{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:01:58Z","timestamp":1761490918437,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T00:00:00Z","timestamp":1576454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2019YFA0606900"],"award-info":[{"award-number":["2019YFA0606900"]}]},{"name":"National Natural Science Foundation for Distinguished Young Scholars of China","award":["41401479"],"award-info":[{"award-number":["41401479"]}]},{"name":"Project Supported by State Key Laboratory of Earth Surface Processes and Resource Ecology","award":["2017-FX-01(1)"],"award-info":[{"award-number":["2017-FX-01(1)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study proposes a cuboid model for soil moisture assessment. In the model, the three edges were the meteorological, soil, and vegetation feature parameters highly related to soil moisture, and the edge lengths represented the degree of influence of each feature parameter on soil moisture. Soil moisture is assessed by the cuboid diagonal, which is referred to as the cuboid soil moisture index (CSMI) in this paper. The model was applied and validated in the Huang-Huai-Hai Plain. The results showed that (1) the difference in land surface temperature between day and night (\u0394LST), land surface water index (LSWI), and accumulated precipitation (AP) were most closely correlated with soil moisture observation data in our study area, and were therefore selected as soil, crop, and meteorological system parameters to participate in CSMI calculations, respectively. (2) CSMI-1, with a cuboid length coefficient of 2\/1\/2, was the best model. The correlation of soil moisture derived from CSMI-1 with observed values was 0.64, 0.60, and 0.52 at depths of 10 cm, 20 cm, and 50 cm, respectively. (3) CSMI-1 had good applicability to the evaluation of soil moisture under different vegetation coverage. When the normalized difference vegetation index (NDVI)was 0\u20130.7, CSMI-1 was highly correlated with soil moisture at a significance level of 0.01. (4) The three-dimensional (3D) CSMI model can be easily converted to a two-dimensional (2D) model to adapt to different surface conditions (as long as the weight coefficient of one parameter is set to 0). Irrigation information (if available) can be considered as artificial recharge precipitation added in the AP to improve the accuracy of soil moisture inversion. This study provides a reference for soil moisture inversion using optical remote sensing images by integrating soil, vegetation, and meteorological feature parameters.<\/jats:p>","DOI":"10.3390\/rs11243034","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T02:59:01Z","timestamp":1576551541000},"page":"3034","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Cuboid Model for Assessing Surface Soil Moisture"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6660-2034","authenticated-orcid":false,"given":"Xiufang","family":"Zhu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2307-2715","authenticated-orcid":false,"given":"Yaozhong","family":"Pan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"School of Geographical Sciences, Qinghai Normal University, Xining 810016, China"}]},{"given":"Junxia","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.geoderma.2011.11.004","article-title":"Field scale spatiotemporal analysis of surface soil moisture for evaluating point-scale in situ networks","volume":"170","author":"Heathman","year":"2012","journal-title":"Geoderma"},{"key":"ref_2","first-page":"181","article-title":"Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index","volume":"28","author":"Holzman","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.pce.2015.02.009","article-title":"Surface soil moisture retrievals from remote sensing: Current status, products & future trends","volume":"83\u201384","author":"Petropoulos","year":"2015","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1175\/JHM-D-14-0039.1","article-title":"Monitoring agricultural risk in Canada using L-band passive microwave soil moisture from SMOS","volume":"16","author":"Champagne","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1007\/s11442-016-1297-9","article-title":"Agricultural drought monitoring: Progress, challenges, and prospects","volume":"26","author":"Liu","year":"2016","journal-title":"J. Geogr. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s11707-009-0023-7","article-title":"Satellite remote sensing applications for surface soil moisture monitoring: A review","volume":"3","author":"Wang","year":"2009","journal-title":"Front. Earth Sci. China"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/S0034-4257(01)00191-2","article-title":"Detecting vegetation leaf water content using reflectance in the optical domain","volume":"77","author":"Ceccato","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/S0034-4257(01)00347-9","article-title":"Relating soil surface moisture to reflectance","volume":"81","author":"Liu","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/0034-4257(85)90038-0","article-title":"On the analysis of thermal infrared imagery: The limited utility of apparent thermal inertia","volume":"18","author":"Price","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/36.739125","article-title":"Retrieval of land surface parameters using passive microwave measurements at 6\u201318 GHz","volume":"37","author":"Njoku","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/S0034-4257(00)00103-6","article-title":"Two-dimensional microwave interferometer retrieval capabilities over land surfaces (SMOS Mission)","volume":"73","author":"Wigneron","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"210","DOI":"10.3390\/rs1030210","article-title":"Soil Moisture retrieval from active spaceborne microwave observations: An evaluation of current techniques","volume":"1","author":"Brian","year":"2009","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.rse.2003.11.009","article-title":"Predicting water content using Gaussian model on soil spectra","volume":"89","author":"Whiting","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"309","article-title":"Monitoring vegetation systems in the great plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0273-1177(95)00079-T","article-title":"Application of vegetation index and brightness temperature for drought detection","volume":"15","author":"Kogan","year":"1995","journal-title":"Adv. Space Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1080\/01431169608949106","article-title":"Monitoring regional drought using the vegetation condition index","volume":"17","author":"Liu","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4393","DOI":"10.1080\/0143116031000084323","article-title":"Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India","volume":"24","author":"Singh","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1016\/j.jaridenv.2007.12.004","article-title":"Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran","volume":"72","author":"Bajgiran","year":"2008","journal-title":"J. Arid Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.agrformet.2009.11.015","article-title":"Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas","volume":"150","author":"Quiring","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index NDWI to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/S0034-4257(02)00037-8","article-title":"Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach","volume":"82","author":"Ceccato","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3987","DOI":"10.1080\/01431160802575653","article-title":"Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS vegetation index product","volume":"31","author":"Chandrasekar","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1080\/01431161.2013.779046","article-title":"VSDI: A visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing","volume":"34","author":"Zhang","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"L20405","DOI":"10.1029\/2007GL031021","article-title":"NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing","volume":"34","author":"Wang","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A generalized split-window algorithm for retrieving land-surface temperature from space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"McVicar, T.R., Jupp, D.L.B., Yang, X., and Tian, G. (1992, January 7). Linking regional water balance models with remote sensing. Proceedings of the 13th Asian Conference on Remote Sensing, Ulaanbaatar, Mongolia.","DOI":"10.1080\/10106049209354391"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3724\/SP.J.1010.2012.00283","article-title":"Soil moisture retrieval based on GA-BP neural networks algorithm","volume":"31","author":"Yu","year":"2012","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhuo, W., Huang, J., Li, L., Zhang, X., Ma, H., Gao, X., Huang, H., Xu, B., and Xiao, X. (2019). Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation. Remote Sens., 11.","DOI":"10.3390\/rs11131618"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.catena.2010.05.008","article-title":"Monitoring the effects of land use and cover type changes on soil moisture using remote-sensing data: A case study in China\u2019s Yongding River basin","volume":"82","author":"Wang","year":"2010","journal-title":"Catena"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(85)90044-6","article-title":"Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape","volume":"18","author":"Goward","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1175\/1520-0450(1995)034<0745:TRSOSS>2.0.CO;2","article-title":"Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models","volume":"34","author":"Gillies","year":"1995","journal-title":"J. Appl. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.3390\/rs6043170","article-title":"Surface soil water content estimation from thermal remote sensing based on the temporal variation of land surface temperature","volume":"6","author":"Zhang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.05.026","article-title":"Temperature-vegetation-soil moisture dryness index (TVMDI)","volume":"197","author":"Amani","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5230","DOI":"10.1109\/TGRS.2013.2287513","article-title":"A robust coinversion model for soil moisture retrieval from multisensor Data","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/S0022-1694(02)00016-1","article-title":"Intraseasonal dynamics of soil moisture variability within a small agricultural maize cropped field","volume":"261","author":"Hupet","year":"2002","journal-title":"J. Hydrol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1023\/B:LAND.0000030451.29571.8b","article-title":"An empirical approach towards improved spatial estimates of soil moisture for vegetation analysis","volume":"19","author":"Lookingbill","year":"2004","journal-title":"Landsc. Ecol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.jhydrol.2014.04.001","article-title":"Reprint of \u201cMoisture content behaviour in extensive green roofs during dry periods: The influence of vegetation and substrate characteristics\u201d","volume":"516","author":"Berretta","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Saaty, T.L., and Kearns, K.P. (1985). Systems characteristics and the analytic hierarchy process. Analytical Planning, Pergamon.","DOI":"10.1016\/B978-0-08-032599-6.50009-X"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7059","DOI":"10.1007\/s12517-014-1668-4","article-title":"Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS","volume":"8","author":"Rahmati","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.compag.2018.07.026","article-title":"Developing an integrated indicator for monitoring maize growth condition using remotely sensed vegetation temperature condition index and leaf area index","volume":"152","author":"Wang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TFUZZ.2011.2116029","article-title":"Analytic Hierarchy Process (AHP) in group decision making and its optimization with an allocation of information granularity","volume":"19","author":"Pedrycz","year":"2011","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.pec.2008.07.032","article-title":"Shared decision-making-transferring research into practice: The Analytic Hierarchy Process (AHP)","volume":"73","author":"Dolan","year":"2008","journal-title":"Patient Educ. Couns."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4490","DOI":"10.1109\/JSTARS.2013.2296899","article-title":"Mapping irrigated areas in China from remote sensing and statistical data","volume":"7","author":"Zhu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jhydrol.2007.03.022","article-title":"Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions","volume":"340","author":"Wang","year":"2007","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/3034\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:42:45Z","timestamp":1760190165000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/3034"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,16]]},"references-count":46,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11243034"],"URL":"https:\/\/doi.org\/10.3390\/rs11243034","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,12,16]]}}}