{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T13:37:18Z","timestamp":1768829838580,"version":"3.49.0"},"reference-count":125,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T00:00:00Z","timestamp":1617667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018ZDPY07"],"award-info":[{"award-number":["2018ZDPY07"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value &lt; 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson\u2019s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0\u2013500 m and 2000\u20132500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and\/or replace coarse resolution satellite imagery in the estimation of RSM.<\/jats:p>","DOI":"10.3390\/ijgi10040233","type":"journal-article","created":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T21:44:47Z","timestamp":1617745487000},"page":"233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2163-4550","authenticated-orcid":false,"given":"Lina","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7763-1108","authenticated-orcid":false,"given":"Long","family":"Li","sequence":"additional","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"},{"name":"Department of Geography &amp; Earth System Science, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium"}]},{"given":"Ting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]},{"given":"Longqian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]},{"given":"Weiqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]},{"given":"Sai","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Humanities and Law, Jiangsu Ocean University, Cangwu Road 59, Lianyungang 222005, China"}]},{"given":"Longhua","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Research and Development, Shanghai Gongjing Environmental Protection Co., Ltd., Yuanjiang Road 525, Shanghai 201100, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S0022-1694(00)00405-4","article-title":"On the structure of soil moisture time series in the context of land surface models","volume":"243","author":"Albertson","year":"2001","journal-title":"J. Hydrol."},{"key":"ref_2","first-page":"96","article-title":"Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models","volume":"64","author":"Spennemann","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.jhydrol.2013.12.045","article-title":"Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale","volume":"510","author":"Tayfur","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.agrformet.2018.10.010","article-title":"A water-energy balance approach for multi-category drought assessment across globally diverse hydrological basins","volume":"264","author":"Zhang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","first-page":"1","article-title":"Satellite soil moisture for advancing our understanding of earth system processes and climate change","volume":"48","author":"Dorigo","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.rse.2017.10.016","article-title":"Information theoretic evaluation of satellite soil moisture retrievals","volume":"204","author":"Kumar","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.jhydrol.2017.07.049","article-title":"Performance of SMAP, AMSR-E and LAI for weekly agricultural drought forecasting over continental United States","volume":"553","author":"Liu","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s41976-019-00025-7","article-title":"A review of satellite-derived soil moisture and its usage for flood estimation","volume":"2","author":"Kim","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_9","first-page":"68","article-title":"Soil moisture sensing techniques for scheduling irrigation","volume":"11","author":"Singh","year":"2019","journal-title":"J. Soil Salin. Water Qual."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"897","DOI":"10.5194\/hess-23-897-2019","article-title":"Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data","volume":"23","author":"Zaussinger","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yuan, L., Li, L., Zhang, T., Chen, L., Zhao, J., Hu, S., Cheng, L., and Liu, W. (2020). Soil moisture estimation for the Chinese Loess Plateau using MODIS-derived ATI and TVDI. Remote Sens., 12.","DOI":"10.3390\/rs12183040"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.jhydrol.2013.12.008","article-title":"Soil moisture at watershed scale: Remote sensing techniques","volume":"516","author":"Fang","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_13","first-page":"673","article-title":"Farmland soil moisture inversion by synergizing optical and microwave remote sensing data","volume":"18","author":"Ma","year":"2014","journal-title":"J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.advwatres.2017.09.010","article-title":"Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons","volume":"109","author":"Karthikeyan","year":"2017","journal-title":"Adv. Water Resour."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.06.010","article-title":"A combination of DISPATCH downscaling algorithm with CLASS land surface scheme for soil moisture estimation at fine scale during cloudy days","volume":"184","author":"Djamai","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Piles, M., Ballabrera-Poy, J., and Mu\u00f1oz-Sabater, J. (2019). Dominant features of global surface soil moisture variability observed by the SMOS satellite. Remote Sens., 11.","DOI":"10.3390\/rs11010095"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.rse.2011.09.031","article-title":"Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia","volume":"120","author":"Sabel","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jhydrol.2018.05.051","article-title":"A soil moisture estimation framework based on the CART algorithm and its application in China","volume":"563","author":"Han","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.advwatres.2014.09.011","article-title":"Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia","volume":"74","author":"Dumedah","year":"2014","journal-title":"Adv. Water Resour."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"599","DOI":"10.3319\/TAO.2015.04.22.01(Hy)","article-title":"Dryness indices based on remotely sensed vegetation and land surface temperature for evaluating the soil moisture status in cropland-forest-dominant watersheds","volume":"26","author":"Moon","year":"2015","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_21","first-page":"221","article-title":"DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and AMSR-E data in Southeastern Australia","volume":"45","author":"Merlin","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.09.013","article-title":"Disaggregation of SMOS soil moisture over the Canadian Prairies","volume":"170","author":"Djamai","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"3783","DOI":"10.3390\/rs70403783","article-title":"Performance metrics for soil moisture downscaling methods: Application to DISPATCH data in Central Morocco","volume":"7","author":"Merlin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/JSTARS.2020.3043628","article-title":"Combined Sentinel-1A with Sentinel-2A to estimate soil moisture in farmland","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114618","DOI":"10.1016\/j.geoderma.2020.114618","article-title":"Estimation and evaluation of high spatial resolution surface soil moisture using multi-sensor multi-resolution approach","volume":"378","author":"Koley","year":"2020","journal-title":"Geoderma"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Palombo, A., Pascucci, S., Loperte, A., Lettino, A., Castaldi, F., Muolo, M.R., and Santini, F. (2019). Soil moisture retrieval by integrating TASI-600 airborne thermal data, WorldView 2 satellite data and field measurements: Petacciato case study. Sensors, 19.","DOI":"10.3390\/s19071515"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1080\/2150704X.2020.1730469","article-title":"Soil water content monitoring using joint application of PDI and TVDI drought indices","volume":"11","author":"Wang","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"895","DOI":"10.5194\/isprs-archives-XLII-3-W10-895-2020","article-title":"Remote sensing retrieval of soil moisture in Guangxi based on ATI and TVDI models","volume":"42","author":"Lu","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1080\/01431161.2014.919677","article-title":"Development of an ATI-NDVI method for estimation of soil moisture from MODIS data","volume":"35","author":"Lu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yuan, L., Li, L., Zhang, T., Chen, L., Zhao, J., Liu, W., Cheng, L., Hu, S., Yang, L., and Wen, M. (2021). Improving soil moisture estimation by identification of NDVI thresholds optimization: An application to the Chinese Loess Plateau. Remote Sens., 13.","DOI":"10.3390\/rs13040589"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2018.02.065","article-title":"Spatially enhanced passive microwave derived soil moisture: Capabilities and opportunities","volume":"209","author":"Sabaghy","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_34","first-page":"179","article-title":"Development of a novel machine vision procedure for rapid and non-contact measurement of soil moisture content","volume":"121","author":"Mollazade","year":"2018","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1016\/j.advwatres.2008.01.012","article-title":"Passive L-band microwave soil moisture retrieval error arising from topography in otherwise uniform scenes","volume":"31","author":"Sandells","year":"2008","journal-title":"Adv. Water Resour."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0341-8162(03)00065-1","article-title":"The effects of land use on soil moisture variation in the Danangou catchment of the Loess Plateau, China","volume":"54","author":"Fu","year":"2003","journal-title":"Catena"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.5194\/se-6-1157-2015","article-title":"Analysis of soil moisture condition under different land uses in the arid region of Horqin sandy land, northern China","volume":"6","author":"Niu","year":"2015","journal-title":"Solid Earth"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.foreco.2018.05.011","article-title":"Evapotranspiration partitioning and its implications for plant water use strategy: Evidence from a black locust plantation in the semi-arid Loess Plateau, China","volume":"424","author":"Jiao","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.jhydrol.2018.01.065","article-title":"A field evaluation of soil moisture modelling with the Soil, Vegetation, and Snow (SVS) land surface model using evapotranspiration observations as forcing data","volume":"558","author":"Maheu","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1007\/BF02915509","article-title":"Retrieving soil water contents from soil temperature measurements by using linear regression","volume":"20","author":"Xu","year":"2003","journal-title":"Adv. Atmos. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2018.02.007","article-title":"A new index to quantify dried soil layers in water-limited ecosystems: A case study on the Chinese Loess Plateau","volume":"322","author":"Wang","year":"2018","journal-title":"Geoderma"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.jhydrol.2017.10.045","article-title":"Similarity of the temporal pattern of soil moisture across soil profile in karst catchments of southwestern China","volume":"555","author":"Li","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/08120099.2019.1620855","article-title":"Integrating satellite soil-moisture estimates and hydrological model products over Australia","volume":"67","author":"Khaki","year":"2020","journal-title":"Aust. J. Earth Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jhydrol.2017.01.036","article-title":"Performance of AMSR_E soil moisture data assimilation in CLM4.5 model for monitoring hydrologic fluxes at global scale","volume":"547","author":"Liu","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2014.07.005","article-title":"The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau","volume":"152","author":"Zhao","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.advwatres.2009.11.007","article-title":"Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals","volume":"33","author":"Li","year":"2010","journal-title":"Adv. Water Resour."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bayat, A.T., Schonbrodt-Stitt, S., Nasta, P., Ahmadian, N., Conrad, C., Bogena, H.R., Vereecken, H., Jakobi, J., Baatz, R., and Romano, N. (2020, January 4\u20136). Mapping near-surface soil moisture in a Mediterranean agroforestry ecosystem using Cosmic-Ray Neutron Probe and Sentinel-1 Data. Proceedings of the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento, Italy.","DOI":"10.1109\/MetroAgriFor50201.2020.9277557"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"125406","DOI":"10.1016\/j.jhydrol.2020.125406","article-title":"Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA","volume":"590","author":"Liu","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.jhydrol.2014.04.068","article-title":"Assessing artificial neural networks and statistical methods for infilling missing soil moisture records","volume":"515","author":"Dumedah","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_51","first-page":"114","article-title":"On the synergy of SMAP, AMSR2 and SENTINEL-1 for retrieving soil moisture","volume":"65","author":"Santi","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jhydrol.2008.08.012","article-title":"On the relevance of using artificial neural networks for estimating soil moisture content","volume":"362","author":"Elshorbagy","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rama.2018.01.001","article-title":"Nondestructive estimation of standing crop and fuel moisture content in tallgrass prairie","volume":"71","author":"Sharma","year":"2018","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1080\/07038992.2016.1175928","article-title":"Soil moisture retrieval over a semiarid area by means of PCA dimensionality reduction","volume":"42","author":"Zhang","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"535","DOI":"10.5589\/m11-065","article-title":"Polarimetric RADARSAT-2 imagery for soil moisture retrieval in alpine areas","volume":"37","author":"Pasolli","year":"2012","journal-title":"Can. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.jhydrol.2016.04.021","article-title":"Evaluating uncertainties in multi-layer soil moisture estimation with support vector machines and ensemble Kalman filtering","volume":"538","author":"Liu","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.agwat.2018.09.004","article-title":"Spatial distribution of soil moisture estimates using a multiple linear regression model and Korean geostationary satellite (COMS) data","volume":"213","author":"Lee","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_58","first-page":"1","article-title":"Estimation of the retention and availability of water in soils of the State of Santa Catarina","volume":"42","author":"Bortolini","year":"2018","journal-title":"Rev. Bras. Ci\u00eancia Do Solo"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"125840","DOI":"10.1016\/j.jhydrol.2020.125840","article-title":"Van Der Root zone soil moisture estimation with Random Forest","volume":"593","author":"Carranza","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1016\/j.asr.2016.11.032","article-title":"Soil moisture retrieval using ground based bistatic scatterometer data at X-band","volume":"59","author":"Gupta","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.rse.2016.09.011","article-title":"A regional scale performance evaluation of SMOS and ESA-CCI soil moisture products over India with simulated soil moisture from MERRA-Land","volume":"186","author":"Chakravorty","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1080\/01431161.2013.875237","article-title":"Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data","volume":"35","author":"Leng","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Liu, M., Huang, C., Wang, L., Zhang, Y., and Luo, X. (2020). Short-term soil moisture forecasting via Gaussian process regression with sample selection. Water, 12.","DOI":"10.3390\/w12113085"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"125865","DOI":"10.1016\/j.jhydrol.2020.125865","article-title":"A nonparametric sequential data assimilation scheme for soil moisture flow","volume":"593","author":"Wang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"14646","DOI":"10.3390\/rs71114646","article-title":"Retrieval of soil water content in saline soils from emitted thermal infrared spectra using partial linear squares regression","volume":"7","author":"Xu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.chemosphere.2017.07.131","article-title":"Development of a predictive model for lead, cadmium and fluorine soil-water partition coefficients using sparse multiple linear regression analysis","volume":"186","author":"Nakamura","year":"2017","journal-title":"Chemosphere"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0341-8162(03)00064-X","article-title":"Spatiotemporal prediction of soil moisture content using multiple-linear regression in a small catchment of the Loess Plateau, China","volume":"54","author":"Qiu","year":"2003","journal-title":"Catena"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"103874","DOI":"10.1016\/j.chemolab.2019.103874","article-title":"Model for estimation of total nitrogen content in sandalwood leaves based on nonlinear mixed effects and dummy variables using multispectral images","volume":"195","author":"Chen","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.jaridenv.2009.08.003","article-title":"Spatial prediction of soil moisture content using multiple-linear regressions in a gully catchment of the Loess Plateau, China","volume":"74","author":"Qiu","year":"2010","journal-title":"J. Arid Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1080\/00103624.2020.1822385","article-title":"Comparison of stepwise multilinear regressions, artificial neural network, and genetic algorithm-based neural network for prediction the plant available water of unsaturated soils in a semi-arid region of Iran (case study: Chaharmahal Bakhtiari province)","volume":"51","author":"Soleimani","year":"2020","journal-title":"Commun. Soil Sci. Plant. Anal."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"104715","DOI":"10.1016\/j.catena.2020.104715","article-title":"Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran","volume":"194","author":"Dahmardeh","year":"2020","journal-title":"Catena"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1002\/qre.1644","article-title":"El Phase II multiple linear regression profile with small sample size","volume":"31","author":"Mahmoud","year":"2015","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Jenkins, D.G., and Quintana-Ascencio, P.F. (2020). A solution to minimum sample size for regressions. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0229345"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.catena.2017.06.006","article-title":"Long-term temporal variations of soil water content under different vegetation types in the Loess Plateau, China","volume":"158","author":"Zhao","year":"2017","journal-title":"Catena"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.gloplacha.2012.12.014","article-title":"Evolution of ecosystem services in the Chinese Loess Plateau under climatic and land use changes","volume":"101","author":"Su","year":"2013","journal-title":"Glob. Planet. Chang."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.5194\/bg-13-4735-2016","article-title":"Moderate topsoil erosion rates constrain the magnitude of the erosion-induced carbon sink and agricultural productivity losses on the Chinese Loess Plateau","volume":"13","author":"Zhao","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10113-010-0127-3","article-title":"Spatiotemporal variation in rainfall erosivity on the Chinese Loess Plateau during the period 1956\u20132008","volume":"11","author":"Xin","year":"2011","journal-title":"Reg. Environ. Chang."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agwat.2012.10.019","article-title":"Estimation of volumetric soil water content over the Liudaogou river basin of the Loess Plateau using the SWEST method with spatial and temporal variability","volume":"118","author":"Tasumi","year":"2013","journal-title":"Agric. Water Manag."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.geoderma.2011.02.008","article-title":"Spatio-temporal variability behavior of land surface soil water content in shrub- and grass-land","volume":"162","author":"Hu","year":"2011","journal-title":"Geoderma"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1080\/01431160903527421","article-title":"Estimating soil moisture using temperature-vegetation dryness index (TVDI) in the Huang-huai-hai (HHH) plain","volume":"32","author":"Chen","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.2298\/TSCI1305375H","article-title":"Study on soil moisture by thermal infrared data","volume":"17","author":"He","year":"2013","journal-title":"Therm. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1007\/s11629-016-4262-2","article-title":"An improved temperature vegetation dryness index (iTVDI) and its applicability to drought monitoring","volume":"14","author":"Yang","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1117\/12.510984","article-title":"Assessing spatial variability of soil water content through thermal inertia and NDVI","volume":"5232","author":"Claps","year":"2004","journal-title":"Remote Sens. Agric. Ecosyst. Hydrol. V"},{"key":"ref_84","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_85","doi-asserted-by":"crossref","unstructured":"Capodici, F., Cammalleri, C., Francipane, A., Ciraolo, G., la Loggia, G., and Maltese, A. (2020). Soil water content diachronic mapping: An FFT frequency analysis of a temperature\u2013vegetation index. Geoscience, 10.","DOI":"10.3390\/geosciences10010023"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.advwatres.2015.05.017","article-title":"A particle batch smoother for soil moisture estimation using soil temperature observations","volume":"83","author":"Dong","year":"2015","journal-title":"Adv. Water Resour."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1080\/00031305.1991.10475808","article-title":"Dummy variables in stepwise regression","volume":"45","author":"Cohen","year":"1991","journal-title":"Am. Stat."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1177\/1536867X19830921","article-title":"Speaking stata: How best to generate indicator or dummy variables","volume":"19","author":"Cox","year":"2019","journal-title":"Stata J."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1002\/esp.4284","article-title":"Dating lava flows of tropical volcanoes by means of spatial modeling of vegetation recovery","volume":"43","author":"Li","year":"2018","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Chen, M., Zhang, Y., Yao, Y., Lu, J., Pu, X., Hu, T., and Wang, P. (2020). Evaluation of the OPTRAM model to retrieve soil moisture in the Sanjiang Plain of northeast China. Earth Space Sci., 7.","DOI":"10.1029\/2020EA001108"},{"key":"ref_91","unstructured":"(2019, February 21). Level-1 and Atmosphere Archive and Distribution System (LAADS) Distributed Archive Center (DAAC), Available online: https:\/\/ladsweb.modaps.eosdis.nasa.gov\/."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rse.2013.08.022","article-title":"Using satellite based soil moisture to quantify the water driven variability in NDVI: A case study over mainland Australia","volume":"140","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.05.010","article-title":"Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought","volume":"152","author":"Wagle","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1071\/WF19193","article-title":"Soil moisture as an indicator of growing-season herbaceous fuel moisture and curing rate in grasslands","volume":"30","author":"Sharma","year":"2020","journal-title":"Int. J. Wildland Fire"},{"key":"ref_95","first-page":"110","article-title":"Validation and trend analysis of ECV soil moisture data on cropland in North China Plain during 1981\u20132010","volume":"48","author":"Wang","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_96","first-page":"96","article-title":"Evaluating ESA CCI soil moisture in East Africa","volume":"48","author":"McNally","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_97","first-page":"102189","article-title":"Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data","volume":"93","author":"Xin","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, X., Chen, L., Chen, L., Zhang, Y., and Liu, Y. (2020). Estimating urban vegetation biomass from Sentinel-2A image data. Forests, 11.","DOI":"10.3390\/f11020125"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, L., Chen, L., Chen, L., and Shen, Z. (2018). Improving ASTER GDEM accuracy using land use-based linear regression methods: A case study of Lianyungang, East China. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040145"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.advwatres.2014.07.012","article-title":"Characterization of Ethiopian mega hydrogeological regimes using GRACE, TRMM and GLDAS datasets","volume":"74","author":"Awange","year":"2014","journal-title":"Adv. Water Resour."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.catena.2018.01.020","article-title":"Soil moisture variations at different topographic domains and land use types in the semi-arid Loess Plateau, China","volume":"165","author":"Yu","year":"2018","journal-title":"Catena"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.biosystemseng.2017.06.001","article-title":"Effects of landscape positions on soil resistance to rill erosion in a small catchment on the Loess Plateau","volume":"160","author":"Geng","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.1016\/j.rse.2011.07.017","article-title":"A proposed extension to the soil moisture and ocean salinity level 2 algorithm for mixed forest and moderate vegetation pixels","volume":"115","author":"Panciera","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Raoult, N., Delorme, B., Ottl\u00e9, C., Peylin, P., Bastrikov, V., Maugis, P., and Polcher, J. (2018). Confronting soil moisture dynamics from the ORCHIDEE land surface model with the ESA-CCI product: Perspectives for data assimilation. Remote Sens., 10.","DOI":"10.3390\/rs10111786"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.advwatres.2017.12.019","article-title":"Patterns of precipitation and soil moisture extremes in Texas, US: A complex network analysis","volume":"112","author":"Sun","year":"2018","journal-title":"Adv. Water Resour."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.jhydrol.2014.01.041","article-title":"Precipitation, soil moisture and runoff variability in a small river catchment (Ardeche, France) during HyMeX Special Observation Period 1","volume":"516","author":"Huza","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_107","unstructured":"(2019, January 11). China Meteorological Data Service Center. Available online: http:\/\/data.cma.cn\/en."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Cenci, L., Pulvirenti, L., Boni, G., and Pierdicca, N. (2018). Defining a trade-off between spatial and temporal resolution of a geosynchronous SAR mission for soil moisture monitoring. Remote Sens., 10.","DOI":"10.3390\/rs10121950"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Cheng, L., Li, L., Chen, L., Hu, S., Yuan, L., Liu, Y., Cui, Y., and Zhang, T. (2019). Spatiotemporal variability and influencing factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014\u20132017 period. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16193522"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.catena.2012.10.006","article-title":"Responses of soil moisture in different land cover types to rainfall events in a re-vegetation catchment area of the Loess Plateau, China","volume":"101","author":"Wang","year":"2013","journal-title":"Catena"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.ecolind.2018.07.058","article-title":"Spatial and temporal variations in surface soil moisture and vegetation cover in the Loess Plateau from 2000 to 2015","volume":"95","author":"Wang","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.amc.2014.12.050","article-title":"Stepwise regression data envelopment analysis for variable reduction","volume":"253","author":"Sharma","year":"2015","journal-title":"Appl. Math. Comput."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"11195","DOI":"10.1021\/es301948k","article-title":"Development of land use regression models for PM2.5, PM2.5 absorbance, PM10 and PMcoarse in 20 European study areas; Results of the ESCAPE project","volume":"46","author":"Eeftens","year":"2012","journal-title":"Environ. Sci. Technol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.3390\/land3031015","article-title":"Investigation of the dominant factors influencing the ERA15 temperature increments at the subtropical and temperate belts with a focus over the Eastern Mediterranean Region","volume":"3","author":"Baharad","year":"2014","journal-title":"Land"},{"key":"ref_115","unstructured":"Lewis-Beck, M., Bryman, A., and Futing Liao, T. (2012). Stepwise Regression. SAGE Encyclopedia of Social Science Research Methods, SAGE."},{"key":"ref_116","first-page":"148","article-title":"Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC","volume":"67","author":"Alavipanah","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.econlet.2019.01.021","article-title":"Estimation of Lorenz curves based on dummy variable regression","volume":"177","author":"Wang","year":"2019","journal-title":"Econ. Lett."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1080\/02664763.2015.1092711","article-title":"On regression modelling with dummy variables versus separate regressions per group: Comment on Holgersson et al","volume":"43","author":"Holgersson","year":"2016","journal-title":"J. Appl. Stat."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Chen, D., Huang, X., Zhang, S., and Sun, X. (2017). Biomass modeling of larch (Larix spp.) plantations in China based on the mixed model, dummy variable model, and Bayesian hierarchical model. Forests, 8.","DOI":"10.3390\/f8080268"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Jiao, Q., Li, R., Wang, F., Mu, X., Li, P., and An, C. (2016). Impacts of re-vegetation on surface soil moisture over the Chinese Loess Plateau based on remote sensing datasets. Remote Sens., 8.","DOI":"10.3390\/rs8020156"},{"key":"ref_121","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_122","doi-asserted-by":"crossref","first-page":"112277","DOI":"10.1016\/j.rse.2020.112277","article-title":"Using SMAP Level-4 soil moisture to constrain MOD16 evapotranspiration over the contiguous USA","volume":"255","author":"Brust","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"23953","DOI":"10.1007\/s11356-017-9974-5","article-title":"Influence of soil physical properties and vegetation coverage at different slope aspects in a reclaimed dump","volume":"24","author":"Pan","year":"2017","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_124","first-page":"102156","article-title":"Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements","volume":"91","author":"Xu","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-019-8111-9","article-title":"Study of the desertification index based on the albedo-MSAVI feature space for semi-arid steppe region","volume":"78","author":"Wu","year":"2019","journal-title":"Environ. Earth Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/4\/233\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:58:44Z","timestamp":1760363924000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/4\/233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,6]]},"references-count":125,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["ijgi10040233"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10040233","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,6]]}}}