Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain

Rosero‐Vlasova, Olga A.; Vlassova, Lidia; Pérez‐Cabello, Fernando; Montorio, Raquel; Nadal‐Romero, Estela

doi:10.1117/1.jrs.12.042803

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…Among modelled properties, SOM calibrations showed the highest predictive capability accounting on average for 86% (CCR‐SD) and 82% (PLSR‐full and PLSR‐MUT) of the variance in the calibration and 87% (PLSR‐full and CCR‐SD) and 79% (PLSR‐MUT) in the validation data sets. The CCR‐SD and PLSR‐full models for SOM developed in this study are more accurate ( R 2 0.05–0.07 higher and the average RPIQ above 3) than the SOM models we built previously for soil samples from wildfire burns from the same area using PLSR with the step‐down variable selection algorithm (Rosero‐Vlasova, Vlassova, Pérez‐Cabello, Montorio, & Nadal‐Romero, ), which is probably due to the different modelling algorithm and larger calibration data set used in this study. The superior RPIQ of the SOM models (~3 or above) developed with CCR‐SD and PLSR‐full is another indicator of their high quality.…”

Section: Resultssupporting

confidence: 90%

Soil organic matter and texture estimation from visible–near infrared–shortwave infrared spectra in areas of land cover changes using correlated component regression

et al. 2019

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Land use changes due to natural and human‐related factors, which include wildfires and crop abandonment, are among the most important drivers of soil degradation and demand regular monitoring. Proximal soil sensing in visible–near infrared–shortwave infrared spectral regions could offer a solution. However, to become operational, optimal combination of data and technique has to be defined. Thus, the purpose of this study was (a) to predict the soil organic matter (SOM) content and soil texture in areas of wildfire burns and crop abandonment in Aragón Province, Northern Spain, from their laboratory reflectance spectra using novel correlated components regression with a step‐down variable selection algorithm (CCR‐SD) and (b) to compare the CCR‐SD and the partial least squares regression (PLSR) methods. The results obtained by the tested methods were similar. CCR‐SD models showed high predictive capacity with coefficients of determination (R2) in the range of 0.80–0.86 and 0.70–0.87 for calibration and validation data sets, respectively, and the highest R2 value was attained in the SOM estimation. Moreover, the CCR‐SD models stand out for the superior accuracy–parsimony relationship: the number of predictors varied from 16 (silt models) to 49 (SOM models). On average, the CCR‐SD calibrations needed less than a half of the predictors employed in PLSR models. This research confirmed that CCR‐SD can be used for monitoring SOM content and texture of soils from visible–near infrared–shortwave infrared spectra in the study area and, probably, in other areas of land use/land cover change and that CCR‐SD can create highly parsimonious models that achieve results comparable with the commonly used PLSR method.

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Section: Resultssupporting

confidence: 90%

Soil organic matter and texture estimation from visible–near infrared–shortwave infrared spectra in areas of land cover changes using correlated component regression

et al. 2019

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“…Unlike high-resolution topographic information, the availability of spatial layers of soil conditions is still limited (Fang et al, 2016). Yet, spaceborne multispectral and imaging spectroscopy instruments have a high potential for mapping topsoil carbon (Peón et al, 2017) and organic matter content as well as soil physical properties (Rosero-Vlasova et al, 2018). These novel possibilities should be tested in SDMs in the future.…”

Section: Topographymentioning

confidence: 99%

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

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Bolliger

et al. 2020

Remote Sensing of Environment

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“…SOC prediction models were found to be overall better when formulated using the visible B4, B5, near infrared B8A, and two SWIR bands (B11, B12) [67]. Furthermore, cropland soil degradation with classification methods have also been studied [68,69].…”

Section: Sentinel-2 For Precision Agriculturementioning

confidence: 99%

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

et al. 2020

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The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

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Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain

Cited by 12 publications

References 36 publications

Soil organic matter and texture estimation from visible–near infrared–shortwave infrared spectra in areas of land cover changes using correlated component regression

Soil organic matter and texture estimation from visible–near infrared–shortwave infrared spectra in areas of land cover changes using correlated component regression

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

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