{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T04:07:47Z","timestamp":1784952467229,"version":"3.55.0"},"reference-count":62,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,22]],"date-time":"2019-01-22T00:00:00Z","timestamp":1548115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["(Grant No. XDA20100104), the National Natural Science Foundation of China (grant numbers: 91425303, 41871250 and 41601482), and the China Postdoctoral Science Foundation funded project (Grant No. 2016T90960)"],"award-info":[{"award-number":["(Grant No. XDA20100104), the National Natural Science Foundation of China (grant numbers: 91425303, 41871250 and 41601482), and the China Postdoctoral Science Foundation funded project (Grant No. 2016T90960)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC\/m2\/day and R2 = 0.53, RMSE = 2.12 gC\/m2\/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (\u03b5max or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.<\/jats:p>","DOI":"10.3390\/rs11030225","type":"journal-article","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T03:52:32Z","timestamp":1548301952000},"page":"225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1233-4484","authenticated-orcid":false,"given":"Haibo","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3783-8363","authenticated-orcid":false,"given":"Mingguo","family":"Ma","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Beibei, Chongqing 400715, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liying","family":"Geng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1046\/j.1365-2486.2003.00594.x","article-title":"Net changes in regional woody vegetation cover and carbon storage in Texas dry-lands, 1937\u20131999","volume":"9","author":"Asner","year":"2003","journal-title":"Glob. 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