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Evaluation of microwave remote sensing soil moisture products in farming-pastoral area of Shandian river basin

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：xieqx@radi.ac.cn
  • Introduction：谢秋霞，1990年生,女,博士研究生, 研究方向为微波遥感土壤水分反演。E-mail: xieqx@radi.ac.cn
XIE Qiuxia12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：jiali@radi.ac.cn
  • Introduction：贾立，1965年生，女，研究员，研究方向为陆地水循环遥感以及陆面过程、气候变化等。E-mail: jiali@radi.ac.cn
JIA Li1*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

CHEN Qiting1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

YIN Yanmin12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

MASSIMO Menenti1

résumé

SMAP (Soil Moisture Active and Passive) with a grid resolution of 9 km, ASCAT (Advanced Scatterometer) with a grid resolution of 0.1D (Degree), FY-3B and ESA-CCI (European Space Agency-Climate Change Initiative) with a grid resolution of 25 km, these satellite soil moisture (SM) products were widely used in drought monitoring, evapotranspiration estimation etc. applications. It is very important to evaluate these satellite SM products before using them. In this study, the approximate synchronous in-situ SM measurements (point-scale) and airborne observation-based soil moisture data (area-scale airborne SM with a grid resolution of 1 km) from the Comprehensive Remote Sensing Experiment of Water Cycle and Energy Balance in the Shandian river basin of Inner Mongolia in September, 2018 were used to evaluate the SMAP, ASCAT, FY-3B, ESA-CCI satellite SM products using RMSE (Mean Square Root Error), R (Correlation Coefficient), MAE (Mean Absolute Error), Bias and ubRMSE (unbiased Mean Square Root Error) etc. evaluation indexes. This study achieved the evaluation process from the point-scale in-situ SM measurements to the area-scale (1 km×1 km) airborne SM data to the area-scale (9 km×9 km, 0.1 D×0.1 D, 25 km×25 km) satellite SM products using airborne SM as the bridge between in-situ SM measurements and satellite SM products. The results dedicated that in bare soil area the airborne SM was more consistent with the in-situ SM measurements, the RMSE, MAE, Bias and ubRMSE and R values were 0.033 cm3/cm3, 0.030 cm3/cm3, -0.004 cm3/cm3, 0.033 cm3/cm3 and 0.474 respectively. Comparing with ASCAT, FY-3B and ESA-CCI satellite SM products, the SMAP satellite SM product were more consistent with in-situ SM measurements, RMSE, MAE, Bias, ubRMSE and R values were 0.037 cm3/cm3, 0.032 cm3/cm3, -0.008 cm3/cm3, 0.036 cm3/cm3 and 0.507 respectively. In addition, the R values between the satellite SM products (SMAP, ASCAT, FY-3B and ESA-CCI) and airborne SM data were higher, 0.735, 0.558, 0.558 and 0.575 respectively. Overall, the SMAP satellite SM product was more consistent with in-situ SM measurements and airborne SM in Shandian river basin area, then FY-3B and ESA-CCI SM products.

mots-clés

soil moisture;SMAP (Soil Moisture Active and Passive);ASCAT (Advanced Scatterometer);FY-3B;ESA-CCI

References

  1. 1.
    Bartalis Z, Wagner W, Naeimi V, Hasenauer S, Scipal K, Bonekamp H，Figa J and Anderson C. 2007. Initial soil moisture retrieval from the METOP-A Advanced Scatterometer. Geophysical Research Letters, 34(20):L20401
  2. 2.
    Brocca L, Hasenauer S, Lacava T, Melone F, Moramarco T, Wagner W, Dorigo W, Matgen P, Martínez-Fernández J, Llorens P, Latron J, Martin C and Bittelli M. 2011. Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe[J]. Remote Sensing of Environment, 115(12):3390-3408
  3. 3.
    Cai X T, Pan M, Chaney N W, Colliander A, Misra S, Cosh M H, Crow W T, Jackson T J and Wood E F. 2017. Validation of SMAP soil moisture for the smapvex15 field campaign using a hyper-resolution model. Water Resources Research, 53(4): 3013-3028
  4. 4.
    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
  5. 5.
    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. 2017. Validation and scaling of soil moisture in a semi-arid environment: SMAP validation experiment 2015 (smapvex15). Remote Sensing of Environment, 196: 101-112
  6. 6.
    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
  7. 7.
    Dorigo W, Wagner W, Albergel C, Albrecht F, Balsamo G, Brocca L, Chung D, Ertl M, Forkel M, Gruber A, Haas E, Hamer P D, Hirschi M, Ikonen J, de Jeu R, Kidd R, Lahoz W, Liu Y Y, Miralles D, Mistelbauer T, Nicolai-Shaw N, Parinussa R, Pratola C, Reimer C, van der Schalie R, Seneviratne S I, Smolander T and Lecomte P. 2017. ESA CCI soil moisture for improved earth system understanding: state-of-the art and future directions. Remote Sensing of Environment, 203: 185-215
  8. 8.
    Dorigo W A, Gruber A, De Jeu R A M, Wagner W, Stacke T, Loew A, Albergel C, Brocca L, Chung D, Parinussa R M and Kidd R. 2015. Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment, 162: 380-395
  9. 9.
    Entekhabi D, Njoku E, O'Neill P, Spencer M, Jackson T, Entin J, Im E and Kellogg K. 2008. The soil moisture active/passive mission (SMAP)//Proceedings of 2008 IEEE International Geoscience and Remote Sensing Symposium. Boston, USA: IEEE
  10. 10.
    Fan T X. 2002. Characteristics and functions of FengYun-3 meteorological satellite. Meteorological Science and Technology, 30(6): 321-327
  11. 11.
    Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji L Y, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Suen H P, Guo J, Xu N, Li W J, Zhao Y, Yang J, Yu C, Wang X, Fu H, Yu L, Dronova I, Hui F, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373
  12. 12.
    Jackson T J. 1993. III. Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes, 7(2): 139-152
  13. 13.
    Jackson T J, Bindlish R, Cosh M H, Zhao T J, Starks P J, Bosch D 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
  14. 14.
    Karthikeyan L, Pan M, Wanders N, Kumar D N and Wood E F. 2017. Four decades of microwave satellite soil moisture observations: part 2. Product validation and inter-satellite comparisons. Advances in Water Resources, 109: 236-252
  15. 15.
    Kawanishi T. 2003. The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA'S contribution to the EOS for global energy and water cycle studies. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 184-194
  16. 16.
    Kerr Y, Font J, Waldteufel P, Camps A, Bara J, Corbella I, Torres F, Duffo N, Vallilossera M and Caudal G. 2000. New radiometers: SMOS-A dual pol l-band 2D aperture synthesis radiometer//Proceedings of 2000 IEEE Aerospace Conference. Big Sky, USA: IEEE: 119-128
  17. 17.
    Kim H, Parinussa R, Konings A G, Wagner W, Cosh M H, Lakshmi V, Zohaib M and Choi M. 2018. Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204: 260-275
  18. 18.
    Liu J, Chai L N, Lu Z, Liu S M, Qu Y Q, Geng D Y, Song Y Z, Guan Y B, Guo Z X, Wang J and Zhu Z L. 2019. Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM soil moisture products over the Qinghai-Tibet plateau and its surrounding areas. Remote Sensing, 11(7): 792
  19. 19.
    Liu Y Y, Dorigo W A, Parinussa R M, de Jeu R A M, Wagner W, McCabe M F, Evans J P and van Dijk A I J M. 2012. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment, 123: 280-297
  20. 20.
    Mohanty B P, Cosh M H, Lakshmi V and Montzka C. 2017. Soil Moisture Remote Sensing: State-of-the-Science. Vadose Zone Journal, 16(1) [DOI: 10.2136/vzj2016.10.0105]
  21. 21.
    Shi J, Jiang L, Zhang L, Chen K S, Wigneron J P, Chanzy A and Jackson T J. 2006. Physically based estimation of bare-surface soil moisture with the passive radiometers. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3145-3153
  22. 22.
    Spencer M, Chan S, Veilleux L and Wheeler K. 2009. The soil moisture active/passive (SMAP) mission radar: a novel conically scanning SAR//Proceedings of 2009 IEEE Radar Conference. Pasadena, USA: IEEE: 1-4
  23. 23.
    Sun Y L,Zhao T J,Li E C,Luan Y H,Wan G Y,Xu H X,Zhao F,Yao C B,Lu L Q,Hu L and Geng D Y. 2021. Radiometer calibration of airborne L-band active and passive microwave detector. National Remote Sensing Bulletin， 25(4):918-928
  24. 24.
    The GLACE Team, Koster R D, Dirmeyer P A, Guo Z C, Bonan G, Chan E, Cox P, Gordon C T, Kanae S, Kowalczyk E, Lawrence D, Liu P, Lu C H, Malyshev S, McAvaney B, Mitchell K, Mocko D, Oki T, Oleson K, Pitman1 A, Sud Y C, Taylor C M, Verseghy D, Vasic R, Xue Y K and Yamada T. 2004. Regions of strong coupling between soil moisture and precipitation. Science, 305(5687): 1138-1140
  25. 25.
    Wagner W, Dorigo W, De Jeu R, Fernandez D, Benveniste J, Haas E and Ertl M. 2012. Fusion of active and passive microwave observations to create an essential climate variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, I-7: 315-321
  26. 26.
    Wagner W, Lemoine G and Rott H. 1999. A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70(2): 191-207
  27. 27.
    William B R. 2016. LANDMET Ancillary Soil Moisture data L3 V1, (Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).)
  28. 28.
    Xie Q X, Menenti M and Jia L. 2019. Improving the AMSR-E/NASA Soil Moisture Data Product Using In-Situ Measurements from the Tibetan Platea. Remote Sensing, 11(23): 2748
  29. 29.
    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
  30. 30.
    Zhao T J, Shi J C, Bindlish R, Jackson T, Cosh M, Jiang L M, Zhang Z J and Lan H M. 2015. Parametric exponentially correlated surface emission model for l-band passive microwave soil moisture retrieval. Physics and Chemistry of the Earth, Parts A/B/C, 83-84: 65-74
  31. 31.
    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
  32. 32.
    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

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