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Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect
Open AccessArticle

Soil Organic Carbon Mapping from Remote Sensing: The Effect of Crop Residues

Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
Walloon Agricultural Research Centre (CRA-W), Farming Systems, Territories and Information Technology Unit, 5030 Gembloux, Belgium
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 1913;
Received: 11 May 2020 / Revised: 2 June 2020 / Accepted: 11 June 2020 / Published: 12 June 2020
Since the onset of agriculture, soils have lost their organic carbon to such an extent that the soil functions of many croplands are threatened. Hence, there is a strong demand for mapping and monitoring critical soil properties and in particular soil organic carbon (SOC). Pilot studies have demonstrated the potential for remote sensing techniques for SOC mapping in croplands. It has, however, been shown that the assessment of SOC may be hampered by the condition of the soil surface. While growing vegetation can be readily detected by means of the well-known Normalized Difference Vegetation Index (NDVI), the distinction between bare soil and crop residues is expressed in the shortwave infrared region (SWIR), which is only covered by two broad bands in Landsat or Sentinel-2 imagery. Here we tested the effect of thresholds for the Cellulose Absorption Index (CAI), on the performance of SOC prediction models for cropland soils. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 240 km2 in the Belgian Loam Belt were used together with a local soil dataset. We used the partial least square regression (PLSR) model to estimate the SOC content based on 104 georeferenced calibration samples (NDVI < 0.26), firstly without setting a CAI threshold, and obtained a satisfactory result (coefficient of determination (R2) = 0.49, Ratio of Performance to Deviation (RPD) = 1.4 and Root Mean Square Error (RMSE) = 2.13 g kgC−1 for cross-validation). However, a cross comparison of the estimated SOC values to grid-based measurements of SOC content within three fields revealed a systematic overestimation for fields with high residue cover. We then tested different CAI thresholds in order to mask pixels with high residue cover. The best model was obtained for a CAI threshold of 0.75 (R2 = 0.59, RPD = 1.5 and RMSE = 1.75 g kgC−1 for cross-validation). These results reveal that the purity of the pixels needs to be assessed aforehand in order to produce reliable SOC maps. The Normalized Burn Ratio (NBR2) index based on the SWIR bands of the MSI Sentinel 2 sensor extracted from images collected nine days before the APEX flight campaign correlates well with the CAI index of the APEX imagery. However, the NBR2 index calculated from Sentinel 2 images under moist conditions is poorly correlated with residue cover. This can be explained by the sensitivity of the NBR2 index to both soil moisture and residues. View Full-Text
Keywords: soil organic carbon mapping; cellulose absorption index; crop residues; hyperspectral data; partial least squares regression soil organic carbon mapping; cellulose absorption index; crop residues; hyperspectral data; partial least squares regression
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MDPI and ACS Style

Dvorakova, K.; Shi, P.; Limbourg, Q.; van Wesemael, B. Soil Organic Carbon Mapping from Remote Sensing: The Effect of Crop Residues. Remote Sens. 2020, 12, 1913.

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