Volume 12 Issue 2
Jun.  2019
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Ke Zhang, Li-jun Chao, Qing-qing Wang, Ying-chun Huang, Rong-hua Liu, Yang Hong, Yong Tu, Wei Qu, Jin-yin Ye. 2019: Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China. Water Science and Engineering, 12(2): 85-97. doi: 10.1016/j.wse.2019.06.001
Citation: Ke Zhang, Li-jun Chao, Qing-qing Wang, Ying-chun Huang, Rong-hua Liu, Yang Hong, Yong Tu, Wei Qu, Jin-yin Ye. 2019: Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China. Water Science and Engineering, 12(2): 85-97. doi: 10.1016/j.wse.2019.06.001

Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China

doi: 10.1016/j.wse.2019.06.001
Funds:  This study was supported by the National Key Research and Development Program of China (Grant No. 2016YFC0402701), the National Natural Science Foundation of China (Grants No. 51879067 and 51579131), the Natural Science Foundation of Jiangsu Province (Grant No. BK20180022), the Six Talent Peaks Project in Jiangsu Province (Grant No. NY-004), the Fundamental Research Funds for the Central Universities of China (Grants No. 2018B42914 and 2018B04714), the China National Flash Flood Disaster Prevention and Control Project (Grant No. 126301001000150068), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX18_0572).
More Information
  • Corresponding author: Ke Zhang
  • Received Date: 2019-01-08
  • Rev Recd Date: 2019-05-13
  •     The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy. Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP, SMOS, AMSR2, FY3B, and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture. We further evaluated our product, the official soil moisture products of the five satellites, and the ensemble mean (i.e., arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province, China. The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas. The root mean square error (RMSE) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province, respectively. Relative to the ensemble mean of the five satellite products, our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province, respectively. This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.

     

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  • Aires, F., Aznay, O., Prigent, C., Paul, M., Bernardo, F., 2012. Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A. Journal of Geophysical Research: Atmospheres, 117(D18). https://doi.org/10.1029/2011JD017188.
    Berg, A., Lintner, B.R., Findell, K.L., Malyshev, S., 2014. Impact of soil moisture-atmosphere interactions on surface temperature distribution. Journal of Climate, 27(21), 7976-7993. https://doi.org/10.1175/jcli-d-13-00591.1.
    Chan, S.K., Bindlish, R., O'Neill, P.E., Njoku, E., 2016. Assessment of the SMAP passive soil moisture product. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 1-14. https://doi.org/10.1109/TGRS.2016.2561938.
    Choudhury, B.J., Schmugge, T.J., Chang, A., Newton, R.W., 1979. Effect of surface roughness on the microwave emission from soil. J. Geophys. Res., 84(C9), 5699-5706. https://doi.org/10.1029/JC084iC09p05699.
    Choudhury, B.J., Schmugge, T.J., Mo, T., 1982. A parameterization of effective soil temperature for microwave emission. J. Geophys. Res., 87(C2), 1301-1304. https://doi.org/10.1029/JC087iC02p01301.
    Crow, W.T., Ryu, D., 2009. A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals. Hydrology and Earth System Sciences, 13(1), 1-16. https://doi.org/10.5194/hess-13-1-2009.
    Dorigo, W.A., Wanger, W., Roland, H., Sebastian, H., Christoph, P., Matthias, D., Mecklenburg, S., Peter, V.O., Robock, A., Tj, J., 2011. The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrology and Earth System Sciences, 15(5), 1675-1698. https://doi.org/10.5194/hessd-8-1609-2011.
    Dorigo, W.A., Gruber, A., De Jeu, R.A.M., Wanger, W., Stacke, T., Loew, A., Albergel, C., Brocca, L., Chung, D., Parinussa, R.M., et al., 2015. Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment, 162, 380-395. https://doi.org/10.1016/j.rse.2014.07.023.
    Du, J.Y., Kimball, J.S., Jones, L.A., 2016. Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 54(1), 597-608. https://doi.org/10.1109/TGRS.2015.2462758.
    Enenkel, M., Reimer, C., Dorigo, W., Wanger, W., Pfeil, I., Parinussa, R., Jeu, R.D., 2016. Combining satellite observations to develop a global soil moisture product for near-real-time applications. Hydrology and Earth System Sciences, 20(10), 4191-4208. https://doi.org/10.5194/hess-20-4191-2016.
    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., et al., 2010. The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704-716. 10.1109/jproc.2010.2043918.https://doi.org/
    Fischer, G., Nachtergaele, F., Prieler, S., Van Velthuizen, H.T., Verelst, L., Wiberg, D., 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008), Laxenburg.
    Flanagan, L.B., Johnson, B.G., 2005. Interacting effects of temperature, soil moisture and plant biomass production on ecosystem respiration in a northern temperate grassland. Agricultural and Forest Meteorology, 130(3-4), 237-253. https://doi.org/10.1016/j.agrformet.2005.04.002.
    Ford, T.W., Quiring, S.M., Frauenfeld, O.W., Rapp, A.D., 2015. Synoptic conditions related to soil moisture-atmosphere interactions and unorganized convection in Oklahoma. Journal of Geophysical Research: Atmospheres, 120(22), 11519-11535. https://doi.org/10.1002/2015JD023975.
    Friedl, M.A., Menashe, D.S., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., Huang, X.M., 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114(1), 168-182. https://doi.org/10.1016/j.rse.2009.08.016.
    Gallego-Elvira, B., Taylor, C.M., Harris, P.P., Ghent, D., Veal, K.L., Folwell, S.S., 2016. Global observational diagnosis of soil moisture control on the land surface energy balance. Geophysical Research Letters, 43(6), 2623-2631. https://doi.org/10.1002/2016GL068178.
    He, M., Kimball, J.S., Running, S., Ballantyne, A., Guan, K., Huemmrich, F., 2016. Satellite detection of soil moisture related water stress impacts on ecosystem productivity using the MODIS-based photochemical reflectance index. Remote Sensing of Environment, 186, 173-183. https://doi.org/10.1016/j.rse.2016.08.019.
    Huang, Y.Y., Gerber, S., Huang, T.Y., Lichstein, J.W., 2016. Evaluating the drought response of CMIP5 models using global gross primary productivity, leaf area, precipitation, and soil moisture data. Global Biogeochemical Cycles, 30(12), 1827-1846. https://doi.org/10.1002/2016GB005480.
    Jackson, T.J., Schmugge, T.J., 1991. Vegetation effects on the microwave emission of soils. Remote Sens. Environ., 36(3), 203-212. https://doi.org/10.1016/0034-4257(91)90057-D.
    Jackson, T.J., 1993. III. Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes, 7(2), 139-152.
    Jia, X., Zha, T.S., Gong, J.N., Wang, B., Zhang, Y.Q., Wu, B., Qin, S.G., Peltola, H., 2016. Carbon and water exchange over a temperate semi-arid shrubland during three years of contrasting precipitation and soil moisture patterns. Agricultural and forest meteorology, (228-229), 120-129. https://doi.org/10.1016/j.agrformet.2016.07.007.
    Juszak, I., Eugster, W., Heijmans, M.M., Schaepman-Strub, G., 2016. Contrasting radiation and soil heat fluxes in Arctic shrub and wet sedge tundra. Biogeosciences, 13(13), 4049-4064. https://doi.org/10.5194/bg-13-4049-2016.
    Kerr, Y.H., Waldteufel, P., Richaume, P., Wigneron, J.P., Ferrazzoli, P., Mahmoodi, A., Al, B.A., Cabot, F., Gruhier, C., Juglea, S.E., et al., 2012. The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1384-1403. https://doi.org/10.1109/TGRS.2012.2184548.
    Koike, T., 2013. Description of the GCOM-W1 AMSR2 Soil Moisture Algorithm. Japan Aerospace Exploration Agency Earth Observation Research Center.
    Kolassa, J., Gentine, P., Prigent, C., Aires, F., 2016. Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy, Part 1: Satellite data analysis. Remote Sensing of Environment, 173, 1-14. https://doi.org/10.1016/j.rse.2015.11.011.
    Kolassa, J., Reichle, R.H., Draper, C.S., 2017. Merging active and passive microwave observations in soil moisture data assimilation. Remote Sensing of Environment, 191, 117-130. https://doi.org/10.1016/j.rse.2017.01.015.
    Li, T., Cui, Y., Liu, A., 2017. Spatiotemporal dynamic analysis of forest ecosystem services using “big data”: A case study of Anhui Province, central-eastern China. Journal of Cleaner Production, 142, 589-599. https://doi.org/10.1016/j.jclepro.2016.09.118.
    Lin, T.-S., Cheng, F.-Y., 2016. Impact of soil moisture initialization and soil texture on simulated land-atmosphere interaction in Taiwan. Journal of Hydrometeorology, 17(5), 1337-1355.
    Lindell, D.B., Long, D.G., 2016. High-resolution soil moisture retrieval with ASCAT. IEEE Geosci. Remote Sensing Lett., 13(7), 972-976. https://doi.org/10.1109/LGRS.2016.2557321 .
    Liu, S., Roujean, J.-L., Tchuente, A.T.K., Ceamanos, X., Calvet, J.-C., 2014. A parameterization of SEVIRI and MODIS daily surface albedo with soil moisture: Calibration and validation over southwestern France. Remote Sensing of Environment, 144, 137-151. https://doi.org/10.1016/j.rse.2014.01.016.
    Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M., Wagner, W., McCabe, M.F., Evan, J.P., 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. https://doi.org/10.1016/j.rse.2012.03.014.
    McInerney, E., Helton, A.M., 2016. The effects of soil moisture and emergent herbaceous vegetation on carbon emissions from constructed wetlands. Wetlands, 36(2), 275-284. https://doi.org/10.1007/s13157-016-0736-9.
    Mironov, V.L., Kosolapova, L.G., Fomin, S.V., 2009. Physically and mineralogically based spectroscopic dielectric model for moist soils. IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2059-2070. https://doi.org/10.1109/TGRS.2008.2011631.
    Morbidelli, R., Saltalippi, C., Flammini, A., Corradini, C., Brocca, L., Govindaraju, R.S., 2016. An investigation of the effects of spatial heterogeneity of initial soil moisture content on surface runoff simulation at a small watershed scale. Journal of Hydrology, 539, 589-598. https://doi.org/10.1016/j.jhydrol.2016.05.067.
    Njoku, E.G., Li, L., 1999. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 79-93. https://doi.org/10.1109/36.739125.
    Njoku, E.G., Jackson, T.J., Lakshmi, V., Chan, T.K., Nghiem, S.V., 2003. Soil moisture retrieval from AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 215-229. https://doi.org/10.1109/TGRS.2002.808243.
    O'Neill, P., Chan, S., Njoku, E., Jackson, T., Bindlish, R., 2015. Soil Moisture Active Passive (SMAP) Algorithm Theoretical Basis Document Level 2 & 3 Soil Moisture (Passive) Data Products. Jet Propulsion Laboratory, Pasadena.
    Paloscia, S., Macelloni, G., Santi, E., Koike, T., 2001. A multifrequency algorithm for the retrieval of soil moisture on a large scale using microwave data from SMMR and SSM/I satellites. IEEE Transactions on Geoscience and Remote Sensing, 39(8), 1655-1661. https://doi.org/10.1109/36.942543.
    Parinussa, R.M., Wang, G., Holmes, T.R.H., Liu, Y., Dolman, A.J., de Jeu, R., Jiang, T., Zhang, P., 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.
    Parinussa, R.M., Holmes, T.R., Wanders, N., Dorigo, W.A., de Jeu, R.A., 2015. A preliminary study toward consistent soil moisture from AMSR2. Journal of Hydrometeorology, 16(2), 932-947. https://doi.org/10.1175/JHM-D-13-0200.1.
    Piles, M., Petropoulos, G.P., Sánchez, N., González-Zamora, Á., 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. https://doi.org/10.1016/j.rse.2016.02.048.
    Reichle, R., De Lannoy, G., Liu, Q., Ardizzone, J., Kimball, J., Koster, R., 2016. SMAP Level 4 surface and root zone soil moisture. In: Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, Beijing, pp. 136-138.
    Rodríguez-Fernández, N.J., Aires, F., Richaume, P., Kerr, Y.H., Prigent, C., Kolassa, J., Cabot, F., Jiménez, C., Mahmoodi, A., Drusch, M., 2015. Soil moisture retrieval using neural networks: Application to SMOS. IEEE Transactions on Geoscience and Remote Sensing, 53(11), 5991-6007. https://doi.org/10.1109/TGRS.2015.2430845.
    Rodríguez-Fernández, N.J., Aires, F., Richaume, P., Kerr, Y.H., Prigent, C., Kolassa, J., Cabot, F., Jimenez, C., Mahmoodi, A., Drusch, M., 2016. Long term global surface soil moisture fields using an SMOS-trained neural network applied to AMSR-E data. Remote Sensing, 8(11), 959. https://doi.org/10.1109/TGRS.2015.2430845.
    Shi, J.C., Jiang, L., Zhang, L., Chen, K.S., Wigneron, J.P., Chanzy, A., 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. https://doi.org/10.1109/TGRS.2006.876706.
    Song, C., Jia, L., 2016. A method for downscaling FengYun-3B soil moisture based on apparent thermal inertia. Remote Sensing, 8(9). https://doi.org/10.3390/rs8090703.
    Suarez, A., Mahmood, R., Quintanar, A.I., Beltran-Przekurat, A., Pielke Sr, R., 2014. A comparison of the MM5 and the Regional Atmospheric Modeling System simulations for land-atmosphere interactions under varying soil moisture. Tellus A: Dynamic Meteorology and Oceanography, 66(1), 21486.
    Sugathan, N., Biju, V., Renuka, G., 2014. Influence of soil moisture content on surface albedo and soil thermal parameters at a tropical station. Journal of Earth System Science, 123(5), 1115-1128.
    Ulaby, F.T., Moore, R.K., Fung, A.K., 1981. Microwave Remote Sensing Active and Passive: Volume 1 Microwave Remote Sensing Fundamentals and Radiometry. Artech House, Norwood, p. 456.
    Van der Schalie, R., De Jeu, R., Parinussa, R., Rodriguez-Fernandez, N., Kerr, Y., Al-Yaari, A., Wigneron, J.-P., Drusch, M., 2018. The effect of three different data fusion approaches on the quality of soil moisture retrievals from multiple passive microwave sensors. Remote Sensing, 10(1), 107. https://doi.org/10.3390/rs10010107.
    Wigneron, J.-P., Jackson, T.J., Neill, P.O., Lannoy, G.D., de Rosnay, P., Walker, J.P., Ferrazzoli, P., Mironov, V., Bircher, S., Grant, J.P., et al., 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. https://doi.org/10.1016/j.rse.2017.01.024.
    Xia, J., Zhao, Z., Sun, J., Liu, J., Zhao, Y., 2017. Response of stem sap flow and leaf photosynthesis in Tamarix chinensis to soil moisture in the Yellow River Delta, China. Photosynthetica, 55(2), 368-377.
    Xu, L.K., Baldocchi, D.D., Tang, J.W., 2004. How soil moisture, rain pulses, and growth alter the response of ecosystem respiration to temperature. Global Biogeochemical Cycles, 18(4). https://doi.org/10.1029/2004GB002281.
    Xu, Z., Zhou, G., 2005. Effects of soil moisture on gas exchange, partitioning of fed 14CO2 and stable carbon isotope composition (δ13C) of Leymus chinensis under two different diurnal temperature variations. Journal of Agronomy and Crop Science, 191(1), 27-34.
    Yang, K., Qin, J., Zhao, L., Chen, Y.Y., Tang, W.J., Han, M.L., La, Z., Chen, Z.Q., Lü, N., Ding, B.H., et al., 2013. A multiscale soil moisture and freeze-thaw monitoring network on the third pole. Bull. Am. Meteorol. Soc., 94(12), 1907-1916. https://doi.org/10.1175/Bams-D-12-00203.1.
    Yao, P.P., Shi, J.C., Zhao, T.J., Lu, H., Al-Yaari, A., 2017. Rebuilding long time series global soil moisture products using the neural network adopting the microwave vegetation index. Remote Sensing, 9(1), 35. https://doi.org/10.3390/rs9010035.
    Zeng, J.Y., Li, Z., Chen, Q., Bi, H.Y., Qiu, J.X., Zou, P.F., 2015. Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations. Remote Sensing of Environment, 163, 91-110. https://doi.org/10.1016/j.rse.2015.03.008. 
    Zhang, K., Kimball, J.S., Nemani, R.R., Running, S.W., Hong, Y., Gourley, J.J., Yu, Z.B., 2015. Vegetation greening and climate change promote multidecadal rises of global land evapotranspiration. Scientific  Reports, 5, 15956. https://doi.org/10.1038/srep15956.
    Zhang, Y.F., Wang, X.P., Hu, R., Pan, Y.X., Zhang, H., 2014. Variation of albedo to soil moisture for sand dunes and biological soil crusts in arid desert ecosystems. Environmental Earth Sciences, 71(3), 1281-1288.
    Zhao, L., Yang, K., Qin, J., Chen, Y.Y., Tang, W.J., Lu, H., Yang, Z.L., 2014. The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau. Remote Sensing of Environment, 152, 345-355. https://doi.org/10.1016/j.rse.2014.07.005.
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