Retrieval of soil salinity from Sentinel-2 multispectral imagery

Taghadosi, Mohammad Mahdi; Hasanlou, Mahdi; Eftekhari, Kamran

doi:10.1080/22797254.2019.1571870

Cited by 74 publications

(28 citation statements)

References 44 publications

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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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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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“…The new generation Sentinel 2A satellite, equipped with a Multi-Spectral Instrument (MSI), launched in 2015, offers freely available optical imageries with high spatial (up to 10 m) and spectral resolution (13 spectral bands), and with short revisit cycles (5 days). Recent studies have highlighted the great potential of this Sentinel 2A satellite imagery for soil salinity mapping [13][14][15][16], compared to Landsat 8 OLI [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Mapping of Soil Salinity and its Impact on Paddy Lands in Jaffna Peninsula, Sri Lanka

Gopalakrishnan

Kumar

2020

Sustainability

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Soil salinity is a major threat to land productivity, water resources and agriculture in coastal areas and arid and semi-arid regions of the world. This has a significantly negative effect on the land and causes desertification. Monitoring salt accumulation in the soil is crucial for the prevention of land degradation in such environments. This study attempted to estimate and map soil salinity in Jaffna Peninsula, a semi-arid region of Sri Lanka. A Partial Least Squares Regression (PLSR) model was constructed using Sentinel 2A satellite imagery and field-measured soil electrical conductivity (EC) values. The results showed that satisfactory prediction of the soil salinity could be made based on the PLSR model coupled with Sentinel 2A satellite imagery (R2 = 0.69, RMSE = 0.4830). Overall, 32.8% of the land and 45% of paddy lands in Jaffna Peninsula are affected by salt. The findings of this study indicate that PLSR is suitable for the soil salinity mapping, especially in semi-arid regions like Jaffna Peninsula. The results underpin the importance of building adaptive capacity and implementing suitable preventive strategies for sustainable land and agricultural management.

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“… Davis, Wang & Dow (2019) and Gorji et al (2020) also discovered that Sentinel-2 had great potential for SSC inversion. Taghadosi, Hasanlou & Eftekhari (2019) established two models (multiple linear regression and support vector regression) using Sentinel-2 images, which had good performance in SSC inversion in the unvegetated areas. Wang et al (2019) created multiple spectral indices based on Sentinel-2 data and developed an RF-PLSR model to estimate SSC.…”

Section: Introductionmentioning

confidence: 99%

Estimating salt content of vegetated soil at different depths with Sentinel-2 data

Chen

Qiu

Zhang

et al. 2020

PeerJ

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The accurate and timely monitoring of the soil salt content (SSC) at different depths is the prerequisite for the solution to salinization in the arid and semiarid areas. Sentinel-2 has demonstrated significant superiority in SSC inversion for its higher temporal, spatial and spectral resolution, but previous research on SSC inversion with Sentinel-2 mainly focused on the unvegetated surface soil. Based on Sentinel-2 data, this study aimed to build four machine learning models at five depths (0∼20 cm, 20∼40 cm, 40∼60 cm, 0∼40 cm, and 0∼60 cm) in the vegetated area, and evaluate the sensitivity of Sentinel-2 to SSC at different depths and the inversion capability of the models. Firstly, 117 soil samples were collected from Jiefangzha Irrigation Area (JIA) in Hetao Irrigation District (HID), Inner Mongolia, China during August, 2019. Then a set of independent variables (IVs, including 12 bands and 32 spectral indices) were obtained based on the Sentinel-2 data (released by the European Space Agency), and the full subset selection was used to select the optimal combination of IVs at five depths. Finally, four machine learning algorithms, back propagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM) and random forest (RF), were used to build inversion models at each depth. The model performance was assessed using adjusted coefficient of determination (R2adj), root mean square error (RMSE) and mean absolute error (MAE). The results indicated that 20∼40 cm was the optimal depth for SSC inversion. All the models at this depth demonstrated a good fitting (R2adj≈ 0.6) and a good control of the inversion errors (RMSE < 0.16%, MAE < 0.12%). At the depths of 40∼60 cm and 0∼20 cm the inversion performance showed a slight and a great decrease respectively. The sensitivity of Sentinel-2 to SSC at different depths was as follows: 20∼40 cm > 40∼60 cm > 0∼40 cm > 0∼60 cm > 0∼20 cm. All four machine learning models demonstrated good inversion performance (R2adj > 0.46). RF was the best model with high fitting and inversion accuracy. Its R2adj at five depths were between 0.5 to 0.68. The SSC inversion capabilities of all the four models were as follows: RF model > ELM model > SVM model > BPNN model. This study can provide a reference for soil salinization monitoring in large vegetated area.

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Retrieval of soil salinity from Sentinel-2 multispectral imagery

Cited by 74 publications

References 44 publications

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

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

Modeling and Mapping of Soil Salinity and its Impact on Paddy Lands in Jaffna Peninsula, Sri Lanka

Estimating salt content of vegetated soil at different depths with Sentinel-2 data

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