Estimation of soil texture using Sentinel-2 multispectral imaging data: An ensemble modeling approach

Swain, Smita Rani; Chakraborty, Poulamee; Panigrahi, Niranjan; Vasava, Hitesh B.; Reddy, Nagarjuna N.; Roy, Sourav; Majeed, Israr; Das, Bhabani S.

doi:10.1016/j.still.2021.105134

Cited by 11 publications

(5 citation statements)

References 45 publications

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“…The findings demonstrated that clay fraction could be predicted from Sentinel-2 images for mapping larger geographic areas [10]. The study by Swain et al [16] was based on 295 soil samples, with an ensemble modeling approach of the surface soil particle-size fractions in the western catchment of Chilika lagoon using the ten bands of Sentinel-2 images, Band2, Band3, Band4, Band5, Band6, Band7, Band8, Band8a, Band11, and Band12, and the results were comparable to hyperspectral data observations measured in the laboratory. Gomez et al [14] used 130 soil surface samples from a cultivated region in India to map soil texture classification through the ten bands of Sentinel-2 images, Band2, Band3, Band4, Band5, Band6, Band7, Band8, Band8a, Band11, and Band12, and demonstrated that Sentinel-2 images provided the potential for inputs of environmental process and crop management modeling.…”

Section: Introductionmentioning

confidence: 76%

“…In the spectral indices constructed by Sentinel-2 images, SATVI (composed of Short-Wave InfraRed and Near-InfraRed) revealed the highest correlation with sand, silt, and clay fractions. Sand, silt, and clay fractions in the study area were correlated to the amount of visible, Near-InfraRed and Short-Wave InfraRed absorbed and/or reflected, according to the high correlations of Band11, Band12, and SATVI [16].…”

Section: Correlation Of Covariatesmentioning

confidence: 94%

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Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery

Zheng,

Wang,

et al. 2023

Remote Sensing

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Soil texture is a key physical property that affects the soil’s ability to retain moisture and nutrients. As a result, it is of extreme importance to conduct remote sensing monitoring of soil texture. Songnen Plain is located in the black soil belt of Northeast China. The development of satellite imagery in remote sensing technology enables the rapid monitoring of large areas. This study aimed to map the surface soil texture of cultivated land in Songnen Plain using Sentinel-2 images and Random Forest (RF) algorithm. We conducted this study by collecting 354 topsoil (0–20 cm) samples in Songnen Plain and evaluating the effectiveness of the bands and spectral indices of Sentinel-2 images and RF algorithm in predicting soil texture (sand, silt, and clay fractions). The results demonstrated that the 16 covariates were moderately and highly correlated with soil texture. And, Band11 of Sentinel-2 images could be used as the corresponding band of soil texture. For sand fraction, the Sentinel-2 images and RF algorithm’s Coefficient of Determination (R2) and Root Mean Square Error (RMSE) were 0.77 and 10.48%, respectively, and for silt fraction, they were 0.75 and 9.38%. Sand fraction decreased from southwest to northeast in Songnen Plain, while silt and clay fractions increased. We found that the Songnen Plain was affected by water erosion and wind erosion, in the northeast and southwest, respectively, providing reference for the implementation of Conservation Tillage policies. The outcome of the study can provide reference for future soil texture mapping with a high resolution.

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Section: Introductionmentioning

confidence: 76%

Section: Correlation Of Covariatesmentioning

confidence: 94%

Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery

Zheng,

Wang,

et al. 2023

Remote Sensing

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“…According to Swain et al [37], PLSR, SVR, and RF models were utilized to predict the silt, clay, and sand contents based on both S2 spectra and laboratory measurements. The results described that the ensemble modeling approach estimated the soil content with high R 2 values of sand, silt, and clay (0.62, 0.54, 0.54).…”

Section: Introductionmentioning

confidence: 99%

Identification of Soil Types and Salinity Using MODIS Terra Data and Machine Learning Techniques in Multiple Regions of Pakistan

Haq,

Shahbaz,

Asif

et al. 2023

Sensors

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Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R2 = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.

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“…It is an important index in the field of cultivated land quality and in the evaluation of crop suitability 4 . The estimation and mapping of the spatial distribution of soil texture can not only enrich and improve the soil digital database, but also provide a basis and data support for research on the spatial distribution of soil attributes and agricultural production planning 5 . Therefore, precision agricultural management is urgently required for the high-resolution and high-precision quantification and monitoring of the spatial and temporal distribution characteristics of soil texture at the field scale.…”

Section: Introductionmentioning

confidence: 99%

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

Song,

Gao,

Song

et al. 2023

Sci Rep

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Soil texture is one of the important physical and natural properties of soil. Much of the current research focuses on soil texture monitoring using non-imaging geophysical spectrometers. However there are fewer studies utilizing unmanned aerial vehicle (UAV) hyperspectral data for soil texture monitoring. UAV mounted hyperspectral cameras can be used for quickly and accurately obtaining high-resolution spatial information of soil texture. A foundation has been laid for the realization of rapid soil texture surveys using unmanned airborne hyperspectral data without field sampling. This study selected three typical farmland areas in Huangshui Basin of Qinghai as the study area, and a total of 296 soil samples were collected. Data calibration of UAV spectra using laboratory spectra and field in situ spectra to explore the feasibility of applying laboratory soil texture models directly to field conditions. This results show that UAV hyperspectral imagery combined with machine learning can obtain a set of ideal processing methods. The pre-processing of the spectral data can obtain high accuracy of soil texture estimation and good mapping effect. The results of this study can provide effective technical support and decision-making assistance for future agricultural land planning on the Tibetan Plateau. The main innovation of this study is to establish a set of processing procedures and methods applicable to UAV hyperspectral imagery to provide data reference for monitoring soil texture in agricultural fields on the Tibetan Plateau.

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Estimation of soil texture using Sentinel-2 multispectral imaging data: An ensemble modeling approach

Cited by 11 publications

References 45 publications

Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery

Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery

Identification of Soil Types and Salinity Using MODIS Terra Data and Machine Learning Techniques in Multiple Regions of Pakistan

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

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