Clay content prediction using spectra data collected from the ground to space platforms in a smallholder tropical area

Bellinaso, Henrique; Silvero, Nélida Elizabet Quiñonez; Ruiz, Luis Fernando Chimelo; Amorim, Merilyn Taynara Accorsi; Rosin, Nícolas Augusto; Mendes, Wanderson de Sousa; Sousa, Gabriel Pimenta Barbosa de; Sepulveda, Leno Márcio Araujo; Queiroz, Louise Gunter de; Nanni, Marcos Rafael; Demattê, José A. M.

doi:10.1016/j.geoderma.2021.115116

Cited by 18 publications

(12 citation statements)

References 115 publications

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“…The model performance obtained from the S2A_S image was superior to that obtained by [6], who also used the ten bands of the Sentinel-2A (S2A) and a PLSR model. Similar results were obtained by [8] using Sentinel-2 MSI and Landsat-8 OLI images for clay content prediction, with R 2 values of 0.68 and 0.62, respectively. Moreover, [29] also found similar performance using the MLR model and nine bands of the ASTER image, while [55] obtained a high prediction accuracy of soil clay content using a mean spectral reflectance from bare soil pixels along a Landsat-TM time series.…”

Section: Clay Predictions Depending On the Multispectral Sensorsupporting

confidence: 81%

“…The percentage of bare soil pixels over both fusion images (i.e., Fusion_SI and Fusion_SB) was equal to 34.6%, which corresponds to the common bare soil area between all derivedmultispectral images. Based on the spectral characteristics of multispectral satellite images (i.e., TM, OLI, AST, and S2A images) (Figure 4), the reflectance values rose as the clay content dropped [8]. The TM and OLI spectra appeared very similar (Figure 4a,b).…”

Section: Bare Soil Images and Spectral Pattern Descriptionsmentioning

confidence: 97%

“…The extensive use of remote sensing satellite images may considerably increase the accuracy of DSM outputs. Indeed, remote sensing satellite images directly estimate some fundamental soil surface properties, such as soil texture (sand, clay, and silt content), carbon content, and soil salinity, e.g., [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, two common approaches have been widely applied to relate soil spectra extracted from remote sensing imagery to soil properties, including spectral indices, e.g., [9][10][11][12] and visible, near-infrared, and short wave infrared (VNIR/SWIR, 400-2500 nm) spectral bands (i.e., entire spectra) approach, e.g., [6][7][8]13]. For instance, spectral datasets based on multi-and hyper-spectral images have been used by researchers to quantify the minerals and/or physicochemical properties of the soil.…”

Section: Introductionmentioning

confidence: 99%

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Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Gasmi¹,

Gomez

Chehbouni

et al. 2022

Remote Sensing

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Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.

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Section: Clay Predictions Depending On the Multispectral Sensorsupporting

confidence: 81%

Section: Bare Soil Images and Spectral Pattern Descriptionsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Gasmi¹,

Gomez

Chehbouni

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

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“…The two most common satellite sensors used in soil mapping applications are National Aeronautics and Space Administration (NASA) Landsat archive and Sentinel-2 from the European Copernicus Space component (23% of the studies for Landsat and 33% for Sentinel). It is noteworthy, that recent high spatial resolution sensors (<3 m), such as Planet Imagery [52], are very important contributors to SOC estimation, while their contribution is low for clay estimation [53]. Žížala et al [54] also indicated that very high-resolution spatial sensors mounted on UAS (<1 m) present moderate accuracy of prediction for organic carbon estimation.…”

Section: The Spectral Dimensionmentioning

confidence: 99%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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Assessing the utility of Munsell soil color in building and evaluating spectral models for soil clay content prediction

Dharumarajan,

Gomez,

Lalitha

et al. 2024

Soil Science Soc of Amer J

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The present study examined how the use of soil color can help build and evaluate clay content prediction models from laboratory visible and near infrared spectroscopic data. This study was based on a regional database containing 449 soil samples collected over Karnataka state in India, which has been divided into red soils (240 samples) and black soils (209 samples) based on their Munsell soil color. Partial least squares regression models were calibrated and validated from both the regional datasets and subsets stratified as red and black soils. In addition, a random forest model was used to classify the validation soil samples into black and red classes to evaluate models’ performance. First, while the clay content predicted by the regression model built from regional data was evaluated as correct at regional scale (R2val of 0.75), this model was evaluated as more accurate over black (R2val of 0.8) than red (R2val of 0.63) soil samples. Second, the regression models built from subsets stratified per soil color provided different performances than the regression model built from the regional data, both at the regional scale and soil color scale. In conclusion, this study demonstrated that (1) predictions are highly dependent on calibration data, (2) the interpretation of prediction performances relies heavily on validation data, and (3) pedological knowledge, such as soil color, can be effectively employed as an encouraging covariate in both the construction and evaluation of regression models.

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Clay content prediction using spectra data collected from the ground to space platforms in a smallholder tropical area

Cited by 18 publications

References 115 publications

Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Assessing the utility of Munsell soil color in building and evaluating spectral models for soil clay content prediction

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