سابِق|تال

Methods， progresses， and challenges of passive microwave soil moisture spatial downscaling

  • role： First author第一作者
  • Affiliation：

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

  • Email：zhaow@imde.ac.cn
  • Introduction：赵伟，研究方向为山地地表水热过程遥感。E-mail：zhaow@imde.ac.cn
ZHAO Wei1,  
  • Affiliation：

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

    University of Chinese Academy of Sciences, Beijing 100049, China

WEN Fengping12,  
  • Affiliation：

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

    University of Chinese Academy of Sciences, Beijing 100049, China

CAI Junfei12

ملخص

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.

مفهوم

passive microwave;soil moisture;spatial downscaling;Empirical method;Semi-empirical method;physical model-based method

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    Das K, Paul P K and Dobesova Z. 2015. Present status of soil moisture estimation by microwave remote sensing. Cogent Geoscience, 1(1): 1084669
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    Gruber A and Peng J. 2022. Remote sensing of soil moisture. Reference Module in Earth Systems and Environmental Sciences: Elsevier: 1-13
  42. 42.
    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: , 2016]
  43. 43.
    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
  44. 44.
    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
  45. 45.
    Jackson T J. 2005. Remote sensing | soil moisture// Hillel D ed. Encyclopedia of Soils in the Environment. Oxford: Elsevier: 392-399
  46. 46.
    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
  47. 47.
    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
  48. 48.
    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
  49. 49.
    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
  50. 50.
    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
  51. 51.
    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
  52. 52.
    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
  53. 53.
    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
  54. 54.
    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
  55. 55.
    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
  56. 56.
    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
  57. 57.
    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
  58. 58.
    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
  59. 59.
    Japan Aerospace Exploration Agency Earth Observation Research Center . 2013. Descriptions of GCOM-W1 AMSR2 Level 1R and Level 2 Algorithms. [online] Available:
  60. 60.
    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
  61. 61.
    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
  62. 62.
    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
  63. 63.
    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
  64. 64.
    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
  65. 65.
    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].
  66. 66.
    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
  67. 67.
    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
  68. 68.
    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
  69. 69.
    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
  70. 70.
    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
  71. 71.
    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
  72. 72.
    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
  73. 73.
    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
  74. 74.
    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
  75. 75.
    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
  76. 76.
    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
  77. 77.
    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
  78. 78.
    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
  79. 79.
    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
  80. 80.
    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
  81. 81.
    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
  82. 82.
    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
  83. 83.
    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
  84. 84.
    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
  85. 85.
    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
  86. 86.
    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
  87. 87.
    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
  88. 88.
    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
  89. 89.
    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
  90. 90.
    Pablos M, González-Haro C, Piles M and BEC Team. 2020. BEC SMOS Soil Moisture Products Description (V.1.0), [online] Available:
  91. 91.
    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
  92. 92.
    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
  93. 93.
    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
  94. 94.
    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
  95. 95.
    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
  96. 96.
    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
  97. 97.
    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
  98. 98.
    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
  99. 99.
    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
  100. 100.
    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
  101. 101.
    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
  102. 102.
    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
  103. 103.
    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
  104. 104.
    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
  105. 105.
    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
  106. 106.
    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
  107. 107.
    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
  108. 108.
    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
  109. 109.
    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
  110. 110.
    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
  111. 111.
    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
  112. 112.
    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
  113. 113.
    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
  114. 114.
    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
  115. 115.
    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
  116. 116.
    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
  117. 117.
    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
  118. 118.
    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
  119. 119.
    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]
  120. 120.
    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
  121. 121.
    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
  122. 122.
    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[]
  123. 123.
    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
  124. 124.
    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
  125. 125.
    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
  126. 126.
    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
  127. 127.
    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
  128. 128.
    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
  129. 129.
    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
  130. 130.
    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
  131. 131.
    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
  132. 132.
    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
  133. 133.
    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
  134. 134.
    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
  135. 135.
    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
  136. 136.
    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
  137. 137.
    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
  138. 138.
    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
  139. 139.
    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
  140. 140.
    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
  141. 141.
    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
  142. 142.
    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
  143. 143.
    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
  144. 144.
    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
  145. 145.
    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
  146. 146.
    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
  147. 147.
    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 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
  148. 148.
    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

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