High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling

Liang, Zhao; Chen, Songchao; Yang, Yuanyuan; Zhou, Yue; Shi, Zhou

doi:10.1016/j.scitotenv.2019.05.332

Cited by 81 publications

(44 citation statements)

References 39 publications

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“…Therefore, efforts to produce appropriate predictors to explain the spatial distribution of soil attributes (at detail and generalization) increases the accuracy of the models. Various covariates (e.g., climate, terrain attributes and RS data) representing soil state factors have been widely used in statistical models to predict soil texture, bulk density, organic carbon, nutrients (Ca, Mg, K, Na, N, P), available water capacity, pH and CEC [4,17,22,[34][35][36][37].…”

Section: Preparing Soil Covariatesmentioning

confidence: 99%

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

et al. 2019

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The Midwest region in Brazil has the largest and most recent agricultural frontier in the country where there is no currently detailed soil information to support the agricultural intensification. Producing large-extent digital soil maps demands a huge volume of data and high computing capacity. This paper proposed mapping surface and subsurface key soil attributes with 30 m-resolution in a large area of Midwest Brazil. These soil maps at multiple depth increments will provide adequate information to guide land use throughout the region. The study area comprises about 851,000 km2 in the Cerrado biome (savannah) in the Brazilian Midwest. We used soil data from 7908 sites of the Brazilian Soil Spectral Library and 231 of the Free Brazilian Repository for Open Soil Data. We selected nine key soil attributes for mapping and aggregated them into three depth intervals: 0–20, 20–60 and 60–100 cm. A total of 33 soil predictors were prepared using Google Earth Engine (GEE), such as climate and geologic features with 1 km-resolution, terrain and two new covariates with 30 m-resolution, based on satellite measurements of the topsoil reflectance and the seasonal variability in vegetation spectra. The scorpan model was adopted for mapping of soil variables using random forest regression (RF). We used the model-based optimization by tuning RF hyperparameters and calculated the scaled permutation importance of covariates in R software. Our results were promising, with a satisfactory model performance for physical and chemical attributes at all depth intervals. Elevation, climate and topsoil reflectance were the most important covariates in predicting sand, clay and silt. In general, for predicting soil chemical attributes, climatic variables, elevation and vegetation reflectance provided to be the most important of predictive components, while for organic matter it was a combination of climatic dynamics and reflectance bands from vegetation and topsoil. The multiple depth maps showed that soil attributes largely varied across the study area, from clayey to sandy, suggesting that less than 44% of the studied soils had good natural fertility. We concluded that key soil attributes from multiple depth increments can be mapped using Earth observations data and machine learning methods with good performance.

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Section: Preparing Soil Covariatesmentioning

confidence: 99%

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

et al. 2019

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“…With an adequate dataset, we can capture spatial variance explained by environmental covariates more precisely. When establishing a new database, areas with low certainty should be emphasized and should be a priority for further database investment [65,66].…”

Section: Uncertainty In Soil Tn Predictionmentioning

confidence: 99%

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

et al. 2019

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Accurate estimates of the spatial distribution of total nitrogen (TN) in soil are fundamental for soil quality assessment, decision making in land management, and global nitrogen cycle modeling. In China, current maps are limited to individual regions or are of coarse resolution. In this study, we compiled a new 90-m resolution map of soil TN in China by the weighted summation of random forest and extreme gradient boosting. After harmonizing soil data from 4022 soil profiles into a fixed soil depth (0–20 cm) by equal area spline, 18 environmental covariates were employed to characterize the spatial pattern of soil TN in topsoil across China. The accuracy assessments from independent validation data showed that the weighted model averaging gave the best predictions with an acceptable R2 (0.41). The prediction map showed that high-value areas of soil TN were mainly distributed in the eastern Tibetan Plateau, central Qilian Mountains and the north of the Greater Khingan Range. Climate factors had a considerable influence on the variation of the soil TN, and land-use types played a pivotal part in each climate zone. This high-resolution and high-quality soil TN data set in China can be very useful for future inventories of soil nitrogen, assessments of soil nutrient status, and management of arable land.

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“…We observed that with increasing elevation, this is true for forest pixels (Figure 8). The effect of elevation on SOC and STN has been examined in a number of studies (e.g., Kieft 1998;Kunkel et al 2011;Minasny et al, 2013;Adhikari et al 2013;Chen et al 2015;Minasny et al, 2017;Liang et al, 2019). Wang et al (2017) reported that SOC and STN significantly increased with elevation.…”

Section: Predicted Distribution Of Soc and Stn Contentmentioning

confidence: 99%

Peer Review #1 of "An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China (v0.1)"

2020

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Soil organic carbon (SOC) and soil total nitrogen (STN) are major soil indicators for soil quality and fertility. Accurate mapping SOC and STN in soils would help both managed and natural soils and ecosystem management. This study developed an improved similaritybased approach (ISA) to predicting and mapping topsoil (0-20 cm soil depth) SOC and STN in a coastal region of northeastern China. Six environmental variables including elevation, slope gradient, topographic wetness index, the mean annual temperature, the mean annual temperature, and normalized difference vegetation index were used as predictors. Soil survey data in 2012 was designed based on the clustering of the study area into six climatic vegetation landscape units. In each landscape unit, 20-25 sampling points were determined at different landform positions considering local climate, soil type, elevation and other environmental factors, and finally 126 sampling points were obtained. Soil sampling from the depth of 0-20 cm were used for model prediction and validation. The ISA model performance was compared with the geographically weighted regression (GWR), regression kriging (RK), boosted regression trees (BRT) considering mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R 2), and maximum relative difference (RD) indices. We found that the ISA method performed best with the highest R 2 and lowest MAE, RMSE compared to GWR, RK, and BRT methods. The ISA method could explain 76 % and 83 % of the total SOC and STN variability, respectively, 12-40% higher than other models in the study area. Elevation had the largest influence on SOC and STN distribution. We conclude that the developed ISA model is robust and effective in mapping SOC and STN, particularly in the areas with complex

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High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling

Cited by 81 publications

References 39 publications

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

Peer Review #1 of "An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China (v0.1)"

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