سابِق|تال

A primary study on downscaling microwave soil moisture with MOD16 and SMAP

  • role： First author第一作者
  • Affiliation：

    College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China

  • Email：sunhao@cumtb.edu.cn
  • Introduction：孙灏，1986年生，男，副教授，研究方向为资源环境遥感。E-mail: sunhao@cumtb.edu.cn
SUN Hao1,  
  • Affiliation：

    College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China

ZHOU Baichi1,  
  • Affiliation：

    Remote Sensing Survey and Mapping Institute of Ningxia Hui Autonomous Region (Ningxia Remote Sensing Center), Yinchuan 750021, China

LI huan2,  
  • Affiliation：

    College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China

RUAN Lin1

ملخص

Improving the spatial resolution of microwave Soil Moisture (SM) production is of great significance for hydrological and agricultural applications on a regional scale. Downscaling microwave satellite SM with optical/thermal infrared and microwave fusion method shows great application potential. However, it mostly relies on remote sensing surface temperature (LST) or the SM index derived by LST decomposition, which is limited by the cloud contamination problems, LST decomposition uncertainties, and the decoupling effect between LST and SM. To circumvent these problems, we made a primary study on downscaling microwave SM by coupling MOD16 and SMAP data. In this study, we constructed three parameterized downscaling functions (i.e., exponent, cosine, cosine squared) between Land surface Evapotranspiration Efficiency (LEE) and SM. MOD16 products is employed to calculate LEE, which has a spatial resolution of 500 m. Combining the parameterized downscaling functions and the high-resolution LEE, original SMAP SM (spatial resolution, 36 km) data were successfully downscaled to a spatial resolution of 500m. The downscaled SM was evaluated in terms of dynamic range, energy conservation, in situ SM at sparse stations, and in situ SM at Core Validation Station (CVS). Results demonstrated that the downscaling algorithm increases the spatial detail characteristics of original SM, maintains the dynamic range of SM, and preserves energy during the downscaling process. Moreover, it maintains the performance of the original SM as compared with in situ SM at CVS and sparse stations. Sensitivity analysis showed that the cosine-square downscaling function is less sensitive to errors in MOD16 production than the other two downscaling functions.

مفهوم

microwave soil moisture;spatially downscaling;land surface evapotranspiration efficiency;MOD16;SMAP

References

  1. 1.
    AghaKouchak A, Farahmand A, Melton F S, Teixeira J, Anderson M C, Wardlow B D and Hain C R. 2015. Remote sensing of drought: progress, challenges and opportunities. Reviews of Geophysics, 53(2): 452-480
  2. 2.
    Al-Yaari A, Wigneron J P, Kerr Y, Rodriguez-Fernandez N, O’Neill P E, Jackson T J, De Lannoy G J M, Al Bitar A, Mialon A, Richaume P, Walker J P, Mahmoodi A and Yueh S. 2017. Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets. Remote Sensing of Environment, 193: 257-273
  3. 3.
    Anderson M C, Norman J M, Mecikalski J R, Otkin J A and Kustas W P. 2007. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Research: Atmospheres, 112(D11): D11112
  4. 4.
    Busch F A, Niemann J D and Coleman M. 2012. Evaluation of an empirical orthogonal function–based method to downscale soil moisture patterns based on topographical attributes. Hydrological Processes, 26(18): 2696-2709
  5. 5.
    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, Holifield Collins C, 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
  6. 6.
    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
  7. 7.
    Chen F, Crow W T, Bindlish R, Colliander A, Burgin M S, Asanuma J and Aida K. 2018. Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation. Remote Sensing of Environment, 214: 1-13
  8. 8.
    Colliander A, Cosh M H, Misra S, Jackson T J, Crow W T, Chan S, Bindlish R, Chae C, Holifield Collins C and Yueh S H. 2017a. Validation and scaling of soil moisture in a semi-arid environment: SMAP validation experiment 2015 (SMAPVEX15). Remote Sensing of Environment, 196: 101-112
  9. 9.
    Colliander A, Fisher J B, Halverson G, Merlin O, Misra S, Bindlish R, Jackson T J and Yueh S. 2017b. Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15. IEEE Geoscience and Remote Sensing Letters, 14(11): 2107-2111
  10. 10.
    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. 2017c. Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment, 191: 215-231
  11. 11.
    Dai A G, Trenberth K E and Qian T T. 2004. A global dataset of Palmer Drought Severity Index for 1870-2002: relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology, 5(6): 1117-1130
  12. 12.
    Das K and Paul P K. 2015. Present status of soil moisture estimation by microwave remote sensing. Cogent Geoscience, 1(1): 1084669
  13. 13.
    Das N N, Entekhabi D, Njoku E G, Shi J J C, Johnson J T and Colliander A. 2014. Tests of the SMAP combined radar and radiometer algorithm using airborne field campaign observations and simulated data. IEEE Transactions on Geoscience and Remote Sensing, 52(4): 2018-2028
  14. 14.
    Dobriyal P, Qureshi A, Badola R and Hussain S A. 2012. A review of the methods available for estimating soil moisture and its implications for water resource management. Journal of Hydrology, 458-549: 110-117
  15. 15.
    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
  16. 16.
    Lee T J and Pielke R A. 1992. Estimating the soil surface specific humidity. Journal of Applied Meteorology, 31(5): 480-484
  17. 17.
    Li Z, Guo D H and Shi J C. 2002. Measuring the change of soil moisture with vegetation cover integration passive and active microwave data. Journal of Remote Sensing, 6(6): 481-484
  18. 18.
    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
  19. 19.
    McNairn H, Jackson T J, Wiseman G, Bélair S, Berg A, Bullock P, Colliander A, Cosh M H, Kim S B, Magagi R, Moghaddam M, Njoku E G, Adams J R, Homayouni S, Ojo E R, Rowlandson T L, Shang J L, Goïta K and Hosseini M. 2015. The soil moisture active passive validation experiment 2012 (SMAPVEX12): prelaunch calibration and validation of the SMAP soil moisture algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2784-2801
  20. 20.
    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
  21. 21.
    Merlin O, Chehbouni A, Walker J P, Panciera R and Kerr Y H. 2008. A simple method to disaggregate passive microwave-based soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 46(3): 786-796
  22. 22.
    Merlin O, Jacob F, Wigneron J P, Walker J and Chehbouni G. 2012. Multidimensional disaggregation of land surface temperature using high-resolution red, near-infrared, shortwave-infrared, and microwave-L bands. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1864-1880
  23. 23.
    Merlin O, Stefan V G, Amazirh A, Chanzy A, Ceschia E, Er-Raki S, Gentine P, Tallec T, Ezzahar J, Bircher S, Beringer J and Khabba S. 2016. Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta-analysis approach. Water Resources Research, 52(5): 3663-3684
  24. 24.
    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
  25. 25.
    Mu Q Z, Heinsch F A, Zhao M S and Running S W. 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4): 519-536
  26. 26.
    Mu Q Z, Zhao M S and Running S W. 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8): 1781-1800
  27. 27.
    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
  28. 28.
    Njoku E G, Wilson W J, Yueh S H, Dinardo S J, Li F K, Jackson T J, Lakshmi V and Bolten J. 2002. Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99. IEEE Transactions on Geoscience and Remote Sensing, 40(12): 2659-2673
  29. 29.
    Noilhan J and Planton S. 1989. A simple parameterization of land surface processes for meteorological models. Monthly Weather Review, 117(3): 536-549
  30. 30.
    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
  31. 31.
    Peng J, Loew A, Zhang S Q, 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(1): 558-566
  32. 32.
    Petropoulos G P, Ireland G and Barrett B. 2015. Surface soil moisture retrievals from remote sensing: current status, products and future trends. Physics and Chemistry of the Earth, Parts A/B/C, 83-84: 36-56
  33. 33.
    Robinson D A, Campbell C S, Hopmans J W, Hornbuckle B K, Jones S B, Knight R, Ogden F, Selker J and Wendroth O. 2008. Soil moisture measurement for ecological and hydrological watershed-scale observatories: a review. Vadose Zone Journal, 7(1): 358-389
  34. 34.
    Sako K, Moriiwa M and Satomi T. 2016. Experimental consideration on evaporation efficiency β of unsaturated sandy soil surface. Japanese Geotechnical Society Special Publication, 2(4): 226-229
  35. 35.
    Seneviratne S I, Corti T, Davin E L, Hirschi M, Jaeger E B, Lehner I, Orlowsky B and Teuling A J. 2010. Investigating soil moisture-climate interactions in a changing climate: a review. Earth-Science Reviews, 99(3-4): 125-161
  36. 36.
    Song L S, Liu S M, Xu T R, Xu Z W and Ma Y F. 2017. Soil evaporation and vegetation transpiration: remotely sensed estimation and validation. Journal of Remote Sensing, 21(6): 966-981
  37. 37.
    Sun D L and Pinker R T. 2004. Case study of soil moisture effect on land surface temperature retrieval. IEEE Geoscience and Remote Sensing Letters, 1(2): 127-130
  38. 38.
    Sun H. 2016a. A two-source model for estimating evaporative fraction (TMEF) coupling Priestley-Taylor formula and two-stage trapezoid. Remote Sensing, 8(3): 248
  39. 39.
    Sun H. 2016b. Two-stage trapezoid: a new interpretation of the land surface temperature and fractional vegetation coverage space. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(1): 336-346
  40. 40.
    Sun H, Cai C C, Liu H X and Yang B 2019a. Microwave and meteorological fusion: a method of spatial downscaling of remotely sensed soil moisture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(4): 1107-1119
  41. 41.
    Sun H, Wang Y M, Liu W H, Yuan S Y and Nie R W. 2017. Comparison of three theoretical methods for determining dry and wet edges of the LST/FVC space: revisit of method physics. Remote Sensing, 9(6): 528
  42. 42.
    Sun H, Zhou B C and Liu H X. 2019b. Spatial evaluation of Soil Moisture (SM), Land Surface Temperature (LST), and LST-Derived SM indexes dynamics during SMAPVEX12. Sensors, 19(5): 1247
  43. 43.
    Verstraeten W W, Veroustraete F and Feyen J. 2008. Assessment of evapotranspiration and soil moisture content across different scales of observation. Sensors, 8(1): 70-117
  44. 44.
    Vivoni E R, Moreno H A, Mascaro G, Rodriguez J C, Watts C J, Garatuza-Payan J and Scott R L. 2008. Observed relation between evapotranspiration and soil moisture in the North American monsoon region. Geophysical Research Letters, 35(22): L22403
  45. 45.
    Werbylo K L and Niemann J D. 2014. Evaluation of sampling techniques to characterize topographically-dependent variability for soil moisture downscaling. Journal of Hydrology, 516: 304-316
  46. 46.
    Wu X L, Walker J P, Rüdiger C, Panciera R and Gao Y. 2017. Medium-resolution soil moisture retrieval using the bayesian merging method. IEEE Transactions on Geoscience and Remote Sensing, 55(11): 6482-6493
  47. 47.
    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
  48. 48.
    Ye Q Y, Chai L N, Jiang L M and Zhao T J. 2014. A disaggregation approach for soil phase transition water content using AMSR2 and MODIS products. Journal of Remote Sensing, 18(6): 1147-1157
  49. 49.
    Zhan X W, Houser P R, Walker J P and Crow W T. 2006. A method for retrieving high-resolution surface soil moisture from Hydros L-Band radiometer and radar observations. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1534-1544
  50. 50.
    ZHANG Y, JIA Z Z, LIU S M, XU Z W, XU T R, YAO Y J, MA Y F, SONG L S, LI X, HU X, WANG Z Y, GUO Z X and ZHOU J. 2020. Advances in validation of remotely sensed land surface evapotranspiration.. Journal of Remote Sensing, 24(8):975-999
  51. 51.
    Zhao W, Li A N and Zhao T J. 2017. Potential of estimating surface soil moisture with the triangle-based empirical relationship model. IEEE Transactions on Geoscience and Remote Sensing, 55(11): 6494-6504
  52. 52.
    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
  53. 53.
    研究综述. 北京师范大学学报(自然科学版), 52(4): 479-485

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