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Ministry of Agriculture","award":["2021YFF0703904"],"award-info":[{"award-number":["2021YFF0703904"]}]},{"name":"the Special Funding for Modern Agricultural Technology Systems from the Chinese Ministry of Agriculture","award":["32130070"],"award-info":[{"award-number":["32130070"]}]},{"name":"the Special Funding for Modern Agricultural Technology Systems from the Chinese Ministry of Agriculture","award":["31971769"],"award-info":[{"award-number":["31971769"]}]},{"name":"the Special Funding for Modern Agricultural Technology Systems from the Chinese Ministry of Agriculture","award":["41771205"],"award-info":[{"award-number":["41771205"]}]},{"name":"the Special Funding for Modern Agricultural Technology Systems from the Chinese Ministry of Agriculture","award":["42101372"],"award-info":[{"award-number":["42101372"]}]},{"name":"the Special Funding for Modern Agricultural Technology Systems from the Chinese Ministry of Agriculture","award":["CARS-34"],"award-info":[{"award-number":["CARS-34"]}]},{"name":"the Special Funding for Modern Agricultural Technology Systems from the Chinese Ministry of Agriculture","award":["1610132021016"],"award-info":[{"award-number":["1610132021016"]}]},{"name":"the Fundamental Research Funds Central Non-profit Scientific Institution","award":["2021YFD1300500"],"award-info":[{"award-number":["2021YFD1300500"]}]},{"name":"the Fundamental Research Funds Central Non-profit Scientific Institution","award":["2021YFF0703904"],"award-info":[{"award-number":["2021YFF0703904"]}]},{"name":"the Fundamental Research Funds Central Non-profit Scientific Institution","award":["32130070"],"award-info":[{"award-number":["32130070"]}]},{"name":"the Fundamental Research Funds Central Non-profit Scientific Institution","award":["31971769"],"award-info":[{"award-number":["31971769"]}]},{"name":"the Fundamental Research Funds Central Non-profit Scientific Institution","award":["41771205"],"award-info":[{"award-number":["41771205"]}]},{"name":"the Fundamental Research Funds Central Non-profit Scientific Institution","award":["42101372"],"award-info":[{"award-number":["42101372"]}]},{"name":"the Fundamental Research Funds Central Non-profit Scientific Institution","award":["CARS-34"],"award-info":[{"award-number":["CARS-34"]}]},{"name":"the Fundamental Research Funds Central Non-profit Scientific Institution","award":["1610132021016"],"award-info":[{"award-number":["1610132021016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables\u2014A, model selected variables\u2014B, overlapping variables\u2014C and self-defined variables\u2014D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2\/m2, MAE = 0.29 m2\/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.<\/jats:p>","DOI":"10.3390\/rs14174196","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge"],"prefix":"10.3390","volume":"14","author":[{"given":"Beibei","family":"Shen","sequence":"first","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leichao","family":"Ma","sequence":"additional","affiliation":[{"name":"Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing 100055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenwang","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6221-0394","authenticated-orcid":false,"given":"Alim","family":"Pulatov","sequence":"additional","affiliation":[{"name":"EcoGIS Center, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME), Tashkent 100000, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheenbek","family":"Kulenbekov","sequence":"additional","affiliation":[{"name":"Department of Environmental and Earth Science, American University of Central Asia, Bishkek 720060, Kyrgyzstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0761-9458","authenticated-orcid":false,"given":"Jiquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saltanat","family":"Mambetova","sequence":"additional","affiliation":[{"name":"Earth and Environmental Sciences Department, University of Central Asia, Bishkek 720001, Kyrgyzstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lulu","family":"Hou","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Xin","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1046\/j.1365-2486.1998.00151.x","article-title":"The global carbon sink: A grassland perspective","volume":"4","author":"Scurlock","year":"2010","journal-title":"Glob. 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