{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:51:12Z","timestamp":1784199072516,"version":"3.55.0"},"reference-count":65,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cUnveiling the List of Hanging\u201d Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["20222-051244"],"award-info":[{"award-number":["20222-051244"]}]},{"name":"\u201cUnveiling the List of Hanging\u201d Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["32101301"],"award-info":[{"award-number":["32101301"]}]},{"name":"\u201cUnveiling the List of Hanging\u201d Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["32371725"],"award-info":[{"award-number":["32371725"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20222-051244"],"award-info":[{"award-number":["20222-051244"]}],"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":["32101301"],"award-info":[{"award-number":["32101301"]}],"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":["32371725"],"award-info":[{"award-number":["32371725"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology\u2019s robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14\u201345% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping.<\/jats:p>","DOI":"10.3390\/rs16152731","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T06:17:44Z","timestamp":1721974664000},"page":"2731","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Leveraging Remote Sensing-Derived Dynamic Crop Growth Information for Improved Soil Property Prediction in Farmlands"],"prefix":"10.3390","volume":"16","author":[{"given":"Jing","family":"Geng","sequence":"first","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuyuan","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9711-9811","authenticated-orcid":false,"given":"Junwei","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajun","family":"Fang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"The Zhongke-Ji\u2019an Institute for Eco-Environmental Sciences, Ji\u2019an 343000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shulan","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e00398","DOI":"10.1016\/j.geodrs.2021.e00398","article-title":"Soils and sustainable development goals of the United Nations: An International Union of Soil Sciences perspective","volume":"25","author":"Lal","year":"2021","journal-title":"Geoderma Reg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s003740050407","article-title":"Soil attributes as predictors of crop production under standardized conditions","volume":"27","author":"Stenberg","year":"1998","journal-title":"Biol. 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