common way to obtain information on soil characterization is soil maps. The spatial resolutions of these maps are limited by the cost of collecting soil samples and measuring soil properties, and the spatial uncertainty of soil characterizations is usually unknown
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