{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:07:41Z","timestamp":1760242061830,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,16]],"date-time":"2018-12-16T00:00:00Z","timestamp":1544918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["lzujbky-2018-45"],"award-info":[{"award-number":["lzujbky-2018-45"]}]},{"name":"the Chinese Natural Science Foundation Project","award":["41801226"],"award-info":[{"award-number":["41801226"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study assessed accuracies of MCD43A3, Global Land-Surface Satellite (GLASS) and forthcoming Multi-source Data Synergized Quantitative Remote Sensing Production system (MuSyQ) albedos using ground observations and Huan Jing (HJ) data over the Heihe River Basin. MCD43A3 and MuSyQ albedos show similar high accuracies with identical root mean square errors (RMSE). Nevertheless, MuSyQ albedo is better correlated with ground measurements when sufficient valid observations are available or snow-free. The opposite happens when less than seven valid observations are available. GLASS albedo presents a larger RMSE than MCD43A3 and MuSyQ albedos in comparison with ground measurements. Over surfaces with smaller seasonal variations, MCD43A3 and MuSyQ albedos show smaller RMSEs than GLASS albedo in comparison with HJ albedo. However, for surfaces with larger temporal variations, both RMSEs and R2 of GLASS albedo are comparable with MCD43A3 and MuSyQ. Generally, MCD43A3 and MuSyQ albedos featured the same RMSEs of 0.034 and similar R2 (0.920 and 0.903, respectively), which are better than GLASS albedo (RMSE = 0.043, R2 = 0.787). However, when it comes to comparison with aggregated HJ albedo, MuSyQ and GLASS albedos are with lower RMSEs of 0.027 and 0.032 and higher R2 of 0.900 and 0.898 respectively than MCD43A3 (RMSE = 0.038, R2 = 0.836). Despite the limited geographic region of the study area, they still provide an important insight into the accuracies of three albedo products.<\/jats:p>","DOI":"10.3390\/rs10122045","type":"journal-article","created":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T02:15:59Z","timestamp":1545099359000},"page":"2045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China"],"prefix":"10.3390","volume":"10","author":[{"given":"Xiaodan","family":"Wu","sequence":"first","affiliation":[{"name":"College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Remote Sensing Laboratories, University of Zurich, 8057 Zurich, Switzerland"}]},{"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"}]},{"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"}]},{"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"}]},{"given":"Baocheng","family":"Dou","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3770-6894","authenticated-orcid":false,"given":"Xinwen","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Geography and Environment Science, Zhejiang Normal University, Zhejiang 321004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4283-2484","authenticated-orcid":false,"given":"Andreas","family":"Hueni","sequence":"additional","affiliation":[{"name":"Remote Sensing Laboratories, University of Zurich, 8057 Zurich, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liang, S. 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