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Open AccessArticle

Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

1
Department of Biosystems Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran
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Department of GIS and RS, Faculty of Geography, University of Tabriz, Tabriz 5166616471, Iran
3
Dryland Agricultural Research Institute, Agricultural Research Education and Extension Organization (AREEO), Maragheh 5517643511, Iran
4
Department of Geoinformatics–Z_GIS, University of Salzburg, 5020 Salzburg, Austria
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2583; https://doi.org/10.3390/rs11212583
Received: 6 September 2019 / Revised: 29 October 2019 / Accepted: 30 October 2019 / Published: 4 November 2019
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques. View Full-Text
Keywords: conservation tillage; crop residue; pixel-based image classification; fuzzy object-based image analysis; Sentinel; tillage indices conservation tillage; crop residue; pixel-based image classification; fuzzy object-based image analysis; Sentinel; tillage indices
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MDPI and ACS Style

Najafi, P.; Navid, H.; Feizizadeh, B.; Eskandari, I.; Blaschke, T. Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue. Remote Sens. 2019, 11, 2583.

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