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Reconstructing Monthly ECV Global Soil Moisture with an Improved Spatial Resolution

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Abstract

Remote sensing techniques have provided global covered soil moisture at high temporal resolution, however, the coarse spatial resolution and the data gaps have greatly reduced their potential values in large numbers of practical and regional applications. This study proposed a two-steps reconstruction approach for reconstructing satellite-based soil moisture products (ECV) at an improved spatial resolution. The reconstruction model implemented the Random Forests (RF) regression algorithm to simulate the relationships between soil moisture and environmental variables, and takes advantages of the high spatial resolution of optical remote sensing products: the data gaps of ECV soil moisture products were firstly filled by the estimation model trained using available pixels of the ECV products and corresponding environmental variables; then a spatial downscaling was carried out to the gap-filled ECV products to obtain the reconstructed soil moisture with fine spatial resolution (0.05°). As a result, the reconstructed soil moisture well fill the data gaps of the original ECV products and nicely reproduced the original soil moisture values (R2 > 0.98). The spatial resolution and variation details of the soil moisture products were also improved significantly. Validation results indicated that the reconstructed soil moisture showed comparable good performance (average R2 = 0.66) as the original ECV products (average R2 = 0.65) and nicely reflect the temporal behavior of ground-based measurements. As a result, the reconstructed soil moisture well filled the data gaps and greatly improved the spatial resolution of ECV products.

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References

  • Belgiu M, Drăguţ L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31

    Article  Google Scholar 

  • Breiman L (2001) Random Forests Machine Learning 45:5–32

    Article  Google Scholar 

  • Champagne C, McNairn H, Berg AA (2011) Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing. Remote Sens of Environ 115:2434–2444

    Article  Google Scholar 

  • Champagne C, Berg AA, Mcnairn H, Drewitt G, Huffman T (2012) Evaluation of soil moisture extremes for agricultural productivity in the Canadian prairies. Agricultural & Forest Meteorology 165:1–11

    Article  Google Scholar 

  • Chauhan NS, Miller S, Ardanuy P (2003) Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach. Int J Remote Sens 24:4599–4622

    Article  Google Scholar 

  • Coulibaly P, Evora ND (2007) Comparison of neural network methods for infilling missing daily weather records. J Hydrol 341:27–41. https://doi.org/10.1016/j.jhydrol.2007.04.020

    Article  Google Scholar 

  • Cui Y et al (2016) Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau. J Hydrol 543:242–254. https://doi.org/10.1016/j.jhydrol.2016.10.005

    Article  Google Scholar 

  • Dorigo W, de Jeu R (2016) Satellite soil moisture for advancing our understanding of earth system processes and climate change. Int J Appl Earth Obs Geoinf 48:1–4. https://doi.org/10.1016/j.jag.2016.02.007

    Article  Google Scholar 

  • Dorigo W et al (2017) ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens Environ. https://doi.org/10.1016/j.rse.2017.07.001

  • Fang L, Hain CR, Zhan X, Anderson MC (2016) An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model. Int J Appl Earth Obs Geoinf 48:37–50. https://doi.org/10.1016/j.jag.2015.10.006

    Article  Google Scholar 

  • Fern N-DM, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181

    Google Scholar 

  • Im J, Park S, Rhee J, Baik J, Choi M (2016) Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environ Earth Sci:75–1120

  • Immerzeel WW, Rutten MM, Droogers P (2009) Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula. Remote Sens Environ 113:362–370

    Article  Google Scholar 

  • Jia S, Zhu W, Lű A, Yan T (2011) A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote Sens Environ 115:3069–3079

    Article  Google Scholar 

  • Jing W, Yang Y, Yue X, Zhao X (2016) A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China. Remote Sens 8:835. https://doi.org/10.3390/rs8100835

    Article  Google Scholar 

  • Kerr YH et al (2012) The SMOS Soil Moisture Retrieval Algorithm. IEEE Trans Geosci Remote Sens 50:1384–1403. https://doi.org/10.1109/TGRS.2012.2184548

    Article  Google Scholar 

  • Kerr YH et al (2010) The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle. Proc IEEE 98:666–687. https://doi.org/10.1109/JPROC.2010.2043032

    Article  Google Scholar 

  • Koike T, Nakamura Y, Kaihotsu I, Davaa G, Matsuura N, Tamagawa K, Fujii H (2004) Development of an advanced microwave scanning radiometer (AMSR-E) Algorithm for soil moisture and vegetation water content. Doboku Gakkai Ronbunshuu B 48:217–222

    Google Scholar 

  • Liu YY et al (2011) Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol Earth Syst Sci 15:425–436. https://doi.org/10.5194/hess-15-425-2011

    Article  Google Scholar 

  • Long D et al (2014) Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data. Remote Sens Environ 155:145–160. https://doi.org/10.1016/j.rse.2014.08.006

    Article  Google Scholar 

  • Mccabe MF, Wood EF, Wójcik R, Pan M, Sheffield J, Gao H, Su H (2008) Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies. Remote Sens Environ 112:430–444

    Article  Google Scholar 

  • Moore ID, Gessler P, Nielsen G, Peterson G (1993) Soil attribute prediction using terrain analysis. Soil Sci Soc Am J 57:443–452

    Article  Google Scholar 

  • Naeimi V, Scipal K, Bartalis Z, Hasenauer S, Wagner W (2009) An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations. IEEE Trans Geosci Remote Sens 47:1999–2013. https://doi.org/10.1109/TGRS.2008.2011617

    Article  Google Scholar 

  • Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26:217–222

    Article  Google Scholar 

  • Peng J, Loew A, Merlin O, Verhoest NEC (2017) A review of spatial downscaling of satellite remotely sensed soil moisture. Rev Geophys 55:341–366. https://doi.org/10.1002/2016rg000543

    Article  Google Scholar 

  • Peng J, Niesel J, Loew A, Zhang S, Wang J (2015) Evaluation of Satellite and Reanalysis Soil Moisture Products over Southwest China Using Ground-Based Measurements. Remote Sens 7:15729–15747. https://doi.org/10.3390/rs71115729

    Article  Google Scholar 

  • Petropoulos G, Carlson TN, Wooster MJ, Islam S (2009) A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Prog Phys Geogr 33:224–250

    Article  Google Scholar 

  • Piles M et al (2011a) Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data. IEEE Transactions on Geoscience & Remote Sensing 49:3156–3166

    Article  Google Scholar 

  • Piles M et al (2011b) Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data. IEEE Trans Geosci Remote Sens 49:3156–3166. https://doi.org/10.1109/tgrs.2011.2120615

    Article  Google Scholar 

  • Piles M, Entekhabi D, Camps A (2009) A Change Detection Algorithm for Retrieving High-Resolution Soil Moisture From SMAP Radar and Radiometer Observations. IEEE Transactions on Geoscience & Remote Sensing 47:4125–4131

    Article  Google Scholar 

  • Qiu J, Gao Q, Wang S, Su Z (2016) Comparison of temporal trends from multiple soil moisture data sets and precipitation: The implication of irrigation on regional soil moisture trend. Int J Appl Earth Obs Geoinf 48:17–27. https://doi.org/10.1016/j.jag.2015.11.012

    Article  Google Scholar 

  • Ray RL, Jacobs JM, Cosh MH (2010) Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US Remote. Sensing of Environment 114:2624–2636. https://doi.org/10.1016/j.rse.2010.05.033

    Article  Google Scholar 

  • Sheffield J, Wood EF (2008) Global Trends and Variability in Soil Moisture and Drought Characteristics, 1950-2000, from Observation-Driven Simulations of the Terrestrial Hydrologic Cycle. J Clim 21:432–458. https://doi.org/10.1175/2007jcli1822.1

    Article  Google Scholar 

  • Srivastava PK, Han D, Ramirez MR, Islam T (2013) Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application. Water Resour Manag 27:3127–3144. https://doi.org/10.1007/s11269-013-0337-9

    Article  Google Scholar 

  • Temimi M, Lacava T, Lakhankar T, Tramutoli V, Ghedira H, Ata R, Khanbilvardi R (2011) A multi-temporal analysis of AMSR-E data for flood and discharge monitoring during the 2008 flood in Iowa. Hydrol Process 25:2623–2634

    Article  Google Scholar 

  • Zhao W, Li A (2013) A Downscaling Method for Improving the Spatial Resolution of AMSR-E Derived Soil Moisture Product Based on MSG-SEVIRI Data. Remote Sens 5:6790–6811

    Article  Google Scholar 

Download references

Acknowledgments

This study were jointly supported by the GDAS’ Special Project of Science and Technology Development (2017GDASCX-0101, 2018GDASCX-0904), the Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06D336), the National Natural Science Foundation of China (41601175, 41401430), and the National Earth System Science Data Sharing Infrastructure (http://www.geodata.cn/).

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Authors and Affiliations

  1. Guangzhou Institute of Geography, Guangzhou, 510070, China

    Wenlong Jing & Xiaodan Zhao

  2. Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou, 510070, China

    Wenlong Jing & Xiaodan Zhao

  3. Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou, 510070, China

    Wenlong Jing & Xiaodan Zhao

  4. College of Environment and Planning, Henan University, Kaifeng, 475004, China

    Pengyan Zhang

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  1. Wenlong Jing
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  2. Pengyan Zhang
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  3. Xiaodan Zhao
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Correspondence to Pengyan Zhang.

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Jing, W., Zhang, P. & Zhao, X. Reconstructing Monthly ECV Global Soil Moisture with an Improved Spatial Resolution. Water Resour Manage 32, 2523–2537 (2018). https://doi.org/10.1007/s11269-018-1944-2

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  • DOI: https://doi.org/10.1007/s11269-018-1944-2

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