{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T01:37:38Z","timestamp":1773106658359,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T00:00:00Z","timestamp":1725062400000},"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":["42307564"],"award-info":[{"award-number":["42307564"]}],"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":["42271316"],"award-info":[{"award-number":["42271316"]}],"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":["21YF5NA070"],"award-info":[{"award-number":["21YF5NA070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Department of Gansu Province, Key R&amp;D Program","award":["42307564"],"award-info":[{"award-number":["42307564"]}]},{"name":"Science and Technology Department of Gansu Province, Key R&amp;D Program","award":["42271316"],"award-info":[{"award-number":["42271316"]}]},{"name":"Science and Technology Department of Gansu Province, Key R&amp;D Program","award":["21YF5NA070"],"award-info":[{"award-number":["21YF5NA070"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Determining the responses of non-photosynthetic vegetation (NPV) and photosynthetic vegetation (PV) communities to climate change is crucial in illustrating the sensitivity and sustainability of these ecosystems. In this study, we evaluated the accuracy of inverting NPV and PV using Landsat imagery with random forest (RF), backpropagation neural network (BPNN), and fully connected neural network (FCNN) models. Additionally, we inverted MODIS NPV and PV time-series data using spectral unmixing. Based on this, we analyzed the responses of NPV and PV to precipitation and drought across different ecological regions. The main conclusions are as follows: (1) In NPV remote sensing inversion, the softmax activation function demonstrates greater advantages over the ReLU activation function. Specifically, the use of the softmax function results in an approximate increase of 0.35 in the R2 value. (2) Compared with a five-layer FCNN with 128 neurons and a three-layer BPNN with 12 neurons, a random forest model with over 50 trees and 5 leaf nodes provides better inversion results for NPV and PV (R2_RF-NPV = 0.843, R2_RF-PV = 0.861). (3) Long-term drought or heavy rainfall events can affect the utilization of precipitation by NPV and PV. There is a high correlation between extreme precipitation events following prolonged drought and an increase in PV coverage. (4) Under long-term drought conditions, the vegetation in the study area responded to precipitation during the last winter and growing season. This study provides an illustration of the response of semi-arid ecosystems to drought and wetting events, thereby offering a data basis for the effect evaluation of afforestation projects.<\/jats:p>","DOI":"10.3390\/rs16173226","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:59:40Z","timestamp":1725263980000},"page":"3226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning-Based Remote Sensing Inversion of Non-Photosynthetic\/Photosynthetic Vegetation Coverage in Desertified Areas and Its Response to Drought Analysis"],"prefix":"10.3390","volume":"16","author":[{"given":"Zichen","family":"Guo","sequence":"first","affiliation":[{"name":"College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8487-3260","authenticated-orcid":false,"given":"Shulin","family":"Liu","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Road 320, Lanzhou 730000, China"}]},{"given":"Kun","family":"Feng","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Road 320, Lanzhou 730000, China"}]},{"given":"Wenping","family":"Kang","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1038\/s43017-021-00226-z","article-title":"Drivers and impacts of changes in China\u2019s drylands","volume":"2","author":"Li","year":"2021","journal-title":"Nat. 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