A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province

Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatial-resolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data.

[1]  Thomas J. Jackson,et al.  Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99 , 2002, IEEE Trans. Geosci. Remote. Sens..

[2]  Yi Liu,et al.  Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations , 2018, Remote. Sens..

[3]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[4]  Chunlin Huang,et al.  Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST , 2017, Remote. Sens..

[5]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[6]  Yann Kerr,et al.  Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[7]  George P. Petropoulos,et al.  Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations , 2016 .

[8]  D. Entekhabi,et al.  The global distribution and dynamics of surface soil moisture , 2017 .

[9]  Niko E. C. Verhoest,et al.  A review of spatial downscaling of satellite remotely sensed soil moisture , 2017 .

[10]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[11]  Thomas J. Jackson,et al.  Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006 , 2011 .

[12]  Philippe Richaume,et al.  Soil Moisture Retrieval Using Neural Networks: Application to SMOS , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Wade T. Crow,et al.  A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Shuang Zhao,et al.  Response of Grassland Degradation to Drought at Different Time-Scales in Qinghai Province: Spatio-Temporal Characteristics, Correlation, and Implications , 2017, Remote. Sens..

[15]  C. Rosenzweig,et al.  Increased crop damage in the US from excess precipitation under climate change , 2002 .

[16]  Q. Qin,et al.  Modified perpendicular drought index (MPDI): a real-time drought monitoring method , 2007 .

[17]  B. Chapron,et al.  New approach for severe marine weather study using satellite passive microwave sensing , 2013 .

[18]  Zushuai Wei,et al.  A spatial downscaling method for SMAP soil moisture through visible and shortwave-infrared remote sensing data , 2020 .

[19]  Andreas Colliander,et al.  Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records , 2020, Remote. Sens..

[20]  Andrew W. Western,et al.  A terrain and data-based method for generating the spatial distribution of soil moisture , 2005 .

[21]  Wade T. Crow,et al.  Recent advances in (soil moisture) triple collocation analysis , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Emanuele Santi,et al.  Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation , 2013 .

[23]  Mohammad Reza Keshavarz,et al.  Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data , 2014 .

[24]  F. Aires,et al.  Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 1: Satellite data analysis , 2016 .

[25]  Dawei Han,et al.  Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application , 2013, Water Resources Management.

[26]  T. Jackson,et al.  Estimating surface soil moisture from SMAP observations using a Neural Network technique. , 2018, Remote sensing of environment.

[27]  Miaoling Liang,et al.  China land soil moisture EnKF data assimilation based on satellite remote sensing data , 2011 .

[28]  Klaus Scipal,et al.  A possible solution for the problem of estimating the error structure of global soil moisture data sets , 2008 .

[29]  Dennis D. Baldocchi,et al.  An analysis of soil moisture dynamics using multi-year data from a network of micrometeorological observation sites , 2007 .

[30]  Ad Stoffelen,et al.  Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target , 2014 .

[31]  Jun Qin,et al.  Land surface model calibration through microwave data assimilation for improving soil moisture simulations , 2016 .

[32]  S. Miller,et al.  Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach , 2003 .

[33]  Shiwei Li,et al.  Mapping Frozen Ground in the Qilian Mountains in 2004-2019 Using Google Earth Engine Cloud Computing , 2021, Remote. Sens..

[34]  Yves Chauvin,et al.  Backpropagation: theory, architectures, and applications , 1995 .

[35]  M. Drinkwater,et al.  The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: A follow on for European wind scatterometers , 2002 .

[36]  Peng Zhang,et al.  General introduction on payloads, ground segment and data application of Fengyun 3A , 2009 .

[37]  W. Wan,et al.  Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau , 2016 .

[38]  Jeffrey Piepmeier,et al.  Design and Development of the SMAP Microwave Radiometer Electronics , 2014 .

[39]  Ping Zhou,et al.  Spatiotemporal characteristics of dryness/wetness conditions across Qinghai Province, Northwest China , 2013 .

[40]  Wade T. Crow,et al.  Global-scale Evaluation of SMAP, SMOS and ASCAT Soil Moisture Products using Triple Collocation. , 2018, Remote sensing of environment.

[41]  Dara Entekhabi,et al.  An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval , 2011, IEEE Transactions on Geoscience and Remote Sensing.