{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T21:59:48Z","timestamp":1776549588187,"version":"3.51.2"},"reference-count":99,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2020QD013"],"award-info":[{"award-number":["ZR2020QD013"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["42071419"],"award-info":[{"award-number":["42071419"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["CAAS-ZDRW202201"],"award-info":[{"award-number":["CAAS-ZDRW202201"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2020QD013"],"award-info":[{"award-number":["ZR2020QD013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071419"],"award-info":[{"award-number":["42071419"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CAAS-ZDRW202201"],"award-info":[{"award-number":["CAAS-ZDRW202201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Agricultural Science and Technology Innovation Program","award":["ZR2020QD013"],"award-info":[{"award-number":["ZR2020QD013"]}]},{"name":"Agricultural Science and Technology Innovation Program","award":["42071419"],"award-info":[{"award-number":["42071419"]}]},{"name":"Agricultural Science and Technology Innovation Program","award":["CAAS-ZDRW202201"],"award-info":[{"award-number":["CAAS-ZDRW202201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatial prediction of soil ammonia (NH3) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration method (tree-structured Parzen estimator\u2013machine learning\u2013ordinary kriging (TPE\u2013ML\u2013OK)) to predict spatial variability of soil NH3 from Sentinel-2 remote sensing image and air quality data. In TPE\u2013ML\u2013OK, we designed the TPE search algorithm, which encourages gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGB) models to pay more attention to the optimal hyperparameters\u2019 high-possibility range, and then the residual ordinary kriging model is used to further improve the prediction accuracy of soil NH3 flux. We found a weak linear correlation between soil NH3 flux and environmental variables using scatter matrix correlation analysis. The optimal hyperparameters from the TPE search algorithm existed in the densest iteration region, and the TPE\u2013XGB\u2013OK method exhibited the highest predicted accuracy (R2 = 85.97%) for soil NH3 flux in comparison with other models. The spatial mapping results based on TPE\u2013ML\u2013OK methods showed that the high fluxes of soil NH3 were concentrated in the central and northeast areas, which may be influenced by rivers or soil water. The analysis result of the SHapley additive explanation (SHAP) algorithm found that the variables with the highest contribution to soil NH3 were O3, SO2, PM10, CO, and NDWI. The above results demonstrate the powerful linear\u2013nonlinear interpretation ability between soil NH3 and environmental variables using the integration method, which can reduce the impact on agricultural nitrogen deposition and regional air quality.<\/jats:p>","DOI":"10.3390\/rs15174268","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T11:41:18Z","timestamp":1693482078000},"page":"4268","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Tree-Structured Parzan Estimator\u2013Machine Learning\u2013Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Yingqiang","family":"Song","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Mingzhu","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Zhao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Dexi","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Wenxu","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Miao","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Zhenqi","family":"Song","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Dengkuo","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Kaizhong","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Ziqi","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7760","DOI":"10.1073\/pnas.1814880116","article-title":"Ammonia emission control in China would mitigate haze pollution and nitrogen deposition, but worsen acid rain","volume":"116","author":"Liu","year":"2019","journal-title":"Proc. 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