{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T04:52:58Z","timestamp":1774932778471,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41971042"],"award-info":[{"award-number":["41971042"]}]},{"name":"National Natural Science Foundation of China","award":["41961134003"],"award-info":[{"award-number":["41961134003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r2) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm\/10 d to 8.48 mm\/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP.<\/jats:p>","DOI":"10.3390\/rs15225411","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T07:33:18Z","timestamp":1700292798000},"page":"5411","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving the STARFM Fusion Method for Downscaling the SSEBOP Evapotranspiration Product from 1 km to 30 m in an Arid Area in China"],"prefix":"10.3390","volume":"15","author":[{"given":"Jingjing","family":"Sun","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"},{"name":"Research Institute for the Geo-Hydrological Protection, National Research Council (CNR), Via Madonna Alta 126, 06128 Perugia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6832-105X","authenticated-orcid":false,"given":"Wen","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"}]},{"given":"Xiaogang","family":"Wang","sequence":"additional","affiliation":[{"name":"The Pearl River Water Resources Research Institute, Guangzhou 510611, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9080-260X","authenticated-orcid":false,"given":"Luca","family":"Brocca","sequence":"additional","affiliation":[{"name":"Research Institute for the Geo-Hydrological Protection, National Research Council (CNR), Via Madonna Alta 126, 06128 Perugia, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2011RG000373","article-title":"A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability","volume":"50","author":"Wang","year":"2012","journal-title":"Rev. Geophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1002\/wat2.1168","article-title":"A review of remote sensing based actual evapotranspiration estimation","volume":"3","author":"Zhang","year":"2016","journal-title":"WIREs Water"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"127422","DOI":"10.1016\/j.jhydrol.2021.127422","article-title":"A hybrid deep learning framework with physical process description for simulation of evapotranspiration","volume":"606","author":"Chen","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1016\/j.rse.2011.02.019","article-title":"Improvements to a MODIS global terrestrial evapotranspiration algorithm","volume":"115","author":"Mu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1111\/jawr.12057","article-title":"Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach","volume":"49","author":"Senay","year":"2013","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"128444","DOI":"10.1016\/j.jhydrol.2022.128444","article-title":"Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations","volume":"613","author":"Zheng","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"453","DOI":"10.5194\/hess-15-453-2011","article-title":"Global land-surface evaporation estimated from satellite-based observations","volume":"15","author":"Miralles","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2017.02.006","article-title":"Spatiotemporal Downscaling Approaches for Monitoring 8-Day 30 m Actual Evapotranspiration","volume":"126","author":"Ke","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Che, X., Zhang, H.K., Sun, Q., Ouyang, Z., and Liu, J. (2022). MODIS Evapotranspiration Downscaling Using a Deep Neural Network Trained Using Landsat 8 Reflectance and Temperature Data. Remote Sens., 14.","DOI":"10.3390\/rs14225876"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"561","DOI":"10.54302\/mausam.v74i3.5112","article-title":"Modeling Medium Resolution Evapotranspiration Using Downscaling Techniques in North-Western Part of India","volume":"74","author":"Dhaloiya","year":"2023","journal-title":"MAUSAM"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4424","DOI":"10.3390\/rs70404424","article-title":"Advancing of land surface temperature retrieval using extreme learning machine and spatio-temporal adaptive data fusion algorithm","volume":"7","author":"Bai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.rse.2016.10.049","article-title":"Estimating High Resolution Evapotranspiration from Disaggregated Thermal Images","volume":"187","author":"Bisquert","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2017.02.004","article-title":"A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration","volume":"126","author":"Mahour","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tan, S., Wu, B., Yan, N., and Zhu, W. (2017). An NDVI-based statistical ET downscaling method. Water, 9.","DOI":"10.3390\/w9120995"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the landsat and MODIS surface reflectance: Predicting daily landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119082","DOI":"10.1016\/j.watres.2022.119082","article-title":"MODIS-Landsat fusion-based single-band algorithms for TSS and turbidity estimation in an urban-waste-dominated river reach","volume":"224","author":"Sahoo","year":"2022","journal-title":"Water Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"116121","DOI":"10.1016\/j.jenvman.2022.116121","article-title":"Integrated remote sensing and machine learning tools for estimating ecological flow regimes in tropical river reaches","volume":"322","author":"Sahoo","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112092","DOI":"10.1016\/j.rse.2020.112092","article-title":"Copula-based probabilistic spectral algorithms for high-frequent streamflow estimation","volume":"251","author":"Sahoo","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112962","DOI":"10.1016\/j.rse.2022.112962","article-title":"A classification-based spatiotemporal adaptive fusion model for the evaluation of remotely sensed evapotranspiration in heterogeneous irrigated agricultural area","volume":"273","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, T., Tang, R., Li, Z.L., Jiang, Y., Liu, M., and Niu, L. (2019). An improved spatio-temporal adaptive Data fusion algorithm for evapotranspiration mapping. Remote Sens., 11.","DOI":"10.3390\/rs11070761"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4672","DOI":"10.1002\/wrcr.20349","article-title":"A data fusion approach for mapping daily evapotranspiration at field scale","volume":"49","author":"Cammalleri","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agwat.2017.03.028","article-title":"Responses of field evapotranspiration to the changes of cropping pattern and groundwater depth in large irrigation district of Yellow River basin","volume":"188","author":"Bai","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhao, H., and Jiang, Y. (2018). Continuous Daily Evapotranspiration Estimation at the Field-Scale over Heterogeneous Agricultural Areas by Fusing ASTER and MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10111694"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/BAMS-D-12-00154.1","article-title":"Heihe watershed allied telemetry experimental research (HiWater) scientific objectives and experimental design","volume":"94","author":"Li","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_27","first-page":"1","article-title":"SLC gap-filled products phase one methodology","volume":"5","author":"Scaramuzza","year":"2004","journal-title":"Landsat Tech. Notes"},{"key":"ref_28","unstructured":"Sayler, K., and Zanter, K. (2022, October 10). Landsat 8 Level 2 Science Product (L2SP) Guide. Available online: https:\/\/d9-wret.s3.us-west-2.amazonaws.com\/assets\/palladium\/production\/s3fs-public\/media\/files\/LSDS-1619_Landsat8-9-Collection2-Level2-Science-Product-Guide-v5.pdf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.agwat.2015.12.001","article-title":"Comparison of three remote sensing based models to estimate evapotranspiration in an oasis-desert region","volume":"165","author":"Lian","year":"2016","journal-title":"Agric. Water Manag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yin, L., Wang, X., Feng, X., Fu, B., and Chen, Y. (2020). A Comparison of SSEBop-Model-Based Evapotranspiration with Eight Evapotranspiration Products in the Yellow River Basin, China. Remote Sens., 12.","DOI":"10.3390\/rs12162528"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ayyad, S., Al Zayed, I.S., Ha, V.T.T., and Ribbe, L. (2019). The Performance of Satellite-Based Actual Evapotranspiration Products and the Assessment of Irrigation Efficiency in Egypt. Water, 11.","DOI":"10.3390\/w11091913"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sun, J., Wang, W., Wang, X., and Huang, D. (2021). Estimating Regional Evapotranspiration Using a Satellite-Based Wind Speed Avoiding Priestley\u2013Taylor Approach. Water, 13.","DOI":"10.3390\/w13213144"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0034-4257(02)00068-8","article-title":"Narrowband to broadband conversions of land surface albedo: II. Validation","volume":"84","author":"Liang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TGRS.2007.904834","article-title":"Land surface emissivity retrieval from different VNIR and TIR sensors","volume":"46","author":"Sobrino","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/0378-3774(83)90095-1","article-title":"Estimation of daily evapotranspiration from one time-of-day measurements","volume":"7","author":"Jackson","year":"1983","journal-title":"Agric. Water Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.agrformet.2011.09.010","article-title":"On the Temporal Upscaling of Evapotranspiration from Instantaneous Remote Sensing Measurements to 8-Day Mean Daily-Sums","volume":"152","author":"Ryu","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2013.07.001","article-title":"Temporal upscaling of instantaneous evapotranspiration: An intercomparison of four methods using eddy covariance measurements and MODIS data","volume":"138","author":"Tang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2995","DOI":"10.5194\/hess-16-2995-2012","article-title":"Reconstruction of temporal variations of evapotranspiration using instantaneous estimates at the time of satellite overpass","volume":"16","author":"Delogu","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3400","DOI":"10.3390\/rs70303400","article-title":"Temporal upscaling and reconstruction of thermal remotely sensed instantaneous evapotranspiration","volume":"7","author":"Xu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xie, D., Gao, F., Sun, L., and Anderson, M. (2018). Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs. Remote Sens., 10.","DOI":"10.3390\/rs10071142"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Reitz, M., Senay, G., and Sanford, W. (2017). Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States. Remote Sens., 9.","DOI":"10.3390\/rs9121181"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2013.07.013","article-title":"A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET","volume":"139","author":"Velpuri","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lu, L., Zhang, T., Wang, T., and Zhou, X. (2018). Evaluation of Collection-6 MODIS Land Surface Temperature Product Using Multi-Year Ground Measurements in an Arid Area of Northwest China. Remote Sens., 10.","DOI":"10.3390\/rs10111852"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2704","DOI":"10.1002\/hyp.10402","article-title":"Simulated impacts of irrigation on evapotranspiration in a strongly exploited region: A case study of the Haihe River basin, China","volume":"29","author":"Lei","year":"2015","journal-title":"Hydrol. Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.rse.2012.02.015","article-title":"A Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery","volume":"121","author":"Long","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/LGRS.2020.2987485","article-title":"A Temperature-Domain SEBAL Model Based on a Wind Speed-Independent Theoretical Trapezoidal Space Between Fractional Vegetation Coverage and Land Surface Temperature","volume":"18","author":"Wang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1109\/JSTARS.2015.2500605","article-title":"Two-Stage Trapezoid: A New Interpretation of the Land Surface Temperature and Fractional Vegetation Coverage Space","volume":"9","author":"Sun","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"58841E","DOI":"10.1117\/12.626665","article-title":"Calculating regional drought indices using evapotranspiration (ET) distribution derived from Landsat7 ETM+ data","volume":"Volume 5884","author":"Gao","year":"2005","journal-title":"Remote Sensing and Modeling of Ecosystems for Sustainability II"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"126029","DOI":"10.1016\/j.jhydrol.2021.126029","article-title":"Evaluation of alternative two-source remote sensing models in partitioning of land evapotranspiration","volume":"597","author":"Chen","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"108853","DOI":"10.1016\/j.agrformet.2022.108853","article-title":"Assessing the impact of urbanization on urban evapotranspiration and its components using a novel four-source energy balance model","volume":"316","author":"Chen","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.advwatres.2012.06.004","article-title":"Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures","volume":"50","author":"Colaizzi","year":"2012","journal-title":"Adv. Water Resour."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5211","DOI":"10.1002\/2016JD026370","article-title":"A simple temperature domain two-source model for estimating agricultural field surface energy fluxes from Landsat images","volume":"122","author":"Yao","year":"2017","journal-title":"J. Geophys. Res. Atmos."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5411\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:23Z","timestamp":1760131523000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":54,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15225411"],"URL":"https:\/\/doi.org\/10.3390\/rs15225411","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]}}}