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Open AccessArticle

Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping

1
LISAH, University Montpellier, IRD, INRA, Montpellier SupAgro, 34060 Montpellier, France
2
ICAR-National Bureau of Soil Survey and Land Use Planning, Hebbal, Bangalore 440033, India
3
TETIS, Irstea, AgroParisTech, CIRAD, CNRS, Université Montpellier, 34000 Montpellier, France
4
Géosciences Environnement Toulouse, Université Paul-Sabatier, IRD, CNRS, 31400 Toulouse, France
5
Indo-French Cell for Water Sciences, IRD, Indian Institute of Science, Bangalore 560012, India
6
Indian Institute of Science, Civil Engineering Department, Bangalore 560012, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 565; https://doi.org/10.3390/rs11050565
Received: 30 January 2019 / Revised: 25 February 2019 / Accepted: 28 February 2019 / Published: 8 March 2019
(This article belongs to the Section Biogeosciences Remote Sensing)
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. View Full-Text
Keywords: time-series; Sentinel-2; soil texture; classification; uncertainty; Simpson index; bootstrap time-series; Sentinel-2; soil texture; classification; uncertainty; Simpson index; bootstrap
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MDPI and ACS Style

Gomez, C.; Dharumarajan, S.; Féret, J.-B.; Lagacherie, P.; Ruiz, L.; Sekhar, M. Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping. Remote Sens. 2019, 11, 565.

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