{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:52:32Z","timestamp":1767084752579,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"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":["2021YFC3000201","2018YFC0407705","45-33-2069999","WR0145B272016","WR0145B012017"],"award-info":[{"award-number":["2021YFC3000201","2018YFC0407705","45-33-2069999","WR0145B272016","WR0145B012017"]}]},{"name":"Research on Key Technologies of shoreline extraction based on machine learning","award":["2021YFC3000201","2018YFC0407705","45-33-2069999","WR0145B272016","WR0145B012017"],"award-info":[{"award-number":["2021YFC3000201","2018YFC0407705","45-33-2069999","WR0145B272016","WR0145B012017"]}]},{"name":"Research on Key Technologies of landslide Dam management and comprehensive utilization of barrier lake in Mahu reservoir","award":["2021YFC3000201","2018YFC0407705","45-33-2069999","WR0145B272016","WR0145B012017"],"award-info":[{"award-number":["2021YFC3000201","2018YFC0407705","45-33-2069999","WR0145B272016","WR0145B012017"]}]},{"name":"Special Project of Basic Scientific Research of China Institute of Water Resources and Hydropower Research","award":["2021YFC3000201","2018YFC0407705","45-33-2069999","WR0145B272016","WR0145B012017"],"award-info":[{"award-number":["2021YFC3000201","2018YFC0407705","45-33-2069999","WR0145B272016","WR0145B012017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency\u2019s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., &gt;3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm3\/cm3 versus 0.027 to 0.032 cm3\/cm3 for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications.<\/jats:p>","DOI":"10.3390\/s22145366","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T05:37:53Z","timestamp":1658209073000},"page":"5366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index"],"prefix":"10.3390","volume":"22","author":[{"given":"Xin","family":"Lu","sequence":"first","affiliation":[{"name":"Sichuan Research Institute of Water Conservancy, Chengdu 610072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7426-4928","authenticated-orcid":false,"given":"Hongli","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}]},{"given":"Yanyan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chengdu University of Information Technology, Chengdu 610200, China"}]},{"given":"Shuangmei","family":"Liu","sequence":"additional","affiliation":[{"name":"Sichuan Research Institute of Water Conservancy, Chengdu 610072, China"}]},{"given":"Zelong","family":"Ma","sequence":"additional","affiliation":[{"name":"Sichuan Research Institute of Water Conservancy, Chengdu 610072, China"}]},{"given":"Yunzhong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Electronics Technology Group Corporation (CETC), Big Data Research Institute Chengdu Branch Co., Ltd., Chengdu 610093, China"},{"name":"National Engineering Laboratory for Big Data Application on Improving Government Governance Capabilities, Guiyang 550081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2407-8051","authenticated-orcid":false,"given":"Chuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Sichuan Research Institute of Water Conservancy, Chengdu 610072, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1175\/1520-0477(2000)081<1281:TGSMDB>2.3.CO;2","article-title":"The global soil moisture data bank","volume":"81","author":"Robock","year":"2000","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"444","DOI":"10.2136\/vzj2003.4440","article-title":"A review of advances in dielectric and electrical conductivity measurement in soils using time domain reflectometry","volume":"2","author":"Robinson","year":"2003","journal-title":"Vadose Zone J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.jhydrol.2007.06.032","article-title":"Evaluation of a low-cost soil water content sensor for wireless network applications","volume":"344","author":"Bogena","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.still.2004.10.004","article-title":"Electrical resistivity survey in soil science: A review","volume":"83","author":"Cousin","year":"2005","journal-title":"Soil Tillage Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, S., She, D., Zhang, L., Guo, M., and Liu, X. (2019). Spatial downscaling methods of soil moisture based on multisource remote sensing data and its application. Water, 11.","DOI":"10.3390\/w11071401"},{"key":"ref_6","first-page":"1","article-title":"Advances in soil moisture retrieval from remote sensing","volume":"39","author":"Pan","year":"2019","journal-title":"Acta Ecol. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Han, Y., Bai, X., Shao, W., and Wang, J. (2020). Retrieval of soil moisture by integrating sentinel-1A and MODIS data over agricultural fields. Water, 12.","DOI":"10.3390\/w12061726"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, D., and Zhou, G. (2016). Estimation of soil moisture from optical and thermal remote sensing: A review. Sensors, 16.","DOI":"10.3390\/s16081308"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Peng, J., and Loew, A. (2017). Recent advances in soil moisture estimation from remote sensing. Water, 9.","DOI":"10.3390\/w9070530"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"F01002","DOI":"10.1029\/2007JF000769","article-title":"Multisensor historical climatology of satellite-derived global land surface moisture","volume":"113","author":"Owe","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1109\/TGRS.2008.2011617","article-title":"An improved soil moisture retrieval algorithm for ERS and METOP scatterometer observations","volume":"47","author":"Naeimi","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jacquette, E., Al Bitar, A., Mialon, A., Kerr, Y., Quesney, A., Cabot, F., and Richaume, P. (2010, January 20\u201323). SMOS CATDS level 3 global products over land. Proceedings of the SPIE Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, Toulouse, France.","DOI":"10.1117\/12.865093"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"425","DOI":"10.5194\/hess-15-425-2011","article-title":"Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals","volume":"15","author":"Liu","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of spatial downscaling of satellite remotely sensed soil moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6790","DOI":"10.3390\/rs5126790","article-title":"A Downscaling method for improving the spatial resolution of AMSR-E derived soil moisture product based on MSG-SEVIRI data","volume":"5","author":"Zhao","year":"2013","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TGRS.2017.2649522","article-title":"Performance evaluation of the triangle-based empirical soil moisture relationship models based on Landsat-5 TM data and in situ measurements","volume":"55","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3935","DOI":"10.1016\/j.rse.2008.06.012","article-title":"Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency","volume":"112","author":"Merlin","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2016.01.009","article-title":"Improving spatial representation of soil moisture by integration of microwave observations and the temperature\u2013vegetation\u2013drought index derived from MODIS products","volume":"113","author":"Wang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yang, Y., Jing, W., and Yue, X. (2018). Comparison of different machine learning approaches for monthly satellite-based soil moisture downscaling over Northeast China. Remote Sens., 10.","DOI":"10.3390\/rs10010031"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1109\/TGRS.2011.2161318","article-title":"Improving spatial soil moisture representation through integration of AMSR-E and MODIS products","volume":"50","author":"Kim","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/TGRS.2015.2462074","article-title":"Spatial downscaling of satellite soil moisture data using a vegetation temperature condition index","volume":"54","author":"Peng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.isprsjprs.2007.03.002","article-title":"Modified perpendicular drought index (MPDI): A real-time drought monitoring method","volume":"62","author":"Ghulam","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"012040","DOI":"10.1088\/1755-1315\/17\/1\/012040","article-title":"A modified perpendicular drought index in NIR-Red reflectance space","volume":"17","author":"Li","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_26","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_27","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_28","doi-asserted-by":"crossref","first-page":"5346","DOI":"10.3390\/rs5105346","article-title":"An enhanced spatial and temporal data fusion model for fusing landsat and MODIS surface reflectance to generate high temporal landsat-like data","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2016.07.028","article-title":"Land cover change detection by integrating object-based data blending model of Landsat and MODIS","volume":"184","author":"Lu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TGRS.2012.2186638","article-title":"Spatiotemporal reflectance fusion via sparse representation","volume":"50","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2018.02.009","article-title":"A robust adaptive spatial and temporal image fusion model for complex land surface changes","volume":"208","author":"Zhao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"140302","DOI":"10.1007\/s11432-019-2805-y","article-title":"A sensor bias-driven spatio-temporal fusion model based on convolutional neural networks","volume":"63","author":"Li","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1016\/j.rse.2009.06.012","article-title":"A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors","volume":"113","author":"Merlin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"1","article-title":"A method for downscaling satellite soil moisture based on land surface temperature and net surface shortwave radiation","volume":"99","author":"Wang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s00254-006-0544-2","article-title":"Designing of the perpendicular drought index","volume":"52","author":"Ghulam","year":"2007","journal-title":"Environ. Geol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1109\/LGRS.2017.2753203","article-title":"Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15","volume":"14","author":"Colliander","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2019.02.022","article-title":"Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau","volume":"225","author":"Wei","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1109\/TGRS.2019.2941696","article-title":"A value-consistent method for downscaling SMAP passive soil moisture with MODIS products using self-adaptive window","volume":"58","author":"Wen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5366\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:53:31Z","timestamp":1760140411000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5366"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,19]]},"references-count":40,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145366"],"URL":"https:\/\/doi.org\/10.3390\/s22145366","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,7,19]]}}}