{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:13:03Z","timestamp":1760145183521,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFD1900500","2023YFD1900801-02","52130906","SKL2022TS13"],"award-info":[{"award-number":["2022YFD1900500","2023YFD1900801-02","52130906","SKL2022TS13"]}]},{"name":"Chinese National Science Fund","award":["2022YFD1900500","2023YFD1900801-02","52130906","SKL2022TS13"],"award-info":[{"award-number":["2022YFD1900500","2023YFD1900801-02","52130906","SKL2022TS13"]}]},{"name":"Independent Research Project of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin","award":["2022YFD1900500","2023YFD1900801-02","52130906","SKL2022TS13"],"award-info":[{"award-number":["2022YFD1900500","2023YFD1900801-02","52130906","SKL2022TS13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation challenges. Moreover, the existing LST products are constrained by their low spatiotemporal resolution, limiting their broader applicability. The fusion of multi-source remote sensing data offers a viable solution to enhance spatiotemporal resolution. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used to estimate time series LST utilizing multi-temporal Landsat 8 (L8) and MOD21A2 within the Haihe basin in 2021. Validation of ESTARFM LST was conducted against L8 LST and in situ LST. The results can be summarized as follows: (1) ESTARFM was found to be effective in heterogeneous regions within the Haihe basin, yielding LST with a spatiotemporal resolution of 30 m and 8 d while retaining clear texture information; (2) the comparison between ESTARFM LST and L8 LST shows a coefficient determination (R2) exceeding 0.59, a mean absolute error (MAE) lower than 2.43 K, and a root mean square error (RMSE) lower than 2.63 K for most dates; (3) comparison between ESTARFM LST and in situ LST showcased high validation accuracy, revealing a R2 of 0.87, a MAE of 2.27 K, and a RMSE of 4.12 K. The estimated time series LST exhibited notable reliability and robustness. This study introduced ESTARFM for LST estimation, achieving satisfactory outcomes. The findings offer a valuable reference for other regions to generate LST data with a spatiotemporal resolution of 8 d and 30 m, thereby enhancing the application of data products in agriculture and hydrology contexts.<\/jats:p>","DOI":"10.3390\/rs16132374","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T08:31:36Z","timestamp":1719563496000},"page":"2374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving the Spatiotemporal Resolution of Land Surface Temperature Using a Data Fusion Method in Haihe Basin, China"],"prefix":"10.3390","volume":"16","author":[{"given":"Rencai","family":"Lin","sequence":"first","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China"}]},{"given":"Zheng","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China"}]},{"given":"He","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China"}]},{"given":"Congying","family":"Han","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China"}]},{"given":"Baozhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China"}]},{"given":"Maomao","family":"Jule","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/MGRS.2021.3050782","article-title":"Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A review of reconstruction and spatiotemporal fusion techniques","volume":"9","author":"Wu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2023.10.004","article-title":"Generating 60\u2013100 m, hourly, all-weather land surface temperatures based on the Landsat, ECOSTRESS, and reanalysis temperature combination (LERC)","volume":"205","author":"Quan","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e2022RG000777","DOI":"10.1029\/2022RG000777","article-title":"Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications","volume":"61","author":"Li","year":"2023","journal-title":"Rev. Geophys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.rse.2017.09.019","article-title":"Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China","volume":"204","author":"Meng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Qiao, Z., Liu, L., Qin, Y., Xu, X., Wang, B., and Liu, Z. (2020). The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China. Remote Sens., 12.","DOI":"10.3390\/rs12050794"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, C., Yan, F., Lei, X., Ding, X., Zheng, Y., Liu, L., and Zhang, S. (2020). Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China. Remote Sens., 12.","DOI":"10.3390\/rs12183006"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.biosystemseng.2017.09.015","article-title":"Modified Penman\u2013Monteith equation for monitoring evapotranspiration of wheat crop: Relationship between the surface resistance and remotely sensed stress index","volume":"164","author":"Amazirh","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.agwat.2018.06.014","article-title":"Estimating the water budget components of irrigated crops: Combining the FAO-56 dual crop coefficient with surface temperature and vegetation index data","volume":"208","author":"Merlin","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2018.10.002","article-title":"Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale","volume":"219","author":"Song","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2018.04.013","article-title":"Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil","volume":"211","author":"Amazirh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.rse.2017.12.036","article-title":"Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters","volume":"206","author":"Tagesson","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106896","DOI":"10.1016\/j.agwat.2021.106896","article-title":"Large-scale monitoring of soil moisture using Temperature Vegetation Quantitative Index (TVQI) and exponential filtering: A case study in Beijing","volume":"252","author":"Zhao","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111863","DOI":"10.1016\/j.rse.2020.111863","article-title":"Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach","volume":"246","author":"Long","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9724","DOI":"10.1029\/2017WR022437","article-title":"Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions","volume":"54","author":"Sheffield","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1029\/2018WR024039","article-title":"Comparison of Contemporary In Situ, Model, and Satellite Remote Sensing Soil Moisture with a Focus on Drought Monitoring","volume":"55","author":"Ford","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1080\/01431160010006971","article-title":"A Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM data and its Application to the Israel-Egypt Border Region","volume":"22","author":"Qin","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/TGRS.2008.2006180","article-title":"Developing Algorithm for Operational GOES-R Land Surface Temperature Product","volume":"47","author":"Yu","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1080\/01431169608948760","article-title":"Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data","volume":"17","author":"Sobrino","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1007\/s11430-010-4160-3","article-title":"China land soil moisture EnKF data assimilation based on satellite remote sensing data","volume":"54","author":"Shi","year":"2011","journal-title":"Sci. China Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1002\/hyp.8240","article-title":"Comparative analysis of relationships between NLDAS-2 forcings and model outputs","volume":"26","author":"Xia","year":"2012","journal-title":"Hydrol. Processes"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The Global Land Data Assimilation System","volume":"85","author":"Rodell","year":"2004","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2017.12.010","article-title":"Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States","volume":"206","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112256","DOI":"10.1016\/j.rse.2020.112256","article-title":"A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering","volume":"254","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4539","DOI":"10.1109\/JSTARS.2015.2464094","article-title":"An Effective Interpolation Method for MODIS Land Surface Temperature on the Qinghai\u2013Tibet Plateau","volume":"8","author":"Yu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Januar, T.W., Lin, T.-H., Huang, C.-Y., and Chang, K.-E. (2020). Modifying an Image Fusion Approach for High Spatiotemporal LST Retrieval in Surface Dryness and Evapotranspiration Estimations. Remote Sens., 12.","DOI":"10.3390\/rs12030498"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, W., Huang, J., Yang, L., Chen, Y., Fang, Y., Jin, H., Sun, H., and Huang, R. (2021). A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13163231"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112437","DOI":"10.1016\/j.rse.2021.112437","article-title":"A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature","volume":"260","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.rse.2018.02.067","article-title":"A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data","volume":"209","author":"Houborg","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2019.02.004","article-title":"Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data","volume":"150","author":"Amazirh","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kou, X., Jiang, L., Bo, Y., Yan, S., and Chai, L. (2016). Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sens., 8.","DOI":"10.3390\/rs8020105"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111364","DOI":"10.1016\/j.rse.2019.111364","article-title":"Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution","volume":"233","author":"Long","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112301","DOI":"10.1016\/j.rse.2021.112301","article-title":"Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale","volume":"255","author":"Abowarda","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, S., Cheng, J., and Zhang, Q. (2021). A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution. Remote Sens., 13.","DOI":"10.3390\/rs13112211"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Han, Y., Jia, D., Zhuo, L., Sauvage, S., S\u00e1nchez-P\u00e9rez, J.-M., Huang, H., and Wang, C. (2018). Assessing the Water Footprint of Wheat and Maize in Haihe River Basin, Northern China (1956\u20132015). Water, 10.","DOI":"10.3390\/w10070867"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Knauer, K., Gessner, U., Fensholt, R., and Kuenzer, C. (2016). An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes. Remote. Sens., 8.","DOI":"10.3390\/rs8050425"},{"key":"ref_40","first-page":"82","article-title":"Problems and Solutions of Rural Water Supply in Beijing Daxing District","volume":"25","author":"Dong","year":"2019","journal-title":"Tianjin Agric. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.agwat.2012.11.008","article-title":"Dual crop coefficient modelling applied to the winter wheat\u2013summer maize crop sequence in North China Plain: Basal crop coefficients and soil evaporation component","volume":"117","author":"Zhao","year":"2012","journal-title":"Agric. Water Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"126104","DOI":"10.1016\/j.jhydrol.2021.126104","article-title":"Crop evapotranspiration prediction by considering dynamic change of crop coefficient and the precipitation effect in back-propagation neural network model","volume":"596","author":"Han","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"127414","DOI":"10.1016\/j.jhydrol.2021.127414","article-title":"Real-time methods for short and medium-term evapotranspiration forecasting using dynamic crop coefficient and historical threshold","volume":"606","author":"Han","year":"2022","journal-title":"J. Hydrol. Hydromech."},{"key":"ref_44","first-page":"1","article-title":"Statistical Analysis on Relationship Between Soil Surface Temperature and Air Temperature","volume":"25","author":"Jiang","year":"2004","journal-title":"Agric. Meteorol."},{"key":"ref_45","first-page":"86","article-title":"Variability analysis of freezing depth mode of vertical buried pipes with different materials in cold area","volume":"48","author":"Wang","year":"2016","journal-title":"J. Hydraul. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.11834\/jrs.20211233","article-title":"Difference of temperature distribution characteristics based on remote sensing and meteorological station temperature data","volume":"25","author":"Wang","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lin, R., Chen, H., Wei, Z., Li, Y., Zhang, B., Sun, H., and Cheng, M. (2022). Improved Surface Soil Moisture Estimation Model in Semi-Arid Regions Using the Vegetation Red-Edge Band Sensitive to Plant Growth. Atmosphere, 13.","DOI":"10.3390\/atmos13060930"},{"key":"ref_48","first-page":"2316","article-title":"Research on Surface Temperature Inversion Algorithm Based on Landsat8 Data in Urumqi City","volume":"48","author":"Ma","year":"2020","journal-title":"Comput. Digit. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"103333","DOI":"10.1016\/j.infrared.2020.103333","article-title":"Calculation of land surface emissivity and retrieval of land surface temperature based on a spectral mixing model","volume":"108","author":"Yin","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1029\/2018WR024162","article-title":"Estimation of Surface Soil Moisture with Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land","volume":"55","author":"Bai","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_51","first-page":"54","article-title":"Generation of land surface temperature with high spatial and temporal resolution based on FSDAF method","volume":"30","author":"Yang","year":"2018","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_52","first-page":"193","article-title":"Verification of high-resolution land surface temperature by blending ASTER and MODIS data in Heihe River Basin","volume":"31","author":"Yang","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2018.04.005","article-title":"A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud","volume":"141","author":"Zeng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2294","DOI":"10.1002\/2013JD020803","article-title":"Spatio-temporal interpolation of daily temperatures for global land areas at 1\u2009km resolution","volume":"119","author":"Kilibarda","year":"2014","journal-title":"J. Geophys. Res. Atmos."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:06:59Z","timestamp":1760108819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":54,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132374"],"URL":"https:\/\/doi.org\/10.3390\/rs16132374","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,6,28]]}}}