{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T15:47:13Z","timestamp":1771688833991,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T00:00:00Z","timestamp":1565568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program Key Project","award":["2018YFC1506502"],"award-info":[{"award-number":["2018YFC1506502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is very important to analyze and monitor agricultural drought to obtain high temporal-spatial resolution soil moisture products. To overcome the deficiencies of passive microwave soil moisture products with low resolution, we construct a spatial fusion downscaling model (SFDM) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. To eliminate the inconsistencies in soil depth and time among different microwave soil moisture products (Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) and its successor (AMSR2) and the Soil Moisture Ocean Salinity (SMOS)), a time series reconstruction of the difference decomposition (TSRDD) method is developed to create long-term multisensor soil moisture datasets. Overall, the downscaled soil moisture (SM) products were consistent with the in situ measurements (R &gt; 0.78) and exhibited a low root mean square error (RMSE &lt; 0.10 m3\/m3), which indicates good accuracy throughout the time series. The downscaled SM data at a 1-km spatial resolution were used to analyze the spatiotemporal patterns and monitor abnormal conditions in the soil water content across North East China (NEC) between 2002 and 2018. The results showed that droughts frequently appeared in western North East China and southwest of the Greater Khingan Range, while drought centers appeared in central North East China. Waterlogging commonly appeared in low-terrain areas, such as the Songnen Plain. Seasonal precipitation and temperature exhibited distinct interdecadal characteristics that were closely related to the occurrence of extreme climatic events. Abnormal SM levels were often accompanied by large meteorological and natural disasters (e.g., the droughts of 2008, 2015, and 2018 and the flooding events of 2003 and 2013). The spatial distribution of drought in this region during the growing season shows that the drought-affected area is larger in the west than in the east and that the semiarid boundary extends eastward and southward.<\/jats:p>","DOI":"10.3390\/s19163527","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T04:31:21Z","timestamp":1565670681000},"page":"3527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Long-Term Spatiotemporal Variations in Soil Moisture in North East China Based on 1-km Resolution Downscaled Passive Microwave Soil Moisture Products"],"prefix":"10.3390","volume":"19","author":[{"given":"Xiangjin","family":"Meng","sequence":"first","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-8428","authenticated-orcid":false,"given":"Kebiao","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Geography, South China Normal University, Guangzhou 510631, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing 100101, China"}]},{"given":"Fei","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, China"}]},{"given":"Xinyi","family":"Shen","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA"}]},{"given":"Tongren","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing 100101, China"}]},{"given":"Mengmeng","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,12]]},"reference":[{"key":"ref_1","unstructured":"(2012, November 13). Global Climate Observing System (GCOS). Available online: http:\/\/www.wmo.int\/pages\/prog\/gcos\/Publications\/gcos-154.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1109\/TGRS.2012.2184548","article-title":"The SMOS soil moisture retrieval algorithm","volume":"50","author":"Kerr","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/LGRS.2008.2002754","article-title":"Role of passive microwave remote sensing in improving flood forecasts","volume":"6","author":"Bindlish","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2014.882564","article-title":"The responses of vegetation water content (EWT) and assessment of drought monitoring along a coastal region using remote sensing","volume":"51","author":"Gao","year":"2014","journal-title":"GISci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1111\/jfr3.12037","article-title":"Application of spaceborne synthetic aperture radar data for extraction of soil moisture and its use in hydrological modelling at Gottleuba Catchment, Saxony, Germany","volume":"7","author":"Elbialy","year":"2014","journal-title":"J. Flood Risk Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/TGRS.2015.2462074","article-title":"Spatial downscaling of global satellite soil moisture data using temperature vegetation dryness index","volume":"1","author":"Peng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/ngeo2646","article-title":"Drought in the anthropocene","volume":"9","author":"Gleeson","year":"2016","journal-title":"Nat. Geosci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lakshmi, V. (2013). Remote sensing of soil moisture. ISRN Soil Sci., 33.","DOI":"10.1155\/2013\/424178"},{"key":"ref_9","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 Zone J., 16.","DOI":"10.2136\/vzj2016.10.0105"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1080\/01431169008955102","article-title":"Remote sensing of weather impacts on vegetation in nonhomogeneous areas","volume":"11","author":"Kogan","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/02757259409532220","article-title":"A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water","volume":"9","author":"Carlson","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"4599","DOI":"10.1080\/0143116031000156837","article-title":"Spaceborne soil moisture estimation at high resolution: A microwave\u2013optical\/IR synergistic approach","volume":"24","author":"Chauhan","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1007\/s11430-012-4444-x","article-title":"Progresses on microwave remote sensing of land surface parameters","volume":"55","author":"Shi","year":"2012","journal-title":"Sci. China Earth Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fang, B., Lakshmi, V., Bindlish, R., and Thomas, J.J. (2018). AMSR2 soil moisture downscaling using temperature and vegetation data. Remote Sens., 10.","DOI":"10.3390\/rs10101575"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.2136\/vzj2013.05.0093","article-title":"Evaluating bias\u2013corrected AMSR\u2013E soil moisture using in situ observations and model estimates","volume":"12","author":"Sridhar","year":"2013","journal-title":"Vadose Zone J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jhydrol.2011.12.034","article-title":"Validation of ground penetrating radar full\u2013waveform inversion for field scale soil moisture mapping","volume":"424","author":"Minet","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/0022-1694(95)02970-2","article-title":"Passive microwave remote sensing of soil moisture","volume":"184","author":"Njoku","year":"1996","journal-title":"J. Hydrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1080\/01431160701442104","article-title":"A method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM\/TMI data","volume":"29","author":"Mao","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","unstructured":"Wagner, W., Sabel, D., Doubkova, M., Bartsch, A., and Pathe, C. (2009, January 18\u201320). The potential of Sentinel-1 for monitoring soil moisture with a high spatial resolution at global scale. Proceedings of the Earth Observation and Water Cycle Science Symposium, Frascati, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.1109\/TGRS.2015.2390219","article-title":"A semi-physical microwave surface emission model for soil moisture retrieval","volume":"53","author":"Shen","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3931","DOI":"10.1109\/TGRS.2012.2228209","article-title":"Bare surface soil moisture estimation using double-angle and dual-polarization L-band radar data","volume":"51","author":"Shen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","unstructured":"Kerr, Y.H., Jacquette, E., Al Bitar, A., Cabot, F., Mialon, A., Richaume, P., Quesney, A., Berthon, L., and Wigneron, J. (2013). CATDS SMOS L3 Soil Moisture Retrieval Processor: Algorithm Theoretical Baseline Document (ATBD), CESBIO."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2434","DOI":"10.1016\/j.rse.2011.04.030","article-title":"Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing","volume":"115","author":"Champagne","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"8505","article-title":"Evaluation of soil moisture downscaling using a simple thermal based proxy\u2014The remedhus network (Spain) example","volume":"12","author":"Peng","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of methods for downscaling remotely sensed soil moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1109\/TGRS.2006.871199","article-title":"High-resolution change estimation of soil moisture using L-band radiometer and Rada observations made during the SMEX02 experiments","volume":"44","author":"Narayan","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.1109\/TGRS.2002.807008","article-title":"Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99","volume":"40","author":"Njoku","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1080\/014311697218584","article-title":"Soil moisture estimation under a vegetation cover: Combined active passive microwave remote sensing approach","volume":"18","author":"Chauhan","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rse.2004.05.003","article-title":"A combined passive\/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations","volume":"92","author":"Lee","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2012.05.009","article-title":"A microwave-optical\/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products","volume":"124","author":"Choi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/JSTARS.2013.2272053","article-title":"Retrieving high-resolution surface soil moisture by downscaling AMSR-E brightness temperature using MODIS LST and NDVI data","volume":"7","author":"Song","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1016\/j.rse.2010.05.007","article-title":"An improved algorithm for disaggregating microwave\u2013derived soil moisture based on red, near\u2013infrared and thermal\u2013infrared data","volume":"114","author":"Merlin","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1007\/s12665-016-5917-6","article-title":"Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches","volume":"75","author":"Im","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.3390\/s7081612","article-title":"An overview of the \u201cTriangle Method\u201d for estimating surface evapotranspiration and soil moisture from satellite imagery","volume":"7","author":"Carlson","year":"2007","journal-title":"Sensors"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2624","DOI":"10.1016\/j.rse.2010.05.033","article-title":"Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US","volume":"114","author":"Ray","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.eja.2011.07.003","article-title":"Progressive and active adaptations of cropping system to climate change in Northeast China","volume":"38","author":"Chen","year":"2012","journal-title":"Eur. J. Agron."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s10584-012-0594-2","article-title":"The effects of past climate change on the northern limits of maize planting in Northeast China","volume":"117","author":"Liu","year":"2013","journal-title":"Clim. Chang."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s11442-010-0495-0","article-title":"Simulation on the dynamics of forest area changes in northeast China","volume":"20","author":"Deng","year":"2010","journal-title":"J. Geogr. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/36.602541","article-title":"A physics\u2013based algorithm for retrieving land\u2013surface emissivity and temperature from EOS\/MODIS data","volume":"35","author":"Wan","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/TGRS.2002.808319","article-title":"Aqua: An Earth-observing satellite mission to examine water and other climate variables","volume":"41","author":"Parkinson","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1109\/JPROC.2009.2036869","article-title":"Global Change Observation Mission (GCOM) for monitoring carbon, water cycles, and climate change","volume":"98","author":"Imaoka","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.rse.2017.04.019","article-title":"A comparison of SMOS and AMSR2 soil moisture using representative sites of the OzNet monitoring network","volume":"195","author":"Yee","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/JPROC.2010.2043032","article-title":"The smos mission: New tool for monitoring key elements of the global water cycle","volume":"98","author":"Kerr","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1080\/10407780903463318","article-title":"Plume Dispersion Characteristics in Various Ambient Air Temperature Gradient Conditions","volume":"56","author":"Wee","year":"2009","journal-title":"Numer. Heat Transf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2018.05.034","article-title":"Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States","volume":"215","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3527\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:10:33Z","timestamp":1760188233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,12]]},"references-count":50,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["s19163527"],"URL":"https:\/\/doi.org\/10.3390\/s19163527","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,12]]}}}