被动微波土壤水分遥感产品空间降尺度研究：方法、进展及挑战
Methods， progresses， and challenges of passive microwave soil moisture spatial downscaling
- 2022年26卷第9期 页码：1699-1722
纸质出版日期： 2022-09-07
DOI： 10.11834/jrs.20210515
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摘要
土壤水分不仅在地表水、能量以及碳循环中发挥着非常重要的作用，其时空变化也是影响和反映气候变化的关键因子。虽然被动微波遥感技术是目前监测大尺度范围土壤水分变化最为成熟的技术手段，但是其土壤水分产品空间分辨率往往较低（几十千米不等），不能满足区域和局地尺度的应用需求。鉴于这一问题，空间降尺度逐渐成为了提高被动微波土壤水分遥感产品空间分辨率的主要方式，也是当前遥感研究领域的热点之一。本文总结与分析了近20多年来国内外被动微波土壤水分遥感产品空间降尺度研究进展，系统归纳了经验性、半经验性和基于物理机理的3大类降尺度方法，并就各方法特征进行了详细说明，概述了各方法的优势和缺点。归纳而言，虽然被动微波土壤水分遥感产品空间降尺度方法众多，但可靠的高分辨率降尺度土壤水分产品仍较少，这与被动微波土壤水分遥感产品、降尺度关系模型方法以及降尺度辅助因子等有着直接的关联。未来相关研究应重点结合多源遥感数据建立适用性强、精度高的降尺度关系模型，进而实现时空无缝的高时空分辨率降尺度土壤水分产品的生产，为推动土壤水分遥感产品在农林业管理、自然灾害监测、水文过程分析等区域应用中提供支持。
Abstract
Soil Moisture (SM) plays an important role in the global water
energy
and carbon cycle
and its spatial distribution is also one of the key components of global climate change. Although passive microwave remote sensing technology is the most effective way of monitoring SM distribution at global scale
these kind products are generally limited by their low spatial resolution
which further prevents them from meeting the requirements of regional applications. On this basis
spatial downscaling has gradually become an alternative way of improving the spatial resolution of passive microwave SM products and a research hotspot in the field of remote sensing. Therefore
this paper reviewed and summarized the progresses on passive microwave SM spatial downscaling in the past 20 years. In terms of the downscaling methods
they can be divided into three key categories: empirical
semi-empirical
and physical model-based downscaling methods. The empirical downscaling method is simple and can easily achieve large-scale downscaling but lacks in physical background on the downscaling process. However
empirical methods have been widely used in passive microwave SM spatial downscaling study due to their simplicity and practicability. Physical model-based methods usually use data assimilation or/and land surface process models as the downscaling relational model. Usually
the process is complex
resulting in the low applicability of the physical model-based method
but this method can often obtain the downscaling results with good accuracy. The semi-empirical downscaling method generally can ensure the accuracy of the downscaling results and the operability of the method itself. However
the applicability of the semi-empirical method is still limited by its uncertainty related to the linking model for soil moisture expression and also some downscaling factors. Although numerous passive microwave SM spatial downscaling methods exist
the available downscaled SM products with good accuracy are limited. Currently
few passive microwave downscaling SM products are continuously produced
including the SMOS L4V5 SM product produced by BEC and the active and passive fusion SM products generated by NASA SMAP/Sentinel-1. Although the two kinds of downscaling SM products have the same spatial resolution (1 km)
both suffers from the poor spatial coverage. In general
there are still some problems and challenges should be considered for current spatial downscaling study for passive microwave SM product. Aiming at obtaining the downscaling results with high spatial resolution
good accuracy
seamless spatial coverage
and daily temporal resolution. The uncertainty in the downscaling model (the relationship between SM and the downscaling factors)
the uncertainty in the original passive microwave SM products and their incomplete spatial coverage
and the uncertainty in the downscaling factors (such as the influence from cloud cover and topography) are the issues should be well addressed. Overall
the development of the spatial downscaling study for passive microwave SM products will also provide more references and opportunities for promoting the application of SM products based on remote sensing in various fields including agro-forestry management
water resource assessment
and natural disaster monitoring.
关键词
被动微波土壤水分空间降尺度经验性方法半经验性方法基于物理机理的方法
Keywords
passive microwavesoil moisturespatial downscalingempirical methodsemi-empirical methodphysical model-based method
references
Abbaszadeh P, Moradkhani H and Zhan X. 2019. Downscaling SMAP radiometer soil moisture over the CONUS using an ensemble learning method. Water Resources Research, 55(1): 324-344 [DOI: 10.1029/2018WR023354http://dx.doi.org/10.1029/2018WR023354]
Albergel C, Dorigo W, Reichle R H, Balsamo G, De Rosnay P, Muñoz-Sabater J, Isaksen L, De Jeu R and Wagner W. 2013. Skill and global trend analysis of soil moisture from reanalyses and microwave remote sensing. Journal of Hydrometeorology, 14(4): 1259-1277 [DOI: 10.1175/jhm-d-12-0161.1http://dx.doi.org/10.1175/jhm-d-12-0161.1]
Alemohammad S H, Kolassa J, Prigent C, Aires F and Gentine P. 2018. Global downscaling of remotely-sensed soil moisture using Neural Networks. Hydrology and Earth System Sciences Discussions, 22(10): 5341-5356 [DOI: 10.5194/hess-2017-680http://dx.doi.org/10.5194/hess-2017-680]
Babaeian E, Sadeghi M, Jones S B, Montzka C, Vereecken H and Tuller M. 2019. Ground, proximal, and satellite remote sensing of soil moisture. Reviews of Geophysics, 57(2): 530-616 [DOI: 10.1029/2018RG000618http://dx.doi.org/10.1029/2018RG000618]
Berg A, Lintner B R, Findell K L, Malyshev S, Loikith P C and Gentine P. 2014. Impact of soil moisture-atmosphere interactions on surface temperature distribution. Journal of Climate, 27(21): 7976-7993 [DOI: 10.1175/jcli-d-13-00591.1http://dx.doi.org/10.1175/jcli-d-13-00591.1]
Bindlish R and Barros A P. 2002. Subpixel variability of remotely sensed soil moisture: an inter-comparison study of SAR and ESTAR. IEEE Transactions on Geoscience and Remote Sensing, 40(2): 326-337 [DOI: 10.1109/36.992792http://dx.doi.org/10.1109/36.992792]
Bindlish R, Jackson T J, Wood E, Gao H L, Starks P, Bosch D and Lakshmi V. 2003. Soil moisture estimates from TRMM microwave imager observations over the Southern United States. Remote Sensing of Environment, 85(4): 507-515 [DOI: 10.1016/S0034-4257(3)00052-Xhttp://dx.doi.org/10.1016/S0034-4257(3)00052-X]
Bircher S, Skou N, Jensen K H, Walker J P and Rasmussen L. 2012. A soil moisture and temperature network for SMOS validation in Western Denmark. Hydrology and Earth System Sciences, 16: 1445-1463 [DOI: 10.5194/hess-16-1445-2012http://dx.doi.org/10.5194/hess-16-1445-2012]
Calvet J, Fritz N, Froissard F, Suquia D, Petitpa A snd Piguet B. 2007. In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network//IEEE International Geoscience and Remote Sensing Symposium. Barcelona, Spain: 1196-1199 [DOI: 10.1109/IGARSS.2007.4423019http://dx.doi.org/10.1109/IGARSS.2007.4423019]
Cao Y P, Jin R, Han X J and Li X. 2011. A downscaling method for AMSR-E soil moisture using MODIS derived dryness index. Remote Sensing Technology and Application, 26(5): 590-597
曹永攀, 晋锐, 韩旭军, 李新. 2011. 基于MODIS和AMSR-E遥感数据的土壤水分降尺度研究. 遥感技术与应用, 26(5): 590-597 [DOI: 10.11873/j.issn.1004-0323.2011.5.590http://dx.doi.org/10.11873/j.issn.1004-0323.2011.5.590]
Carlson T N, Gillies R R and Schmugge T J. 1995. An interpretation of methodologies for indirect measurement of soil water content. Agricultural and Forest Meteorology, 77(3/4): 191-205 [DOI: 10.1016/0168-1923(95)02261-Uhttp://dx.doi.org/10.1016/0168-1923(95)02261-U]
Carlson T N and Petropoulos G P. 2019. A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle. International Journal of Remote Sensing, 40(20): 7716-7729 [DOI: 10.1080/01431161.2019.1601288http://dx.doi.org/10.1080/01431161.2019.1601288]
Chai S S, Walker J, Makarynskyy O, Kuhn M, Veenendaal B and West G. 2009. Use of soil moisture variability in artificial neural network retrieval of soil moisture. Remote Sensing, 2(1): 166-190 [DOI: 10.3390/rs2010166http://dx.doi.org/10.3390/rs2010166]
Chan S K, Bindlish R, O’Neill P E, Njoku E, Jackson T, Colliander A, Chen F, Burgin M, Dunbar S, Piepmeier J, Yueh S, Entekhabi D, Cosh M H, Caldwell T, Walker J, Wu X L, Berg A, Rowlandson T, Pacheco A, McNairn H, Thibeault M, Martínez-Fernández J, González-Zamora Á, Seyfried M, Bosch D, Starks P, Goodrich D, Prueger J, Palecki M, Small E E, Zreda M, Calvet J C, Crow W T and Kerr Y. 2016. Assessment of the SMAP passive soil moisture product. IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4994-5007 [DOI: 10.1109/TGRS.2016.2561938http://dx.doi.org/10.1109/TGRS.2016.2561938]
Chan S K, Bindlish R, O’Neill P, Jackson T, Njoku E, Dunbar S, Chaubell J, Piepmeier J, Yueh S, Entekhabi D, Colliander A, Chen F, Cosh M H, Caldwell T, Walker J, Berg A, McNairn H, Thibeault M, Martínez-Fernández J, Uldall F, Seyfried M, Bosch D, Starks P, Collins C H, Prueger J, Van Der Velde R, Asanuma J, Palecki M, Small E E, Zreda M, Calvet J, Crow W T and Kerr Y. 2018. Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of Environment, 204: 931-941 [DOI: 10.1016/j.rse.2017.08.025http://dx.doi.org/10.1016/j.rse.2017.08.025]
Chauhan N S, Miller S and Ardanuy P. 2003. Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach. International Journal of Remote Sensing, 24(22): 4599-4622 [DOI: 10.1080/0143116031000156837http://dx.doi.org/10.1080/0143116031000156837]
Chen L, Zhang C, Chang B and Lv P. 2019. Application of General Ts-NDVI space in farmland drought monitoring. Remote Sensing information, 34(5): 29-34
陈亮, 张超, 常斌, 吕鹏. 2019. 通用温度-植被指数特征空间农田干旱遥感监测. 遥感信息, 34(5): 29-34 [DOI: 10.3969/j.issn.1000-3177.2019.05.007http://dx.doi.org/10.3969/j.issn.1000-3177.2019.05.007]
Chen T, Zhuo G and La B. 2017. Study on soil moisture by remote sensing retrieval based on MODIS data in Eastern Naqu. Chinese Journal of Soil Science, 48(2): 298-303
陈涛, 卓嘎, 拉巴. 2017. 那曲东部土壤水分MODIS遥感反演研究. 土壤通报, 48(2): 298-303 [DOI: 10.19336/j.cnki.trtb.2017.02.07http://dx.doi.org/10.19336/j.cnki.trtb.2017.02.07]
Choi M and Hur Y. 2012. A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products. Remote Sensing of Environment, 124: 259-269 [DOI: 10.1016/j.rse.2012.05.009http://dx.doi.org/10.1016/j.rse.2012.05.009]
Colliander A, Jackson T J, Bindlish R, Chan S, Das N, Kim S B, Cosh M H, Dunbar R S, Dang L, Pashaian L, Asanuma J, Aida K, Berg A, Rowlandson T, Bosch D, Caldwell T, Caylor K, Goodrich D, Al Jassar H, Lopez-Baeza E, Martínez-Fernández J, González-Zamora A, Livingston S, McNairn H, Pacheco A, Moghaddam M, Montzka C, Notarnicola C, Niedrist G, Pellarin T, Prueger J, Pulliainen J, Rautiainen K, Ramos J, Seyfried M, Starks P, Su Z, Zeng Y, Van Der Velde R, Thibeault M, Dorigo W, Vreugdenhil M, Walker J P, Wu X, Monerris A, O'Neill P E, Entekhabi D, Njoku E G and Yueh S. 2017. Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment, 191: 215-231 [DOI: 10.1016/j.rse.2017.01.021http://dx.doi.org/10.1016/j.rse.2017.01.021]
Collow T W, Robock A, Basara J B and Illston B G. 2012. Evaluation of SMOS retrievals of soil moisture over the Central United States with currently available in situ observations. Journal of Geophysical Research: Atmospheres, 117(D9): D09113 [DOI: 10.1029/2011jd017095http://dx.doi.org/10.1029/2011jd017095]
Crow W T and Wood E F. 2002. the value of coarse-scale soil moisture observations for regional surface energy balance modeling. Journal of Hydrometeorology, 3(4): 467-482 [DOI: 10.1175/1525-7541(2002)003<0467:TVOCSS>2.0.CO;2http://dx.doi.org/10.1175/1525-7541(2002)003<0467:TVOCSS>2.0.CO;2]
Cui C Y, Xu J, Zeng J Y, Chen K S, Bai X J, Lu H, Chen Q and Zhao T J. 2018. Soil moisture mapping from satellites: an intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales. Remote Sensing, 10(1): 33 [DOI: 10.3390/rs10010033http://dx.doi.org/10.3390/rs10010033]
Dadap N C, Cobb A R, Hoyt A M, Harvey C F and Konings A G. 2019. Satellite soil moisture observations predict burned area in Southeast Asian peatlands. Environmental Research Letters, 14(9): 094014 [DOI: 10.1088/1748-9326/ab3891http://dx.doi.org/10.1088/1748-9326/ab3891]
Das K, Paul P K and Dobesova Z. 2015. Present status of soil moisture estimation by microwave remote sensing. Cogent Geoscience, 1(1): 1084669 [DOI: 10.1080/23312041.2015.1084669http://dx.doi.org/10.1080/23312041.2015.1084669]
Das N N, Entekhabi D, Dunbar R S, Njoku E G and Yueh S H. 2016. Uncertainty estimates in the SMAP combined active-passive downscaled brightness temperature. IEEE Transactions on Geoscience and Remote Sensing, 54(2): 640-650 [DOI: 10.1109/TGRS.2015.2450694http://dx.doi.org/10.1109/TGRS.2015.2450694]
Das N N, Entekhabi D, Dunbar R S, Chaubell M J, Colliander A, Yueh S, Jagdhuber T, Chen F, Crow W, O'Neill P E, Walker J P, Berg A, Bosch D D, Caldwell T, Cosh M H, Collins C H, Lopez-Baeza E and Thibeault M. 2019. The SMAP and Copernicus sentinel 1A/B microwave active-passive high resolution surface soil moisture product. Remote Sensing of Environment, 233: 111380 [DOI: 10.1016/j.rse.2019.111380http://dx.doi.org/10.1016/j.rse.2019.111380]
Das N N, Entekhabi D and Njoku E G. 2011. An algorithm for merging SMAP radiometer and radar data for high-resolution soil-moisture retrieval. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1504-1512 [DOI: 10.1109/TGRS.2010.2089526http://dx.doi.org/10.1109/TGRS.2010.2089526]
Djamai N, Magagi R, Goita K, Merlin O, Kerr Y and Walker A. 2015. Disaggregation of SMOS soil moisture over the Canadian prairies. Remote Sensing of Environment, 170, 255-268 [DOI: 10.1016/j.rse.2015.09.013http://dx.doi.org/10.1016/j.rse.2015.09.013]
Dong C H, Yang J, Zhang W J, Yang Z D, Lu N M, Shi J M, Zhang P, Liu Y J and Cai B. 2009. An overview of a new Chinese weather satellite FY-3A. Bulletin of the American Meteorological Society, 90(10): 1531-1544 [DOI: 10.1175/2009BAMS2798.1http://dx.doi.org/10.1175/2009BAMS2798.1]
Duan S B, Li Z L and Leng P. 2017. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data. Remote Sensing of Environment, 195: 107-117 [DOI: 10.1016/j.rse.2017.04.008http://dx.doi.org/10.1016/j.rse.2017.04.008]
Duan S B, Li Z L, Tang B H, Wu H and Tang R L. 2014. Generation of a time-consistent land surface temperature product from MODIS data. Remote Sensing of Environment, 140: 339-349 [DOI: 10.1016/j.rse.2013.09.003http://dx.doi.org/10.1016/j.rse.2013.09.003]
El Hajj M, Baghdadi N and Zribi M. 2019. Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands. International Journal of Applied Earth Observation and Geoinformation, 82: 101888 [DOI: 10.1016/j.jag.2019.05.021http://dx.doi.org/10.1016/j.jag.2019.05.021]
Entekhabi D, Njoku E G, O'Neill P E, Kellogg K H, Crow W T, Edelstein W N, Entin J K, Goodman S D, Jackson T J, Johnson J, Kimball J, Piepmeier J R, Koster R D, Martin N, McDonald K C, Moghaddam M, Moran S, Reichle R, Shi J C, Spencer M W, Thurman S W, Tsang L and Van Zyl J. 2010. The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5): 704-716 [DOI: 10.1109/jproc.2010.2043918http://dx.doi.org/10.1109/jproc.2010.2043918]
Entin J K, Robock A, Vinnikov K Y, Hollinger S E, Liu S X and Namkhai A. 2000. Temporal and spatial scales of observed soil moisture variations in the extratropics. Journal of Geophysical Research: Atmospheres, 105(D9): 11865-11877 [DOI: 10.1029/2000jd900051http://dx.doi.org/10.1029/2000jd900051]
Fan L, Al-Yaari A, Frappart F, Swenson J J, Xiao Q, Wen J G, Jin R, Kang J, Li X J, Fernandez-Moran R and Wigneron J P. 2019. Mapping soil moisture at a high resolution over mountainous regions by integrating in situ measurements, topography data, and modis land surface temperatures. Remote Sensing, 11(6): 656 [DOI: 10.3390/rs11060656http://dx.doi.org/10.3390/rs11060656]
Fang B, Lakshmi V, Bindlish R and Jackson T J. 2018. AMSR2 soil moisture downscaling using temperature and vegetation data. Remote Sensing, 10(10): 1575 [DOI: 10.3390/rs10101575http://dx.doi.org/10.3390/rs10101575]
Font J, Camps A, Borges A, Martín-Neira M, Boutin J, Reul N, Kerr Y H, Hahne A and Mecklenburg S. 2010. SMOS: the challenging sea surface salinity measurement from space. Proceedings of the IEEE, 98(5): 649-665 [DOI: 10.1109/JPROC.2009.2033096http://dx.doi.org/10.1109/JPROC.2009.2033096]
Fu P and Weng Q H. 2018. Variability in annual temperature cycle in the urban areas of the United States as revealed By MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 65-73 [DOI: 10.1016/j.isprsjprs.2018.09.003http://dx.doi.org/10.1016/j.isprsjprs.2018.09.003]
Gao H, Wood E F, Jackson T J, Drusch M and Bindlish R. 2006. Using TRMM/TMI to retrieve surface soil moisture over the Southern United States from 1998 to 2002. Journal of Hydrometeorology, 7(1): 23-38 [DOI: 10.1175/JHM473.1http://dx.doi.org/10.1175/JHM473.1]
Gruber A and Peng J. 2022. Remote sensing of soil moisture. Reference Module in Earth Systems and Environmental Sciences: Elsevier: 1-13[DOI: 10.1016/B978-0-12-822974-3.00019-7http://dx.doi.org/10.1016/B978-0-12-822974-3.00019-7]
Ikonen J, Vehviläinen J, Rautiainen K, Smolander T, Lemmetyinen J, Bircher S and Pulliainen J. 2016. The Sodankylä in situ soil moisture observation network: an example application of ESA CCI soil moisture product evaluation. Geoscientific Instrumentation, Methods and Data Systems, 5: 95-108 [DOI: 10.5194/gi-5-95-2016http://dx.doi.org/10.5194/gi-5-95-2016, 2016]
Im J, Park S, Rhee J, Baik J and Choi M. 2016. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environmental Earth Sciences, 75(15): 1120 [DOI: 10.1007/s12665-016-5917-6http://dx.doi.org/10.1007/s12665-016-5917-6]
Jackson T J, Bindlish R, Cosh M H, Zhao T J, Starks P J, Bosch D D, Seyfried M, Moran M S, Goodrich D C, Kerr Y H and Leroux D. 2012. Validation of Soil Moisture and Ocean Salinity (SMOS) soil moisture over watershed networks in the U.S. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1530-1543 [DOI: 10.1109/TGRS.2011.2168533http://dx.doi.org/10.1109/TGRS.2011.2168533]
Jackson T J. 2005. Remote sensing | soil moisture// Hillel D ed. Encyclopedia of Soils in the Environment. Oxford: Elsevier: 392-399 [DOI: 10.1016/B0-12-348530-4/00898-5http://dx.doi.org/10.1016/B0-12-348530-4/00898-5]
Jagdhuber T, Baur M, Akbar R, Das N N, Link M, He L and Entekhabi D. 2019. Estimation of active-passive microwave covariation Using SMAP and Sentinel-1 data. Remote Sensing of Environment, 225: 458-468 [DOI: 10.1016/j.rse.2019.03.021http://dx.doi.org/10.1016/j.rse.2019.03.021]
Jiménez-Muñoz J C, Sobrino J A, Skoković D, Mattar C and Cristóbal J. 2014. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, 11(10): 1840-1843 [DOI: 10.1109/LGRS.2014.2312032http://dx.doi.org/10.1109/LGRS.2014.2312032]
Jin Y, Ge Y, Wang J H, Heuvelink G B M and Wang L. 2018a. Geographically weighted area-to-point Regression Kriging for spatial downscaling in remote sensing. Remote Sensing, 10(4): 579 [DOI: 10.3390/rs10040579http://dx.doi.org/10.3390/rs10040579]
Jin Y, Ge Y, Wang J H, Chen Y H, Heuvelink G B M and Atkinson P M. 2018b. Downscaling AMSR-2 soil moisture data with geographically weighted area-to-area Regression Kriging. IEEE Transactions on Geoscience and Remote Sensing, 56(4): 2362-2376 [DOI: 10.1109/TGRS.2017.2778420http://dx.doi.org/10.1109/TGRS.2017.2778420]
Jin Y, Ge Y, Wang J H and Heuvelink G B M. 2018c. Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product. International Journal of Applied Earth Observation and Geoinformation, 68: 8-19 [DOI: 10.1016/j.jag.2018.01.010http://dx.doi.org/10.1016/j.jag.2018.01.010]
Kaheil Y H, Gill M K, Mckee M, Bastidas L A and Rosero E. 2008. Downscaling and assimilation of surface soil moisture using ground truth measurements. IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1375-1384 [DOI: 10.1109/TGRS.2008.916086http://dx.doi.org/10.1109/TGRS.2008.916086]
Kang C S, Zhao T J, Shi J C, Cosh M H, Chen Y Y, Starks P J, Collins C H, Wu S L, Sun R J and Zheng J Y. 2021. Global soil moisture retrievals from the Chinese FY-3D microwave radiation imager. IEEE Transactions on Geoscience and Remote Sensing, 59(5): 4018-4052 [DOI: 10.1109/TGRS.2020.3019408http://dx.doi.org/10.1109/TGRS.2020.3019408]
Karthikeyan L, Pan M, Wanders N, Kumar D N and Wood E F. 2017. Four decades of microwave satellite soil moisture observations: Part 1. a review of retrieval algorithms. Advances in Water Resources, 109: 106-120 [DOI: 10.1016/j.advwatres.2017.09.006http://dx.doi.org/10.1016/j.advwatres.2017.09.006]
Kerr Y H, Waldteufel P, Richaume P, Wigneron J P, Ferrazzoli P, Mahmoodi A, Al Bitar A, Cabot F, Gruhier C, Juglea S E, Leroux D, Mialon A and Delwart S. 2012. The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1384-1403 [DOI: 10.1109/TGRS.2012.2184548http://dx.doi.org/10.1109/TGRS.2012.2184548]
Kerr Y H, Waldteufel P, Wigneron J P, Delwart S, Cabot F, Boutin J, Escorihuela M J, Font J, Reul N, Gruhier C, Juglea S E, Drinkwater M R, Hahne A, Martín-Neira M and Mecklenburg S. 2010. The SMOS mission: new tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 98(5): 666-687 [DOI: 10.1109/JPROC.2010.2043032http://dx.doi.org/10.1109/JPROC.2010.2043032]
Kim G and Barros A P. 2002. Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data. Remote Sensing of Environment, 83(3): 400-413 [DOI: 10.1016/S0034-4257(02)00044-5http://dx.doi.org/10.1016/S0034-4257(02)00044-5]
Kim J and Hogue T S. 2012. Improving spatial soil moisture representation through integration of AMSR-E and MODIS products. IEEE Transactions on Geoscience and Remote Sensing, 50(2): 446-460 [DOI: 10.1109/TGRS.2011.2161318http://dx.doi.org/10.1109/TGRS.2011.2161318]
Kolassa J, Reichle R H, Liu Q, Alemohammad S H, Gentine P, Aida K, Asanuma J, Bircher S, Caldwell T, Colliander A, Cosh M, Holifield Collins C, Jackson T J, Martínez-Fernández J, McNairn, H, Pacheco A, Thibeault M and Walker J P. 2018. Estimating surface soil moisture from SMAP observations using a Neural Network technique. Remote Sensing of Environment, 204, 43-59 [DOI: 10.1016/j.rse.2017.10.045http://dx.doi.org/10.1016/j.rse.2017.10.045]
Japan Aerospace Exploration Agency Earth Observation Research Center . 2013. Descriptions of GCOM-W1 AMSR2 Level 1R and Level 2 Algorithms. [online] Available: http://suzaku.eorc.jaxa.jp/GCOM_W/data/doc/NDX-120015A.pdf (2013)http://suzaku.eorc.jaxa.jp/GCOM_W/data/doc/NDX-120015A.pdf(2013)
Kolassa J, Aires F, Polcher J, Prigent C, Jimenez C and Pereira J M. 2013. Soil moisture retrieval from multi-instrument observations: information content analysis and retrieval methodology. Journal of Geophysical Research: Atmospheres, 118(10): 4847-4859 [DOI: 10.1029/2012JD018150http://dx.doi.org/10.1029/2012JD018150]
Leroux D J, Das N N, Entekhabi D, Colliander A, Njoku E, Jackson T J and Yueh S. 2016. Active-Passive soil moisture retrievals during the SMAP validation experiment 2012. IEEE Geoscience and Remote Sensing Letters, 13(4): 475-479 [DOI: 10.1109/LGRS.2015.2491643http://dx.doi.org/10.1109/LGRS.2015.2491643]
Li J H, Wang S S, Gunn G, Joosse P and Russell H A J. 2018. A model for downscaling SMOS soil moisture using sentinel-1 SAR data. International Journal of Applied Earth Observation and Geoinformation, 72: 109-121 [DOI: 10.1016/j.jag.2018.07.012http://dx.doi.org/10.1016/j.jag.2018.07.012]
Li Z J, Chen J P, Liu Y M, Yao X L and Yu J S. 2020. Soil moisture retrieval from remote sensing. Journal of Beijing Normal University (Natural Science), 56(3): 474-481
李占杰, 陈基培, 刘艳民, 姚晓磊, 鱼京善. 2020. 土壤水分遥感反演研究进展. 北京师范大学学报(自然科学版), 56(3): 474-481 [DOI: 10.12202/j.0476-0301.2020231http://dx.doi.org/10.12202/j.0476-0301.2020231]
Li Z L, Tang R L, Wan Z M, Bi Y Y, Zhou C H, Tang B H, Yan G J and Zhang X Y. 2009. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9(5): 3801-3853 [DOI: 10.3390/s90503801http://dx.doi.org/10.3390/s90503801]
Li Z L, Leng P, Zhou C, Chen K S, Zhou F C and Shang G F. 2021. Soil moisture retrieval from remote sensing measurements: current knowledge and directions for the future. Earth-Science Reviews, 218: 103673 [DOI: 10.1016/j.earscirev.2021.103673].
Lievens H, Reichle R H, Liu Q, De Lannoy G J M, Dunbar R S, Kim S B, Das N N, Cosh M, Walker J P and Wagner W. 2017. Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters, 44(12): 6145-6153 [DOI: 10.1002/2017GL073904http://dx.doi.org/10.1002/2017GL073904]
Ling Z W, He L B and Zeng H. 2014. Evaluating the performance of the UCLA method for spatially downscaling soil moisture products using three Ts/VI indices. Chinese Journal of Applied Ecology, 25(2): 545-552
凌自苇, 何龙斌, 曾辉. 2014. 三种Ts/VI指数在UCLA土壤湿度降尺度法中的效果评价. 应用生态学报, 25(2): 545-552 [DOI: 10.13287/j.1001-9332.2014.0057http://dx.doi.org/10.13287/j.1001-9332.2014.0057]
Liu J, Guo J and Han W T. 2020. Advances in research on soil moisture retrieval using synthetic aperture radar. Ecology and Environmental Monitoring of Three Gorges, 5(2): 44-53
刘健, 郭交, 韩文霆. 2020. 基于合成孔径雷达的土壤水分反演研究进展. 三峡生态环境监测, 5(2): 44-53 [DOI: 10.19478/j.cnki.2096-2347.2020.02.07http://dx.doi.org/10.19478/j.cnki.2096-2347.2020.02.07]
Long D, Bai L L, Yan L, Zhang C J, Yang W T, Lei H M, Quan J L, Meng X Y and Shi C X. 2019. Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution. Remote Sensing of Environment, 233: 111364 [DOI: 10.1016/j.rse.2019.111364http://dx.doi.org/10.1016/j.rse.2019.111364]
Lu H, Koike T, Fujii H, Ohta T and Tamagawa K. 2009. Development of a physically-based soil moisture retrieval algorithm for spaceborne passive microwave radiometers and its application to AMSR-E. Journal of the Remote Sensing Society of Japan, 29(1): 253-262 [DOI: 10.11440/rssj.29.253http://dx.doi.org/10.11440/rssj.29.253]
Luo W, Xu X L, Liu W, Liu M X, Li Z W, Peng T, Xu C H, Zhang Y H and Zhang R F. 2019. UAV based soil moisture remote sensing in a karst mountainous catchment. Catena, 174: 478-489 [DOI: 10.1016/j.catena.2018.11.017http://dx.doi.org/10.1016/j.catena.2018.11.017]
Malbéteau Y, Merlin O, Molero B, Rüdiger C and Bacon S. 2016. 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. International Journal of Applied Earth Observation and Geoinformation, 45: 221-234 [DOI: 10.1016/j.jag.2015.10.002http://dx.doi.org/10.1016/j.jag.2015.10.002]
Mao H Z, Kathuria D, Duffield N and Mohanty B P. 2019. Gap filling of high-resolution soil moisture for SMAP/Sentinel-1: a two-layer machine learning-based framework. Water Resources Research, 55(8): 6986-7009 [DOI: 10.1029/2019WR024902http://dx.doi.org/10.1029/2019WR024902]
Meng X J, Mao K B, Meng F, Shen X Y, Xu T R and Cao M M. 2019. Long-term spatiotemporal variations in soil moisture in North East China based on 1-km resolution downscaled passive microwave soil moisture products. Sensors, 19(16): 3527 [DOI: 10.3390/s19163527http://dx.doi.org/10.3390/s19163527]
Merlin O, Al Bitar A, Walker J P and Kerr Y. 2009. A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors. Remote Sensing of Environment, 113(10): 2275-2284 [DOI: 10.1016/j.rse.2009.06.012http://dx.doi.org/10.1016/j.rse.2009.06.012]
Merlin O, Al Bitar A, Walker J P and Kerr Y. 2010. An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data. Remote Sensing of Environment, 114(10): 2305-2316 [DOI: 10.1016/j.rse.2010.05.007http://dx.doi.org/10.1016/j.rse.2010.05.007]
Merlin O, Chehbouni A, Kerr Y H and Goodrich D C. 2006. A downscaling method for distributing surface soil moisture within a microwave pixel: application to the monsoon '90 data. Remote Sensing of Environment, 101(3): 379-389 [DOI: 10.1016/j.rse.2006.01.004http://dx.doi.org/10.1016/j.rse.2006.01.004]
Merlin O, Chehbouni A G, Kerr Y H, Njoku E G and Entekhabi D. 2005. A combined modeling and multispectral/multiresolution remote sensing approach for disaggregation of surface soil moisture: application to SMOS configuration. IEEE Transactions on Geoscience and Remote Sensing, 43(9): 2036-2050 [DOI: 10.1109/TGRS.2005.853192http://dx.doi.org/10.1109/TGRS.2005.853192]
Merlin O, Chehbouni A, Walker J P, Panciera R and Kerr Y H. 2008a. A simple method to disaggregate passive microwave-based soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 46(3): 786-796 [DOI: 10.1109/TGRS.2007.914807http://dx.doi.org/10.1109/TGRS.2007.914807]
Merlin O, Escorihuela M J, Mayoral M A, Hagolle O, Al Bitar A and Kerr Y. 2013. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: an evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sensing of Environment, 130: 25-38 [DOI: 10.1016/j.rse.2012.11.008http://dx.doi.org/10.1016/j.rse.2012.11.008]
Merlin O, Malbéteau Y, Notfi Y, Bacon S, Khabba S E and Jarlan L. 2015. Performance metrics for soil moisture downscaling methods: application to DISPATCH data in Central Morocco. Remote Sensing, 7(4): 3783-3807 [DOI: 10.3390/rs70403783http://dx.doi.org/10.3390/rs70403783]
Merlin O, Rudiger C, Bitar A A, Richaume P, Walker J P and Kerr Y H. 2012. Disaggregation of SMOS soil moisture in Southeastern Australia. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1556-1571 [DOI: 10.1109/TGRS.2011.2175000http://dx.doi.org/10.1109/TGRS.2011.2175000]
Merlin O, Walker J, Chehbouni A and Kerr Y. 2008b. Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency. Remote Sensing of Environment, 112(10): 3935-3946 [DOI: 10.1016/j.rse.2008.06.012http://dx.doi.org/10.1016/j.rse.2008.06.012]
Metz M, Andreo V and Neteler M. 2017. A new fully gap-free time series of land surface temperature from MODIS LST data. Remote Sensing, 9(12): 1333 [DOI: 10.3390/rs9121333http://dx.doi.org/10.3390/rs9121333]
Mladenova I, Lakshmi V, Jackson T J, Walker J P, Merlin O and De Jeu R A M. 2011. Validation of AMSR-E soil moisture Using L-band airborne radiometer data from National Airborne field experiment 2006. Remote Sensing of Environment, 115(8): 2096-2103 [DOI: 10.1016/j.rse.2011.04.011http://dx.doi.org/10.1016/j.rse.2011.04.011]
Molero B, Merlin O, Malbéteau Y, Al Bitar A, Cabot F, Stefan V, Kerr Y, Bacon S, Cosh M H, Bindlish R and Jackson T J. 2016. SMOS disaggregated soil moisture product at 1 km resolution: processor overview and first validation results. Remote Sensing of Environment, 180: 361-376 [DOI: 10.1016/j.rse.2016.02.045http://dx.doi.org/10.1016/j.rse.2016.02.045]
Naz B S, Kurtz W, Montzka C, Sharples W, Goergen K, Keune J, Gao H L, Springer A, Franssen H J H and Kollet S. 2019. Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation. Hydrology and Earth System Sciences, 23(1): 277-301 [DOI: 10.5194/hess-23-277-2019http://dx.doi.org/10.5194/hess-23-277-2019]
Njoku E G, Jackson T J, Lakshmi V, Chan T K and Nghiem S V. 2003. Soil moisture retrieval from AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 215-229 [DOI: 10.1109/TGRS.2002.808243http://dx.doi.org/10.1109/TGRS.2002.808243]
Oh Y, Sarabandi K and Ulaby F T. 1992. An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30: 370-381 [DOI:10.1109/36.134086http://dx.doi.org/10.1109/36.134086]
Pablos M, González-Haro C, Piles M and BEC Team. 2020. BEC SMOS Soil Moisture Products Description (V.1.0), [online] Available: https://digital.csic.es/bitstream/10261/218969/1/Pablos_et_al_2020.pdf (2020)https://digital.csic.es/bitstream/10261/218969/1/Pablos_et_al_2020.pdf(2020)
Pablos M, Martínez-Fernández J, Piles M, Sánchez N, Vall-Llossera M and Camps A. 2016a. Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations. Remote Sensing, 8(7): 587 [DOI: 10.3390/rs8070587http://dx.doi.org/10.3390/rs8070587]
Pablos M, Piles M, Sánchez N, Vall-Llossera M, Martínez-Fernández J and Camps A. 2016b. Impact of day/night time land surface temperature in soil moisture disaggregation algorithms. European Journal of Remote Sensing, 49(1): 899-916 [DOI: 10.5721/EuJRS20164947http://dx.doi.org/10.5721/EuJRS20164947]
Parinussa R M, Holmes T R H, Wanders N, Dorigo W A and De Jeu R A M. 2015. A preliminary study toward consistent soil moisture from AMSR2. Journal of Hydrometeorology, 16(2): 932-947 [DOI: 10.1175/JHM-D-13-0200.1http://dx.doi.org/10.1175/JHM-D-13-0200.1]
Parinussa R M, Wang G, Holmes T R H, Liu Y Y, Dolman A J, De Jeu R A M, Jiang T, Zhang P and Shi J. 2014. Global surface soil moisture from the microwave radiation imager onboard the Fengyun-3B satellite. International Journal of Remote Sensing, 35(19): 7007-7029 [DOI: 10.1080/01431161.2014.960622http://dx.doi.org/10.1080/01431161.2014.960622]
Pellenq J, Kalma J, Boulet G, Saulnier G M, Wooldridge S, Kerr Y and Chehbouni A. 2003. A disaggregation scheme for soil moisture based on topography and soil depth. Journal of Hydrology, 276(1/4): 112-127 [DOI: 10.1016/s0022-1694(03)00066-0http://dx.doi.org/10.1016/s0022-1694(03)00066-0]
Pelletier C, Valero S, Inglada J, Champion N and Dedieu G. 2016. Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sensing of Environment, 187: 156-168 [DOI: 10.1016/j.rse.2016.10.010http://dx.doi.org/10.1016/j.rse.2016.10.010]
Peng J, Loew A, Merlin O and Verhoest N E C. 2017. A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 55(2): 341-366 [DOI: 10.1002/2016rg000543http://dx.doi.org/10.1002/2016rg000543]
Peng J, Loew A, Zhang S, Wang J and Niesel J. 2016. Spatial downscaling of satellite soil moisture data using a vegetation temperature condition index. IEEE Transactions on Geoscience and Remote Sensing, 54: 558-566 [DOI: 10.1109/TGRS.2015.2462074http://dx.doi.org/10.1109/TGRS.2015.2462074]
Piepmeier J R, Focardi P, Horgan K A, Knuble J, Ehsan N, Lucey J, Brambora C, Brown P R, Hoffman P J, French R T, Mikhaylov R L, Kwack E Y, Slimko E M, Dawson D E, Hudson D, Peng J Z, Mohammed P N, De Amici G, Freedman A P, Medeiros J, Sacks F, Estep R, Spencer M W, Chen C W, Wheeler K B, Edelstein W N, O’Neill P E and Njoku E G. 2017. SMAP L-band microwave radiometer: instrument design and first year on orbit. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 1954-1966 [DOI: 10.1109/TGRS.2016.2631978http://dx.doi.org/10.1109/TGRS.2016.2631978]
Piles M, Camps A, Vall-Llossera M, Corbella I, Panciera R, Rüdiger C, Kerr Y H and Walker J. 2011. Downscaling SMOS-derived soil moisture Using MODIS visible/infrared data. IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3156-3166 [DOI: 10.1109/TGRS.2011.2120615http://dx.doi.org/10.1109/TGRS.2011.2120615]
Piles M, Entekhabi D and Camps A. 2009. A change detection algorithm for retrieving high-resolution soil moisture from SMAP radar and radiometer observations. IEEE Transactions on Geoscience and Remote Sensing, 47(12): 4125-4131 [DOI: 10.1109/TGRS.2009.2022088http://dx.doi.org/10.1109/TGRS.2009.2022088]
Piles M, Petropoulos G P, Sánchez N, González-Zamora Á and Ireland G. 2016. Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations. Remote Sensing of Environment, 180: 403-417 [DOI: 10.1016/j.rse.2016.02.048http://dx.doi.org/10.1016/j.rse.2016.02.048]
Piles M, Sánchez N, Vall-Llossera M, Camps A, Martínez-Fernández J, Martínez J and González-Gambau V. 2014. A downscaling approach For SMOS land observations: evaluation of high-resolution soil moisture maps over the Iberian Peninsula. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3845-3857 [DOI: 10.1109/jstars.2014.2325398http://dx.doi.org/10.1109/jstars.2014.2325398]
Portal G, Jagdhuber T, Vall-Llossera M, Camps A, Pablos M, Entekhabi D and Piles M. 2020. Assessment of multi-Scale SMOS and SMAP soil moisture products across the Iberian Peninsula. Remote Sensing, 12(3): 570 [DOI: 10.3390/rs12030570http://dx.doi.org/10.3390/rs12030570]
Portal G, Vall-Llossera M, Piles M, Camps A, Chaparro D, Pablos M and Rossato L. 2018. A spatially consistent downscaling approach For SMOS using an adaptive moving window. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6): 1883-1894 [DOI: 10.1109/jstars.2018.2832447http://dx.doi.org/10.1109/jstars.2018.2832447]
Price J C. 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing, 28(5): 940-948 [DOI: 10.1109/36.58983http://dx.doi.org/10.1109/36.58983]
Raffelli G, Previati M, Canone D, Gisolo D, Bevilacqua I, Capello G, Biddoccu M, Cavallo E, Deiana R, Cassiani G and Ferraris S. 2017. Local- and plot-scale measurements of soil moisture: time and spatially resolved field techniques in plain, hill and mountain sites. Water, 9(9): 706 [DOI: 10.3390/w9090706http://dx.doi.org/10.3390/w9090706]
Ray R L, Jacobs J M and Cosh M H. 2010. Landslide susceptibility mapping using downscaled AMSR-E soil moisture: a case study from Cleveland Corral, California, US. Remote Sensing of Environment, 114(11): 2624-2636 [DOI: 10.1016/j.rse.2010.05.033http://dx.doi.org/10.1016/j.rse.2010.05.033]
Ren Z J and Huang Q F. 2017. Study on the downscaling method of remote sensing products of soil moisture. Journal of Heilongjiang Vocational institute of Ecological Engineering, 30(5): 29-31
任中杰, 黄秋锋. 2017. 土壤水分遥感产品降尺度方法研究. 黑龙江生态工程职业学院学报, 30(5): 29-31 [DOI: 10.3969/j.issn.1674-6341.2017.05.010http://dx.doi.org/10.3969/j.issn.1674-6341.2017.05.010]
Rodríguez-Fernández N J, Aires F, Richaume P, Kerr Y H, Prigent C, Kolassa J, Cabot F, Jiménez C, Mahmoodi A and Drusch M. 2015. Soil moisture retrieval using Neural Networks: application to SMOS. IEEE Transactions on Geoscience and Remote Sensing, 53: 5991-6007 [DOI: 10.1109/TGRS.2015.2430845http://dx.doi.org/10.1109/TGRS.2015.2430845]
Sánchez-Ruiz S, Piles M, Sánchez N, Martínez-Fernández J, Vall-Llossera M and Camps A. 2014. Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates. Journal of Hydrology, 516: 273-283 [DOI: 10.1016/j.jhydrol.2013.12.047http://dx.doi.org/10.1016/j.jhydrol.2013.12.047]
Sanchez N, Martinez-Fernandez J, Scaini A and Perez-Gutierrez C. 2012. Validation of the SMOS L2 soil moisture data in the REMEDHUS Network (Spain). IEEE Transactions on Geoscience and Remote Sensing, 50: 1602-1611 [DOI: 10.1109/TGRS.2012.2186971http://dx.doi.org/10.1109/TGRS.2012.2186971]
Sahoo A K, De Lannoy G J M, Reichle R H and Houser P R. 2013. Assimilation and downscaling of satellite observed soil moisture over the little river experimental watershed in Georgia, USA. Advances in Water Resources, 52: 19-33 [DOI: 10.1016/j.advwatres.2012.08.007http://dx.doi.org/10.1016/j.advwatres.2012.08.007]
Sandholt I, Rasmussen K and Andersen J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2/3): 213-224 [DOI: 10.1016/S0034-4257(01)00274-7http://dx.doi.org/10.1016/S0034-4257(01)00274-7]
Senanayake I P, Yeo I Y, Tangdamrongsub N, Willgoose G R, Hancock G R, Wells T, Fang B, Lakshmi V and Walker J P. 2019. An in-situ data based model to downscale radiometric satellite soil moisture products in the Upper Hunter Region of NSW, Australia. Journal of Hydrology, 572: 820-838 [DOI: 10.1016/j.jhydrol.2019.03.014http://dx.doi.org/10.1016/j.jhydrol.2019.03.014]
Shi J C, Du Y, Du J Y, Jiang L M, Chai L N, Mao K B, Xu P, Ni W J, Xiong C, Liu Q, Liu C Z, Guo P, Cui Q, Li Y Q, Chen J, Wang A Q, Luo H J and Wang Y H. 2012. Progresses on microwave remote sensing of land surface parameters. Science China: Earth Sciences, 55(7): 1052-1078
施建成, 杜阳, 杜今阳, 蒋玲梅, 柴琳娜, 毛克彪, 徐鹏, 倪文俭, 熊川, 刘强, 刘晨洲, 郭鹏, 崔倩, 李云青, 陈晶, 王安琪, 罗禾佳, 王殷辉. 2012. 微波遥感地表参数反演进展. 中国科学: 地球科学, 42(6): 814-842 [DOI: 10.1007/s11430-012-4444-xhttp://dx.doi.org/10.1007/s11430-012-4444-x]
Singh R P, Roy S and Kogan F. 2003. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing, 24(22): 4393-4402 [DOI: 10.1080/0143116031000084323http://dx.doi.org/10.1080/0143116031000084323]
Srivastava P K, Han D W, Ramirez M R and Islam T. 2013. Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resources Management, 27(8): 3127-3144 [DOI: 10.1007/s11269-013-0337-9http://dx.doi.org/10.1007/s11269-013-0337-9]
Sun Q, Tan J and Chen S. 2008. Estimation of soil moisture conditions with Landsat TM in Guangzhou//Proceedings of SPIE 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments. Guangzhou: SPIE:7145 [DOI: 10.1117/12.812999]
Tian J, Han Z B, Bogena H R, Huisman J A, Montzka C, Zhang B Q and He C S. 2020. Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas. Hydrology and Earth System Sciences, 24(9): 4659-4674 [DOI: 10.5194/hess-24-4659-2020http://dx.doi.org/10.5194/hess-24-4659-2020]
Toride K, Sawada Y, Aida K and Koike T. 2019. Toward high-resolution soil moisture monitoring by combining active-passive microwave and optical vegetation remote sensing products with land surface model. Sensors, 19(18): 3924 [DOI: 10.3390/s19183924http://dx.doi.org/10.3390/s19183924]
Ulaby F T, Long D G, Blackwell W, Elachi C and Zebker H. 2014. Microwave radar and radiometric remote sensing. USA: University of Michigan Press[https://books.google.com/books?id=y6pZngEACAAJhttps://books.google.com/books?id=y6pZngEACAAJ]
Vergopolan N, Chaney N W, Beck H E, Pan M, Sheffield J, Chan S and Wood E F. 2020. Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 242: 111740 [DOI: 10.1016/j.rse.2020.111740http://dx.doi.org/10.1016/j.rse.2020.111740]
Wagner W. 2003. Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. Journal of Geophysical Research: Atmospheres, 108(D19): 4611 [DOI: 10.1029/2003jd003663http://dx.doi.org/10.1029/2003jd003663]
Wang A Q, Xie C, Shi J C and Gong H L. 2013. The relationship between the variation rate of MODIS land surface temperature and AMSR-E soil moisture and its application to downscaling. Spectroscopy and Spectral Analysis, 33(3): 623-627
王安琪, 解超, 施建成, 宫辉力. 2013. MODIS温度变化率与AMSR-E土壤水分的关系的提出与降尺度算法推广. 光谱学与光谱分析, 33(3): 623-627 [DOI: 10.3964/j.issn.1000-0593(2013)03-0623-05http://dx.doi.org/10.3964/j.issn.1000-0593(2013)03-0623-05]
Wang L L and Qu J J. 2009. Satellite remote sensing applications for surface soil moisture monitoring: a review. Frontiers of Earth Science in China, 3(2): 237-247 [DOI: 10.1007/s11707-009-0023-7http://dx.doi.org/10.1007/s11707-009-0023-7]
Wei Z S, Meng Y Z, Zhang W, Peng J and Meng L K. 2019. Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau. Remote Sensing of Environment, 225: 30-44 [DOI: 10.1016/j.rse.2019.02.022http://dx.doi.org/10.1016/j.rse.2019.02.022]
Wen F P, Zhao W, Wang Q M and Sánchez N. 2020. A value-consistent method for downscaling SMAP passive soil moisture with MODIS products using self-adaptive window. IEEE Transactions on Geoscience and Remote Sensing, 58(2): 913-924 [DOI: 10.1109/TGRS.2019.2941696http://dx.doi.org/10.1109/TGRS.2019.2941696]
Wigneron J P, Jackson T J, O'Neill P, De Lannoy G, de Rosnay P, Walker J P, Ferrazzoli P, Mironov V, Bircher S, Grant J P, Kurum M, Schwank M, Munoz-Sabater J, Das N, Royer A, Al-Yaari A, Al Bitar A, Fernandez-Moran R, Lawrence H, Mialon A, Parrens M, Richaume P, Delwart S and Kerr Y. 2017. Modelling the passive microwave signature from land surfaces: a review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sensing of Environment, 192: 238-262 [DOI: 10.1016/j.rse.2017.01.024http://dx.doi.org/10.1016/j.rse.2017.01.024]
Wu X L, Walker J P, Rüdiger C and Panciera R. 2015. Effect of land-cover type on the SMAP active/passive soil moisture downscaling algorithm performance. IEEE Geoscience and Remote Sensing Letters, 12(4): 846-850 [DOI: 10.1109/LGRS.2014.2364049http://dx.doi.org/10.1109/LGRS.2014.2364049]
Xin Q, Li Z F, Li R J, Guo T, Wu M and Pan J J. 2016. Downscaling AMSR-E soil moisture data based on temperature vegetation drought index in Eastern China. Research of Agricultural Modernization, 37(5): 956-963
辛强, 李兆富, 李瑞娟, 郭泰, 吴敏, 潘剑君. 2016. 基于温度植被干旱指数的华东地区AMSR-E土壤水分数据的空间降尺度研究. 农业现代化研究, 37(5): 956-963 [DOI: 10.13872/j.1000-0275.2016.0099http://dx.doi.org/10.13872/j.1000-0275.2016.0099]
Xu C Y, Qu J J, Hao X J, Cosh M H, Prueger J H, Zhu Z L and Gutenberg L. 2018. Downscaling of surface soil moisture retrieval by combining MODIS/Landsat and in situ measurements. Remote Sensing, 10(2): 210 [DOI: 10.3390/rs10020210http://dx.doi.org/10.3390/rs10020210]
Yang T, Gong H L, Li X J, Zhao W J and Meng D. 2010. Progress of soil moisture monitoring by remote sensing. Acta Ecologica Sinica, 30(22): 6264-6277
杨涛, 宫辉力, 李小娟, 赵文吉, 孟丹. 2010. 土壤水分遥感监测研究进展. 生态学报, 30(22): 6264-6277
Yao C S, Zhang Z X and Wang X. 2004. Evaluating soil moisture status in Xinjiang using the temperature vegetation dryness index (TVDI). Remote Sensing Technology and Application, 19(6): 473-478
姚春生, 张增祥, 汪潇. 2004. 使用温度植被干旱指数法(TVDI)反演新疆土壤湿度. 遥感技术与应用, 19(6): 473-478 [DOI: 10.3969/j.issn.1004-0323.2004.06.008http://dx.doi.org/10.3969/j.issn.1004-0323.2004.06.008]
Yin J F, Zhan X W, Liu J C, Moradkhani H, Fang L and Walker J P. 2020. Near-real-time one-kilometre soil moisture active passive soil moisture data product. Hydrological Processes, 34(21): 4083-4096 [DOI: 10.1002/hyp.13857http://dx.doi.org/10.1002/hyp.13857]
Yu F and Zhao Y S. 2011. A new semi-empirical model for soil moisture content retrieval By ASAR and TM data in vegetation-covered areas. Science China: Earth Sciences, 54(12): 1955-1964
余凡, 赵英时. 2011. ASAR和TM数据协同反演植被覆盖地表土壤水分的新方法. 中国科学: 地球科学, 41(4): 532-540 [DOI: 10.1360/zd-2011-41-4-532http://dx.doi.org/10.1360/zd-2011-41-4-532]
Zeng J, Chen K S, Bi H and Chen Q. 2016. A preliminary evaluation of the SMAP radiometer soil moisture product over United States and Europe using ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing, 54: 4929-4940 [DOI: 10.1109/TGRS.2016.2553085http://dx.doi.org/10.1109/TGRS.2016.2553085]
Zhang D J, Tang R L, Zhao W, Tang B H, Wu H, Shao K and Li Z L. 2014. Surface soil water content estimation from thermal remote sensing based on the temporal variation of land surface temperature. Remote Sensing, 6(4): 3170-3187 [DOI: 10.3390/rs6043170http://dx.doi.org/10.3390/rs6043170]
Zhang P, Yang J, Dong C H, Lu N M, Yang Z D and Shi J M. 2009. General introduction on payloads, ground segment and data application of Fengyun 3A. Frontiers of Earth Science in China, 3(3): 367-373 [DOI: 10.1007/s11707-009-0036-2http://dx.doi.org/10.1007/s11707-009-0036-2]
Zhao T J. 2018. Recent advances of L-band application in the passive microwave remote sensing of soil moisture and its prospects. Progress in Geography, 37(2): 198-213
赵天杰. 2018. 被动微波反演土壤水分的L波段新发展及未来展望. 地理科学进展, 37(2): 198-213 [DOI: 10.18306/dlkxjz.2018.02.003http://dx.doi.org/10.18306/dlkxjz.2018.02.003]
Zhao T J, Shi J C, Lin M S, Yin X J, Liu Y, Lan H M and Xiong C. 2014. Potential soil moisture product from the Chinese HY-2 scanning microwave radiometer and its initial assessment. Journal of Applied Remote Sensing, 8(1): 083560 [DOI: 10.1117/1.JRS.8.083560http://dx.doi.org/10.1117/1.JRS.8.083560]
Zhao T J, Shi J C, Lv L Q, Xu H X, Chen D Q, Cui Q, Jackson T J, Yan G J, Jia L, Chen L F, Zhao K, Zheng X M, Zhao L M, Zheng C L, Ji D B, Xiong C, Wang T X, Li R, Pan J M, Wen J G, Yu C, Zheng Y M, Jiang L M, Chai L N, Lu H, Yao P P, Ma J W, Lv H S, Wu J J, Zhao W, Yang N, Guo P, Li Y X, Hu L, Geng D Y and Zhang Z Q. 2020. Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sensing of Environment, 240: 111680 [DOI: 10.1016/j.rse.2020.111680http://dx.doi.org/10.1016/j.rse.2020.111680]
Zhao W and Duan S B. 2020. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data. Remote Sensing of Environment, 247: 111931 [DOI: 10.1016/j.rse.2020.111931http://dx.doi.org/10.1016/j.rse.2020.111931]
Zhao W and Li A N. 2013. A downscaling method for improving the spatial resolution of AMSR-E derived soil moisture product based on MSG-SEVIRI data. Remote Sensing, 5(12): 6790-6811 [DOI: 10.3390/rs5126790http://dx.doi.org/10.3390/rs5126790]
Zhao W, Li A N, Jin H A, Zhang Z J, Bian J H and Yin G F. 2017. Performance evaluation of the triangle-based empirical soil moisture relationship models based on Landsat-5 TM data and in situ measurements. IEEE Transactions on Geoscience and Remote Sensing, 55(5): 2632-2645 [DOI: 10.1109/TGRS.2017.2649522http://dx.doi.org/10.1109/TGRS.2017.2649522]
Zhao W, Li Z L, Wu H, Tang B H, Zhang X Y, Song X N and Zhou G Q. 2013. Determination of bare surface soil moisture from combined temporal evolution of land surface temperature and net surface shortwave radiation. Hydrological Processes, 27(19): 2825-2833 [DOI: 10.1002/hyp.9410http://dx.doi.org/10.1002/hyp.9410]
Zhao W, Sánchez N, Lu H and Li A N. 2018. A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of Hydrology, 563: 1009-1024 [DOI: 10.1016/j.jhydrol.2018.06.081http://dx.doi.org/10.1016/j.jhydrol.2018.06.081]ZhaoW, WenF, WangQ, SanchezN and PilesM. 2021. Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products. Journal of Hydrology, 603: 126930[DOI: 10.1016/j.jhydrol.2021.126930http://dx.doi.org/10.1016/j.jhydrol.2021.126930]
Zhao W, Wu H, Yin G. and Duan S B. 2019. Normalization of the temporal effect on the MODIS land surface temperature product using random forest regression. ISPRS Journal of Photogrammetry and Remote Sensing, 152: 109-118 [DOI: 10.1016/j.isprsjprs.2019.04.008http://dx.doi.org/10.1016/j.isprsjprs.2019.04.008]
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