Land use and climate change effects on soil organic carbon in North and Northeast China

Zhou, Yin; Hartemink, Alfred E.; Shi, Zhou; Liang, Zhao; Lu, Yanli

doi:10.1016/j.scitotenv.2018.08.016

Cited by 160 publications

(88 citation statements)

References 48 publications

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“…RMSE explained the difference between the samples and the model predictions. Generally, a model that performed well would have high CC and RPD values, and a low RMSE value [28,54].…”

Section: Soil Salinity Prediction Using Rfmentioning

confidence: 99%

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

et al. 2019

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Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.

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“…RMSE explained the difference between the samples and the model predictions. Generally, a model that performed well would have high CC and RPD values, and a low RMSE value [28,54].…”

Section: Soil Salinity Prediction Using Rfmentioning

confidence: 99%

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

et al. 2019

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“…The amount of importance in assessing the uncertainty is almost as great as that in making a prediction map [58], uncertainty map helped researchers to identify the source of the uncertainty and propose solutions [59,60]. To obtain a very reliable prediction, we can control the uncertainty of soil TN map from two aspects.…”

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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“…This problem is relevant for most countries worldwide (Chappell et al, 2019;Wunder, Bodle, 2019). Land degradation is closely related to the possible consequences of climate changes observed nowadays (Bonfante et al, 2019;Netsvetov et al, 2018;Zhou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Changes in Physical and Chemical Properties of Calcic Chernozem Affected by Robinia pseudoacacia and Quercus robur Plantings

Gorban

Huslystyi

Kotovych

et al. 2020

Ekológia (Bratislava)

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Gorban V., Huslystyi A., Kotovych O., Yakovenko V.: Changes in physical and chemical properties of Calcic chernozem affected by Robinia pseudoacacia and Quercus robur plantings. EkológiaGrowth of forest plantations on soils causes changes in their properties. These changes, their behavior, and magnitude depend on the original soil characteristics and also on the effect of forest plantations being grown. In the steppe zone of Ukraine, Robinia pseudoacacia L. and Quercus robur L. are the woody species most widely used in planting of forest plantations on chernozem soil. Chernozem soil formed exclusively under zonal steppe vegetation and chernozem soil under Robinia pseudoacacia and Quercus robur plantations were studied in this work to analyze the changes in soil properties caused by growth of these tree species. Dry aggregate size distribution, density, particle density, total porosity, organic carbon content, cation exchange capacity, pH values, hydrolytic soil acidity and dry residue, and the available nitrogen, phosphorus, and potassium content were analyzed. The studies found that Robinia pseudoacacia and Quercus robur plantations contribute to an increase in the share of aggregates 2-1 mm in size, as well as formation of aggregate fraction >10 mm, which are completely absent in the Calcic chernozem developed under the steppe vegetation. An increase in the density and particle density, as well as a decrease in the total porosity values were observed under the influence of forest stands studied. This is more common with chernozem under Q. robur plantation. It was found that the carbon percentage decreased in chernozem under the influence of Robinia pseudoacacia growth (on average, 0.4% by a meter-deep layer), but under Quercus robur planting it increased (on average 0.3% by meter-deep layer). Effect of Robinia pseudoacacia plantings on chernozem was also manifested by a decrease in cation exchange capacity (on average, 11 cmol/100 g by a meter-deep layer). The growth of R. pseudoacacia and Quercus robur plantations results in decrease of pH values (0.2 by a meter-deep layer) and increase of hydrolytic soil acidity and dry residue in chernozem water extract. Effect of Robinia pseudoacacia planting leads to a decrease in carbon, nitrogen, and phosphorus content in chernozem. The change in chernozem properties under the influence of Quercus robur plantation is reflected in accumulation of these nutrients. Growth of Robinia pseudoacacia and Quercus robur plantations leads to a decrease in potassium reserves in chernozem, which may indicate its active uptake by these woody species. In general, Q. robur planting is characterized by a large positive effect on the physical and chemical properties of chernozem than Robinia pseudoacacia planting. The findings obtained serve as a ground for making a recommendation for growing Quercus robur plantations under climate conditions of the steppe zone of Ukraine in order to improve the zonal chernozems' state and fertility.

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Land use and climate change effects on soil organic carbon in North and Northeast China

Cited by 160 publications

References 48 publications

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

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

Changes in Physical and Chemical Properties of Calcic Chernozem Affected by Robinia pseudoacacia and Quercus robur Plantings

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