Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging

Gholizadeh, Asa; Žížala, Daniel; Saberioon, Mohammadmehdi; Borůvka, Luboš

doi:10.1016/j.rse.2018.09.015

Cited by 328 publications

(190 citation statements)

References 93 publications

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“…More recently, Gholizadeh et al [76] proved the advantages of Sentinel-2 to derive high-quality information on variations in SOC comparing to airborne sensors, especially where SOC levels were relatively high. In that regard, they applied a simple SVM model to train prediction models over the spectral signature of Sentinel-2 and a set of spectral indices.…”

Section: Spacebornementioning

confidence: 99%

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

et al. 2019

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Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end.

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Section: Spacebornementioning

confidence: 99%

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

et al. 2019

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“…The vegetation indices [57][58][59] included in this study included: NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), SR (Simple Ratio Index), DVI (Difference Vegetation Index), and EVI (Enhanced Vegetation Index). Additionally, the texture variables based on the gray-level co-occurrence matrix (GLCM) [60,61] included Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular second moment, and Correlation [27,58,59,[62][63][64] with different windows (3 × 3, 5 × 5, 7 × 7, 9 × 9, and 11 × 11) [27,65,66]. There were 251 total variables derived with the details that are shown in Table 2.…”

Section: Processing Landsat Times Series Productsmentioning

confidence: 99%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

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Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R2 from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices.

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“…Some authors already used Sentinel-2 data for soil variable prediction and mapping [5][6][7][8], and obtained encouraging results, especially for the SOC content in the plough layer. However, some issues still need to be addressed to improve a soil product based on Sentinel-2 data.…”

Section: Introductionmentioning

confidence: 99%

Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects

Castaldi

Chabrillat

Don³

et al. 2019

Remote Sensing

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Soil organic carbon (SOC) loss is one of the main causes of soil degradation in croplands. Thus, spatial and temporal monitoring of SOC is extremely important, both from the environmental and economic perspective. In this regard, the high temporal, spatial, and spectral resolution of the Sentinel-2 data can be exploited for monitoring SOC contents in the topsoil of croplands. In this study, we aim to test the effect of the threshold for a spectral index linked to soil moisture and crop residues on the performance of SOC prediction models using the Multi-Spectral Instrument (MSI) Sentinel-2 and the European Land Use/cover Area frame Statistical survey (LUCAS) topsoil database. The LUCAS spectral data resampled according to MSI/Sentinel-2 bands, which were used to build SOC prediction models combining pairs of the bands. The SOC models were applied to a Sentinel-2 image acquired in North-Eastern Germany after removing the pixels characterized by clouds and green vegetation. Then, we tested different thresholds of the Normalized Burn Ratio 2 (NBR2) index in order to mask moist soil pixels and those with dry vegetation and crop residues. The model accuracy was tested on an independent validation database and the best ratio of performance to deviation (RPD) was obtained using the average between bands B6 and B5 (Red-Edge Carbon Index: RE-CI) (RPD: 4.4) and between B4 and B5 (Red-Red-Edge Carbon Index: RRE-CI) (RPD: 2.9) for a very low NBR2 threshold (0.05). Employing a higher NBR2 tolerance (higher NBR2 values), the mapped area increases to the detriment of the validation accuracy. The proposed approach allowed us to accurately map SOC over a large area exploiting the LUCAS spectral library and, thus, avoid a new ad hoc field campaign. Moreover, the threshold for selecting the bare soil pixels can be tuned, according to the goal of the survey. The quality of the SOC map for each tolerance level can be judged based on the figures of merit of the model.

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Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging

Cited by 328 publications

References 93 publications

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects

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