Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

Ceddia, Marcos Bacis; Gomes, Andréa S.; Vasques, Gustavo M.; Pinheiro, Érika Flávia Machado

doi:10.3390/rs9020124

Cited by 23 publications

(13 citation statements)

References 38 publications

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“…; Ceddia et al. ). This suggests that Landsat images could be used as an environmental layer in species distribution modelling and other applications that require in situ data to be interpolated across large areas.…”

Section: Introductionmentioning

confidence: 98%

A Landsat composite covering all Amazonia for applications in ecology and conservation

doninck

Tuomisto

2018

Remote Sens Ecol Conserv

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Studies at small spatial extents have shown that local floristic and edaphic patterns within the hyper-diverse Amazonian forests can be identified at a high thematic resolution using Landsat imagery. This suggests that Landsat images have the potential to indicate ecologically relevant environmental and biotic variation in the forests also at the extent of the entire basin. However, the full potential of Landsat data for these purposes has not yet been exploited in ecological and biodiversity research or in conservation applications. This is largely because the artifactual noise that is introduced by atmospheric and directional effects into multi-scene composite images can swamp the subtle spectral differences between different types of primary forest. Here, we present a new Landsat TM/ETM+ image composite for the entire Amazon biome that largely overcomes these problems. It is based on more than 16 000 individual image acquisitions from the 10-year period 2000-2009. The images were individually processed to directionally and topographically normalized surface reflectance and combined into 2.5 degree tiles using the medoid compositing criterion. Visual inspection showed that the resulting image composite is radiometrically clearly more consistent than other currently available Landsat composites. We tested the ecological relevance of the new Landsat composite by comparing its reflectance values with edaphic properties measured in more than 300 field sampling localities spread across 2000 km of Amazonia. We found a strong correlation between observed and predicted concentration of exchangeable base cations in the surface soil, which indicates that the compositing approach has succeeded in removing most of the artifactual noise. The Landsat composite image should be of great value for a multitude of applications in ecology, biodiversity research and conservation planning that require environmental data layers combining detailed spatial resolution, basin-wide coverage and high radiometric accuracy.

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Section: Introductionmentioning

confidence: 98%

A Landsat composite covering all Amazonia for applications in ecology and conservation

doninck

Tuomisto

2018

Remote Sens Ecol Conserv

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“…The terrain controls the flow of solute, water, and sediment, which in turn affects the development of soil and the spatial distribution of soil properties [63]. Although terrain factors have a significant correlation with SOC content [64], the prediction ability of the model can be significantly improved by adding vegetation index and backscatter coefficient compared with those using only terrain variables [65]. On the one hand, the vegetation features have enough heterogeneity, and there is a strong interaction between vegetation features and soil properties [66].…”

Section: Variable Importancementioning

confidence: 99%

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Wang

Zhang

et al. 2021

Remote Sensing

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Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.

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“…Our results not only indicate the importance of remote sensing data to predict STN but also emphasize the need to incorporate SAR images. Radar can provide spectral information beyond vegetation cover and soil surface [12], and backscatter signals from SAR images are used to retrieve target properties, such as forest above-ground biomass, soil texture, soil moisture, and salinity. Many studies have shown that information from SAR data, such as the backscatter coefficient, can detect vegetation [64] and soil moisture [65].…”

Section: Importance Of Predictor Variablesmentioning

confidence: 99%

“…For example, Bartsch et al [11] explored the feasibility of C-band ENVISAT ASAR data in soil organic carbon (SOC) mapping and found that the SOC in the study area can be quantified by SAR images. Ceddia et al [12] used optical and L-band ALOS PALSAR data to conduct SOC prediction studies in central Amazon, and found that the backscattering coefficient of SAR data can improve the prediction accuracy. A similar study was also reported by Ma et al [13], who used SAR (Sentinel-1) and optical (Landsat 7) images to map soil properties in eastern China.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

et al. 2019

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Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.

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Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

Cited by 23 publications

References 38 publications

A Landsat composite covering all Amazonia for applications in ecology and conservation

A Landsat composite covering all Amazonia for applications in ecology and conservation

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

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