Spatio-temporal distribution of soil nitrogen in Poyang lake ecological economic zone (South-China)

Jiang, Yefeng; Rao, Lei; Sun, Kai; Han, Yi; Guo, Xiaomin

doi:10.1016/j.scitotenv.2018.01.087

Cited by 34 publications

(13 citation statements)

References 34 publications

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“…Incontrovertibly, there is an important need to make policy decisions about land-use planning that take account of the local conditions rather than applying experience mechanically. These N-rich soils should be used for activities that are suitable for their soil formation characteristics and environmental histories such as forestry and animal husbandry, and these N-lacking soils in arable land can be improved by applying N fertilizer rationally and returning straw [77].…”

Section: Effect Of Land Use On Soil Tn Contentsmentioning

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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Section: Effect Of Land Use On Soil Tn Contentsmentioning

confidence: 99%

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

et al. 2019

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“…The B NIR and NDVI were the key predictors for mapping the TN distribution owing to their abilities to effectively reflect the vegetation coverage and biomass in the study area [ 17 ]. Previous studies have confirmed that the TN content is strongly affected by land use patterns [ 35 ]. The NDVI, which was synthesized through a calculation involving B red and B NIR , can effectively represent the land use conditions, and thus, it demonstrates significant effects on variations in soil TN.…”

Section: Discussionmentioning

confidence: 77%

“…Li et al reported that the soil TN content increased by 27.27% from 1981 to 2012 in Renshou County, which is located in the hilly region of the mid-Sichuan Basin in southwestern China [ 18 ]. Jiang et al reported that the average soil TN content significantly increased by 0.08 g kg −1 in the farmland of Yangon County, which is located in the ecological economic area of Poyang Lake in China [ 35 ]. As mentioned above, the land use pattern factors became more important in terms of their influence on the soil TN distribution, and we found that the N fertilizer inputs and crop planting structure played critical roles on the amount of soil TN in the present study area; these results were supported by the results of previous studies [ 18 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Variability of Soil Nitrogen in Relation to Environmental Factors in a Low Hilly Region of Southeastern China

Zhu

Shahtahmassebi

et al. 2018

IJERPH

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Soil total nitrogen (TN) plays a major role in agriculture, geochemical cycles and terrestrial ecosystem functions. Knowledge regarding the TN distribution is crucial for the sustainable use of soil resources. This paper therefore aims to characterize the spatiotemporal distribution of soil TN and improve the current understanding of how various factors influence changes in TN. Natural characteristics and remote sensing (RS) variables were used in conjunction with the random forest (RF) model to map the TN distribution in a low hilly region of southeastern China in 1979, 2004 and 2014. The means and changes of TN in different geographic regions and farmland protection regions were also analyzed. The results showed that: (1) the TN showed an increasing trend in the early periods and exhibited a decreasing trend from 2004 to 2014; (2) the geographic and RS variables played more important roles in predicting TN distribution than did the other variables; and (3) changes in the fertilization and crop planting structure caused by soil testing and formulated fertilization techniques (STFFT—Soil Testing and Formulated Fertilization Techniques) as well as farmland protection policies influenced the spatiotemporal variability of TN. Evidently, more attention should be focused on improving the quality and soil fertility in the surrounding low mountainous areas.

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“…The visible blue and green bands are the important predictors for the distribution of STN, and their reflection bands are closely related to the vegetation cover [35,36]. Previous studies have shown that vegetation cover can affect nitrogen accumulation which is highly correlated with the STN content [79]. Besides, other environmental variables also show robust performances in the estimation of the STN content, such as precipitation and elevation, which can play important roles in the spatial pattern of the STN content at regional scales.…”

Section: Predictor Variables Importancementioning

confidence: 98%

Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

Xiao

Chen

et al. 2020

Sustainability

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Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

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Spatio-temporal distribution of soil nitrogen in Poyang lake ecological economic zone (South-China)

Cited by 34 publications

References 34 publications

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

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

Spatiotemporal Variability of Soil Nitrogen in Relation to Environmental Factors in a Low Hilly Region of Southeastern China

Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

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