{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T14:55:53Z","timestamp":1761663353208,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,3,18]],"date-time":"2015-03-18T00:00:00Z","timestamp":1426636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High spatial resolution airborne data with little sub-pixel heterogeneity were used to evaluate the suitability of the temperature\/vegetation (Ts\/VI) space method developed from satellite observations, and were explored to improve the performance of the Ts\/VI space method for estimating soil moisture (SM). An evaluation of the airborne \u0394Ts\/Fr space (incorporated with air temperature) revealed that normalized difference vegetation index (NDVI) saturation and disturbed pixels were hindering the appropriate construction of the space. The non-disturbed \u0394Ts\/Fr space, which was modified by adjusting the NDVI saturation and eliminating the disturbed pixels, was clearly correlated with the measured SM. The SM estimations of the non-disturbed \u0394Ts\/Fr  space using the evaporative fraction (EF) and temperature vegetation dryness index (TVDI) were validated by using the SM measured at a depth of 4 cm, which was determined according to the land surface types. The validation results show that the EF approach provides superior estimates with a lower RMSE  (0.023 m3\u00b7m\u22123) value and a higher correlation coefficient (0.68) than the TVDI. The application of the airborne \u0394Ts\/Fr  space shows that the two modifications proposed in this study strengthen the link between the \u0394Ts\/Fr space and SM, which is important for improving the precision of the remote sensing Ts\/VI space method for monitoring SM.<\/jats:p>","DOI":"10.3390\/rs70303114","type":"journal-article","created":{"date-parts":[[2015,3,18]],"date-time":"2015-03-18T14:09:08Z","timestamp":1426687748000},"page":"3114-3137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Evaluation of the Airborne CASI\/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture"],"prefix":"10.3390","volume":"7","author":[{"given":"Lei","family":"Fan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguang","family":"Wen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Joint Center for Global Change Studies, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5302-9849","authenticated-orcid":false,"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongqin","family":"You","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heshun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoning","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,3,18]]},"reference":[{"key":"ref_1","first-page":"54","article-title":"Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing","volume":"32","author":"Gao","year":"2014","journal-title":"Int. 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