{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:08:48Z","timestamp":1760710128393,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T00:00:00Z","timestamp":1581033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes.<\/jats:p>","DOI":"10.3390\/rs12030551","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T11:50:28Z","timestamp":1581076228000},"page":"551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices\u2014Prospects and Case Study"],"prefix":"10.3390","volume":"12","author":[{"given":"Mengyu","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland\u2013College Park, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3836-2723","authenticated-orcid":false,"given":"Marion","family":"Pause","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Sciences, TU Dresden, 01062 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolas","family":"Prechtel","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Sciences, TU Dresden, 01062 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5913-8600","authenticated-orcid":false,"given":"Matthias","family":"Schramm","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Mohanty, B.P., Cosh, M.H., Lakshmi, V., and Montzka, C. (2017). Soil Moisture Remote Sensing: State-of-the-Science. Vadose Zo. J., 16.","DOI":"10.2136\/vzj2016.10.0105"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"315","DOI":"10.5194\/isprsannals-I-7-315-2012","article-title":"Fusion of Active and Passive Microwave Observations to Create an Essential Climate Variable Data Record on Soil Mmoisture","volume":"1","author":"Wagner","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.rse.2014.07.023","article-title":"Evaluation of the ESA CCI Soil Moisture Product Using Ground-Based Observations","volume":"162","author":"Dorigo","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dorigo, W., de Jeu, R., Chung, D., Parinussa, R., Liu, Y., Wagner, W., and Fern\u00e1ndez-Prieto, D. (2012). Evaluating Global Trends (1988-2010) in Harmonized Multi-Satellite Surface Soil Moisture. Geophys. Res. Lett., 39.","DOI":"10.1029\/2012GL052988"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Filippucci, P., Tarpanelli, A., Massari, C., Serafini, A., Strati, V., Alberi, M., Raptis, K.G.C., Mantovani, F., and Brocca, L. (2020). Soil Moisture as a Potential Variable for Tracking and Quantifying Irrigation: A Case Study with Proximal Gamma-Ray Spectroscopy Data. Adv. Water Resour, 136.","DOI":"10.1016\/j.advwatres.2019.103502"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zreda, M., Desilets, D., Ferr\u00e9, T.P.A., and Scott, R.L. (2008). Measuring Soil Moisture Content Non-Invasively at Intermediate Spatial Scale Using Cosmic-Ray Neutrons. Geophys. Res. Lett., 35.","DOI":"10.1029\/2008GL035655"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1109\/36.942551","article-title":"Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission","volume":"39","author":"Kerr","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","unstructured":"Bartalis, Z., Naeimi, V., Hasenauer, S., and Wagner, W. (2008). ASCAT Soil Moisture Report Series No. 15 ASCAT Soil Moisture Product Handbook."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer, S., Scipal, K., Bonekamp, H., Figa, J., and Anderson, C. (2007). Initial Soil Moisture Retrievals from the METOP-A Advanced Scatterometer (ASCAT). Geophys. Res. Lett., 34.","DOI":"10.1029\/2007GL031088"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/JPROC.2010.2043918","article-title":"The Soil Moisture Active Passive (SMAP) Mission","volume":"98","author":"Entekhabi","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2017.01.021","article-title":"Validation of SMAP surface soil moisture products with core validation sites","volume":"191","author":"Colliander","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bauer-Marschallinger, B., Paulik, C., Hochst\u00f6ger, S., Mistelbauer, T., Modanesi, S., Ciabatta, L., Massari, C., Brocca, L., Wagner, W., and Bauer-Marschallinger, B. (2018). Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sens., 10.","DOI":"10.3390\/rs10071030"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1109\/TGRS.2010.2050488","article-title":"Sensitivity of Passive Microwave Observations to Soil Moisture and Vegetation Water Content: L-Band to W-Band","volume":"49","author":"Calvet","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Elachi, C., and Van Zyl, J. (2006). Introduction to the Physics and Techniques of Remote Sensing. Wiley-Interscience.","DOI":"10.1002\/0471783390"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alexandridis, T., Cherif, I., Bilas, G., Almeida, W., Hartanto, I., van Andel, S., Araujo, A., Alexandridis, T.K., Cherif, I., and Bilas, G. (2016). Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes. Water, 8.","DOI":"10.3390\/w8010032"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Torres-Rua, A., Ticlavilca, A., Bachour, R., and McKee, M. (2016). Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines. Water, 8.","DOI":"10.3390\/w8040167"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pause, M., Zacharias, S., Schulz, K., and Lausch, A. (2012). Near-Surface Soil Moisture Estimation by Combining Airborne L-Band Brightness Temperature Observations and Imaging Hyperspectral Data at the Field Scale. J. Appl. Remote Sens., 6.","DOI":"10.1117\/1.JRS.6.063516"},{"key":"ref_19","first-page":"47","article-title":"Effect of Vegetation Index Choice on Soil Moisture Retrievals via the Synergistic Use of Synthetic Aperture Radar and Optical Remote Sensing","volume":"80","author":"Qiu","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","first-page":"71","article-title":"Indicator-Based Soil Moisture Monitoring of Wetlands by Utilizing Sentinel and Landsat Remote Sensing Data","volume":"86","author":"Klinke","year":"2018","journal-title":"PFG J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Zribi, M., and Bazzi, H. (2017). Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sens., 9.","DOI":"10.3390\/rs9121292"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dabrowska-Zielinska, K., Musial, J., Malinska, A., Budzynska, M., Gurdak, R., Kiryla, W., Bartold, M., and Grzybowski, P. (2018). Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery. Remote Sens., 10.","DOI":"10.20944\/preprints201810.0453.v1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Samaniego, L., Kumar, R., and Attinger, S. (2010). Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46.","DOI":"10.1029\/2008WR007327"},{"key":"ref_24","unstructured":"(2019, November 27). USGS EROS Archive - Sentinel-2, Available online: https:\/\/www.usgs.gov\/centers\/eros\/science\/usgs-eros-archive-sentinel-2?qt-science_center_objects=0#qt-science_center_objects."},{"key":"ref_25","unstructured":"(2019, November 27). STEP | Science Toolbox Exploitation Platform. Available online: http:\/\/step.esa.int\/main\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0034-4257(99)00036-X","article-title":"A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data","volume":"70","author":"Wagner","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"Bauer-Marschallinger, B., and Paulik, C. (2019). \u201cCGLOPS-1\u201d Algorithm Theoretical Basis Document Soil Water Index Collection 1km Version 1. Copernic. Glob. Land Oper."},{"key":"ref_28","first-page":"1","article-title":"Validation of the ASCAT Soil Water Index Using in Situ Data from the International Soil Moisture Network","volume":"30","author":"Paulik","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","unstructured":"(2019, November 27). Okavango Delta Monitoring & Forecasting. Available online: http:\/\/okavangodata.ub.bw\/ori\/monitoring\/water\/."},{"key":"ref_30","unstructured":"Buchhorn, M., Smets, B., Bertels, L., Lesiv, M., Tsendbazar, N.-E., Herold, M., and Fritz, S. (2019). Land Cover 100m: Collection 2: Epoch 2015. Copernic. Glob. Land Serv."},{"key":"ref_31","unstructured":"Deering, D.W. Rangeland Reflectance Characteristics Measured by Aircraft and Spacecraft Sensors."},{"key":"ref_32","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_33","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with Erts."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_35","unstructured":"Weiss, M., and Baret, F. (2016). S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER, INRA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.rse.2006.07.013","article-title":"Influence of Landscape Spatial Heterogeneity on the Non-Linear Estimation of Leaf Area Index from Moderate Spatial Resolution Remote Sensing Data","volume":"105","author":"Garrigues","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1080\/01431169108929728","article-title":"A Model of Regional Primary Production for Use with Coarse Resolution Satellite Data","volume":"12","author":"Prince","year":"1991","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Weiss, M., Baret, F., Myneni, R., Pragn\u00e8re, A., Knyazikhin, Y., Myneni, R.B., and Weiss, M. (2000). Investigation of a Model Inversion Technique to Estimate Canopy Biophysical Variables from Spectral and Directional Reflectance Data Investigation of a Model Inversion Technique to Estimate Canopy Biophysical Variables from Spectral and Directional Reflectance Data Investigation of a Model Inversion Technique to Estimate Canopy Biophysical Variables from Spectral and Directional Reflectance Data. Agron. EDP Sci., 20.","DOI":"10.1051\/agro:2000105"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wood, E.M., Pidgeon, A.M., Radeloff, V.C., and Keuler, N.S. (2012). Image Texture as a Remotely Sensed Measure of Vegetation Structure. Remote Sens. Environ., 516\u2013526.. No. 121.","DOI":"10.1016\/j.rse.2012.01.003"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1590\/S0044-59672005000200015","article-title":"Exploring TM Image Texture and Its Relationships with Biomass Estimation in Rond\u00f4nia, Brazilian Amazon","volume":"35","author":"Lu","year":"2005","journal-title":"Acta Amaz."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kukal, M.S., and Irmak, S. (2020). Light Interactions, Use and Efficiency in Row Crop Canopies under Optimal Growth Conditions. Agric. For. Meteorol., 284.","DOI":"10.1016\/j.agrformet.2019.107887"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:55:44Z","timestamp":1760172944000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,7]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030551"],"URL":"https:\/\/doi.org\/10.3390\/rs12030551","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,2,7]]}}}