{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:30:54Z","timestamp":1716424254915},"reference-count":62,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T00:00:00Z","timestamp":1716336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101295"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20210657"]},{"name":"Jiangsu Distinguished Professor Program of the People\u2019s Government of Jiangsu Province, China"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Climate warming hampers grassland growth, particularly in dryland regions. To preserve robust grassland growth and ensure the resilience of grassland in these arid areas, a comprehensive understanding of the interactions between vegetation and climate is imperative. However, existing studies often analyze climate\u2013vegetation interactions using concurrent vegetation indices and meteorological data, neglecting time-lagged influences from various determinants. To address this void, we employed the random forest machine learning method to predict the grassland NDVI (Normalized Difference Vegetation Index) in Asian drylands (including five central Asia countries, the Republic of Mongolia, and Parts of China) from 2001 to 2020, incorporating time-lag influences. We evaluated the prediction model\u2019s performance using three indexes, namely the coefficient of determination (R2), root-mean-square error (RMSE), and Mean Absolute Error (MAE). The results underscore the superiority of the model incorporating time-lag influences, demonstrating its enhanced capability to capture the grassland NDVI in Asian drylands (R2 \u2265 0.915, RMSE \u2264 0.033, MAE \u2264 0.019). Conversely, the model without time-lag influences exhibited relatively poor performance, notably inferior to the time-lag-inclusive model. The latter result aligns closely with remote sensing observations and more accurately reproduces the spatial distributions of the grassland NDVI in Asian drylands. Over the study period, the grassland NDVI in Asian drylands exhibited a weak decreasing trend, primarily concentrated in the western region. Notably, key factors influencing the grassland NDVI included the average grassland NDVI in the previous month, total precipitation in the current month, and average soil moisture in the previous month. This study not only pioneers a novel approach to predicting grassland growth but also contributes valuable insights for formulating sustainable strategies to preserve the integrity of grassland ecosystems.<\/jats:p>","DOI":"10.3390\/rs16111838","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T10:54:09Z","timestamp":1716375249000},"page":"1838","source":"Crossref","is-referenced-by-count":0,"title":["Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0332-8488","authenticated-orcid":false,"given":"Lijuan","family":"Miao","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Yuyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0058-4857","authenticated-orcid":false,"given":"Evgenios","family":"Agathokleous","sequence":"additional","affiliation":[{"name":"School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Gang","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Geographical Science, Inner Mongolia Normal University, Hohhot 010028, China"}]},{"given":"Ziyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Changwang School of Honors, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2332-2873","authenticated-orcid":false,"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Remote sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,22]]},"reference":[{"key":"ref_1","first-page":"1537","article-title":"Spatio-temporal characteristics of Xinjiang grassland NDVI and its response to climate change from 1981 to 2018","volume":"43","author":"Chen","year":"2023","journal-title":"Acta Ecol. 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