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SMAP passive microwave soil moisture spatial downscaling based on optical remote sensing data： A case study in Shandian river basin

  • role： First author第一作者
  • Affiliation：

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

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：wenfp@imde.ac.cn
  • Introduction：文凤平，1994年生，女，硕士研究生，研究方向为土壤水分空间尺度转换。E-mail：wenfp@imde.ac.cn
WEN Fengping12,  
  • role： Corresponding author通信作者
  • Affiliation：

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

  • Email：zhaow@imde.ac.cn
  • Introduction：赵伟，1984年生，男，副研究员，研究方向为山地地表水热参量遥感反演。E-mail：zhaow@imde.ac.cn
ZHAO Wei1*,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

HU Lu3,  
  • Affiliation：

    Shanghai Academy of Spaceflight Technology, Shanghai 201109, China

XU Hongxin4,  
  • Affiliation：

    Information Center of Ministry of Water Resources of China, Beijing 100053, China

CUI Qian5

реферат

Soil Moisture (SM) is not only an important variable in land surface processes, but also a key parameter in global water cycle. In this paper, the objectives are: (1) downscaling SMAP (Soil Moisture Active Passive) SM (SMAP SM) from spatial resolution of 9 km to 1 km, with the using of the auxiliary data from MODIS (Moderate-Resolution Imaging Spectroradiometer) products (land surface temperature and normalized difference vegetation index) by a downscaling method based on self-adaptive window in Shandian river basin; (2) validating the downscaled SM with the in-situ SM and the airborne passive microwave SM (airborne SM); and (3) analyzing the uncertainty caused by auxiliary data and SM estimated model in the downscaling process. The downscaling method used in this paper involves two steps. The SM model was established by using geographically weighted regression model between SMAP SM and the auxiliary data to calculate the 1-km estimated model SM (SMR). Then the 9-km residual (RC) generated by the SM estimated model is downscaled to 1-km spatial resolution (RF) by area-to-point kriging. Finally, the downscaled SM (SMF) is the sum of SMR and RF. It’s worth noting that to derive the robust downscaled SM, self-adaptive windows are adopted in these two steps. Visual assessment shows that the downscaling method can not only improve the spatial resolution of SMAP SM, but also retain the consistency between the spatial distributions of the downscaled SM and of the original SMAP SM. The validation results of the airborne SM, the SMAP SM and the downscaled SM against the in-situ SM are not satisfactory. On Sep 24, the correlation coefficient (R) between the three SM data and the in-situ SM are less than 0.5, and on Sep 26, the root mean squared errors (RMSE) are greater than 0.08 m3/m3. By analyzing these data, we found that the limited amount of valid data used in validation was one of the reasons for the poor validation. In addition, the different spatial representativeness and the inconsistent spatial matching of point-scale data and pixel-scale data are also the factors caused the uncertainty in the validation results. Compared with the in-situ SM, the SMAP SM and the downscaled SM have better correlations with the airborne SM. The RMSEs between the downscaled SM and the airborne SM are about 0.04 m3/m3, while the RMSEs between the SMAP SM and the airborne SM are less than 0.04 m3/m3. The correlation between the SMAP SM and auxiliary data (the absolute values of Rs are greater than 0.6) is higher than that between the airborne SM and the auxiliary data (the absolute values of Rs are less than 0.53). It can be seen that there are some differences between the SMAP SM and the airborne SM, which is mainly affected by different spatial scales, observation configurations, SM derived algorithms and auxiliary data using in algorithms of these two SM data. However, more studies are needed on the mechanism of the relationship between auxiliary data and SM in the downscaling process. By adding auxiliary data (land surface albedo) or changing the SM estimation model, the validated results of the downscaled SM against the airborne SM did not improve obviously. This is mainly because more auxiliary data and higher polynomials caused overfitting in the downscaling process, which will be still the focus of future research.

ключеви́че слова́

soil moisture;spatial downscaling;airborne passive microwave soil moisture;uncertainty analysis;SMAP;MODIS

References

  1. 1.
    Cao Y N, Yuan Y, Zheng X Y and Zhou S X. 2019. MODIS data-based cloud properties in Huaibei region. Journal of Remote Sensing, 23(2): 349-358
  2. 2.
    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
  3. 3.
    Cheng Y, Chen L F, Liu Q H, Zhang H and Gu X F. 2006. The soil moisture detection for different vegetation coverage based on the MODIS data. Journal of Remote Sensing, 10(5): 783-788
  4. 4.
    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
  5. 5.
    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: D09113
  6. 6.
    Das K, Paul P K and Dobesova Z. 2015. Present status of soil moisture estimation by microwave remote sensing. Cogent Geoscience, 1(1): 1084669
  7. 7.
    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
  8. 8.
    Han H Z, Bai J J, Zhang B and Ma G. 2018. Spatial-temporal characteristics of vegetation phenology in Shaanxi province based on MODIS time series. Remote Sensing for Land and Resources, 30(4): 125-131
  9. 9.
    Hu L, Zhao T J, Shi J C, Li S N, Fan D, Wang P K, Geng D Y, Xiao Q, Cui Q and Chen D Q. 2020. Evaluation of soil moisture retrieval algorithms based on ground-based microwave radiation observation. Remote Sensing Technology and Application, 35(1): 74-84
  10. 10.
    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
  11. 11.
    Jin Y, Ge Y, Wang J H, Chen Y H, Heuvelink G B M and Atkinson P M. 2018. 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
  12. 12.
    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
  13. 13.
    Li M Y and Huang F. 2016. Down-scaling AMSR-E derived soil moisture using SPOT-VGT visible/shortwave infrared data. Remote Sensing Technology and Application, 31(2): 342-348
  14. 14.
    Lu Y, Steele-Dunne S C and De Lannoy G J M. 2020. Improving soil moisture and surface turbulent heat flux estimates by assimilation of SMAP brightness temperatures or soil moisture retrievals and GOES land surface temperature retrievals. Journal of Hydrometeorology, 21(2): 183-203
  15. 15.
    Ma H Z, Zhang L J, Sun L and Liu Q H. 2014. Farmland soil moisture inversion by synergizing optical and microwave remote sensing data. Journal of Remote Sensing, 18(3): 673-685
  16. 16.
    Nasta P, Penna D, Brocca L, Zuecco G and Romano N. 2018. Downscaling near-surface soil moisture from field to plot scale: a comparative analysis under different environmental conditions. Journal of Hydrology, 557: 97-108
  17. 17.
    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
  18. 18.
    Piles M, Camps A, Vall-Llossera M, Corbella I, Panciera R, Rüdiger C, Kerr Y H and Walker J P. 2011. Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3156-3166
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    Shibata A, Imaoka K and Koike T. 2003. AMSR/AMSR-E level 2 and 3 algorithm developments and data validation plans of NASDA. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 195-203
  24. 24.
    Wang A Q and Liu P. 2018. Extended application of a downscaling algorithm for large-scale soil moisture acquired by passive microwave radiometer. Journal of Arid Land Resources and Environment, 32(5): 104-109
  25. 25.
    Wang L, Hu Y M, Zhao Y S and Liu Z H. 2012. Remote sensing scale transformation of soil moisture based on block kriging. Journal of Geo-Information Science, 14(4): 465-473
  26. 26.
    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
  27. 27.
    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
  28. 28.
    Yan G J， Zhao T J, Mu X H, Wen J G, Pang Y, Jia L, Zhang Y G, Chen D Q, Yao C B, Cao Z Y, Lei Y H, Ji D B, Chen L F, Liu Q H, Lyu L Q, Chen J M and Shi J C. 2021. Comprehensive Remote Sensing Experiment of Carbon Cycle, Water Cycle and Energy Balance in Luan River Basin. National Remote Sensing Bulletin， 25(4): 856-870
  29. 29.
    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
  30. 30.
    Zhao T J, Shi J C, Xu H X, Sun Y L, Chen D Q, Cui Q, Jia L, Huang S, Niu S D, Li X W, Yan G J, Chen L F, Liu Q H, 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, Mu X H, 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, Zhang Z Q, Hu J F and Du A P. 2021. Comprehensive remote sensing experiment of water cycle and energy balance in the Shandian river basin. National Remote Sensing Bulletin， 25(4): 871-887
  31. 31.
    Zhao W, He J L, Wu Y H, Xiong D H, Wen F P and Li A N. 2019. An analysis of land surface temperature trends in the central Himalayan region based on MODIS products. Remote Sensing, 11(8): 900
  32. 32.
    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
  33. 33.
    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
  34. 34.
    Zhou P, Ding J L, Wang F, Ubul G and Zhang Z G. 2010. Retrieval methods of soil water content in vegetation covering areas based on multi-source remote sensing data. Journal of Remote Sensing, 14(5): 959-973
  35. 35.
    Zhou Z, Zhao S J and Jiang L M. 2016. Downscaling methods of passive microwave remote sensing of soil moisture. Journal of Beijing Normal University (Natural Science), 52(4): 479-485

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