Mapping total soil nitrogen from a site in northeastern China

Wang, Shuai; Jin, Xinxin; Adhikari, Kabindra; Li, Wenwen; Yu, Miao; Bian, Zhenxing; Wang, Qiubing

doi:10.1016/j.catena.2018.03.023

Cited by 50 publications

(49 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an STN prediction study in Fuyang, Zhejiang Province, China, He et al [62] found that the RF and BRT models explained approximately 50% of the STN variability. In contrast, Wang et al [63] used the RF model to predict STN content in northeast China, obtaining R 2 values that were higher than this study that explained 69% of the STN variation. These different predictive performances may be due to differences in the type and quality of the ancillary data, the study area, and the number of field observations.…”

Section: Model Performancecontrasting

confidence: 91%

“…Previous studies have confirmed the spatial relationship between STN and vegetation [87,88]. For example, Wang et al [63] used the RF method to predict STN content in northeastern China, and found that areas with dense vegetation cover had higher STN. Similar findings were also reported in the STN mapping studies by Zhang et al [89] and Wang et al [90].…”

Section: Spatial Prediction Of Stn Contentmentioning

confidence: 88%

“…Xu et al [61] found that spectral reflectance is one of the main factors affecting STN variation. Wang et al [63] reported that optical remote sensing-derived variables were the most important environmental variables affecting STN variation compared to topographic and climate variables. However, this finding differed from our results, mainly due to the addition of multi-temporal Sentinel-1A images to map STN in this study.…”

Section: Importance Of Predictor Variablesmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Model Performancecontrasting

confidence: 91%

Section: Spatial Prediction Of Stn Contentmentioning

confidence: 88%

Section: Importance Of Predictor Variablesmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…Random Forest (RF) model is a non-parametric technique that has been successfully applied to soil properties prediction (Wiesmeier et al, 2011;Castro Franco et al, 2015;Hengl et al, 2015;Chagas et al, 2016;Yang et al, 2016;Dharumarajan;Hedge;Singh, 2017;Silva et al, 2017;Blanco et al, 2018;Wang et al, 2018a). The model combines a set of decision trees to improve the accuracy of prediction of a given environmental variable, where each tree is generated by bootstrap samples (random sampling with substitution), leaving one-third of training samples, called Out-of-Bag (OOB) data, for using in the model's performance evaluation (Breiman, 2001;Liaw;Wiener, 2002).…”

Section: Machine Learning Algorithms For Ko Predictionmentioning

confidence: 99%

“…Ko affects water flows and other hydrological and biogeochemical processes, including questions about how human-induced changes may affect the ecological balance. However, characterization of Ko covering extensive areas is expensive, long time consuming and complex, especially due to its high spatial variability, as reported in several studies (Bevington et al, 2016;Gwenzi et al, 2011;Jačka et al, 2016Jačka et al, , 2018Kurnianto et al, 2019;Moustafa, 2000;Papanicolaou et al, 2015;Rienzner;Gandolfi, 2014;Ronayne;Houghton;Stednick, 2012;Wang et al, 2018a;Zimmermann;Elsenbeer, 2008). This high spatial variability of Ko occurs due to different extrinsic and intrinsic factors, including geomorphic surface, weather, land-use and management, soil structure, soil granulometric distribution and bulk density (Sobieraj et al, 2002;Pachepsky et al, 2008;Zimmermann et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Land-use influence on the soil hydrology: An approach in upper Grande River basin, Southeast Brazil

et al. 2019

View full text Add to dashboard Cite

Brazil is a large country that depends on the hydroelectricity generation hydropower dams. The Upper Grande River Basin (UGRB) is one of the most important Brazilian hydrological regions in terms of water availability and electric energy production. Therefore, studies of water availability are indispensable for a better and more successful decision making in water resources management in the region. This study objective to approach the land-use influence on the soil hydrology in the Upper Grande River Basin, a strategic headwater basin of southeastern Brazil. This study uses hydrological indicators (baseflow/runoff (BF/R) and overland flow/runoff (OF/R)) extracted from eight watersheds, varying the size and localization in the region, to support the results found. Soil saturated hydraulic conductivity (Ko) was determined in situ using a constant flow permeameter, totaling 224 sampled points. Five machine learning algorithms were compared in their performance to predict Ko (Random Forest, Support Vector Machine, Gradient Boosting, Linear Regression, Regularization) using terrain attributes as covariates. The tested methods for predicting Ko resulted in a relatively low coefficient of determination (R2) due to the high spatial variability of this soil hydrologic attribute. The hydrological indicator BF/R was sensitive to land-use changes in the watersheds. The greatest Ko values were associated with native forest and the least values area associated with pasture and rupestrian field.

show abstract

Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map

Parsaie

Firouzi

Mousavi

et al. 2021

Environ Monit Assess

View full text Add to dashboard Cite

Mapping total soil nitrogen from a site in northeastern China

Cited by 50 publications

References 59 publications

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

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

Land-use influence on the soil hydrology: An approach in upper Grande River basin, Southeast Brazil

Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map

Contact Info

Product

Resources

About