Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model

Abstract. Estimating volumetric soil moisture (Mv) and surface roughness (S) are the key parameters for numerous agricultural and hydrological applications. Although these two parameters can be effectively retrieved from synthetic aperture radar (SAR) data, the presence of vegetation can negatively affect the results. A method was proposed to accurately estimate Mv and S over vegetated agricultural areas. The method was based on applying the machine learning inversion approach along with SAR data to invert a combination of the parameterized water cloud model (PWCM) and the calibrated integral equation model (CIEM). The soil backscattered component in water cloud model (WCM) was generated by CIEM to be applied to the WCM parameterization and dataset simulation. Three machine learning algorithms, including the support vector regression (SVR), multi-output SVR (MSVR), and artificial neural network (ANN), were employed to model the relationship between the simulated dataset variables. The genetic algorithm was also applied to optimize the models’ parameters. The inversion technique results demonstrated that the MSVR and ANN had the highest accuracy in estimating Mv and S due to their better structures. The SMAPVEX-16 in situ dataset, along with three Sentinel-1 images, was applied to evaluate the accuracy of the WCM parameterization and the proposed method for Mv and S estimation. The accuracies of the PWCM in the VV and VH polarizations of Sentinel-1 C-band data were reasonable for VWC  <  2.5  kg  /  m2 [root-mean-square error (RMSE) = 1.44 and 1.77 dB, respectively]. Additionally, it was observed that the trained SVR, MSVR, and ANN had similar results for different VWC values. In summary, the proposed method had high potential in vegetated agricultural areas with VWC  <  2.5  kg  /  m2, for which the RMSEs were 4 to 7 vol. % and 0.35 to 0.46 cm depending on the VWC values in retrieving Mv and S, respectively.

[1]  Malcolm Davidson,et al.  On current limits of soil moisture retrieval from ERS-SAR data , 2002, IEEE Trans. Geosci. Remote. Sens..

[2]  Luis Alonso,et al.  Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation , 2011, IEEE Geoscience and Remote Sensing Letters.

[3]  Mehrez Zribi,et al.  Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas , 2017, Remote. Sens..

[4]  A. Chehbouni,et al.  Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation , 2011 .

[5]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[6]  B. Brisco,et al.  Effect of surface soil moisture gradients on modelling radar backscattering from bare fields , 1997 .

[7]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[8]  Mahdi Hasanlou,et al.  Semi-analytical soil moisture retrieval using PolSAR imagery , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[9]  Sabrina Esch,et al.  Soil moisture index from ERS-SAR and its application to the analysis of spatial patterns in agricultural areas , 2018 .

[10]  Jieping Ye,et al.  Feature Constrained Multi-Task Learning Models for Spatiotemporal Event Forecasting , 2017, IEEE Transactions on Knowledge and Data Engineering.

[11]  Slobodan P. Simonovic,et al.  Short term streamflow forecasting using artificial neural networks , 1998 .

[13]  Jiancheng Shi,et al.  Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data , 1997, IEEE Trans. Geosci. Remote. Sens..

[14]  Farhad Samadzadegan,et al.  SVM-based hyperspectral image classification using intrinsic dimension , 2013, Arabian Journal of Geosciences.

[15]  N. Baghdadi,et al.  Soil moisture retrieval over irrigated grassland using X-band SAR data , 2016 .

[16]  Mahmod Reza Sahebi,et al.  Assessment of Different Backscattering Models for Bare Soil Surface Parameters Estimation from SAR Data in band C, L and P , 2016 .

[17]  Li Jianing,et al.  Inversion of Soil Moisture from Backscattering Coefficient Using LS-SVM , 2013 .

[18]  A. Kalra,et al.  Estimating soil moisture using remote sensing data: A machine learning approach , 2010 .

[19]  Meredith Beilfuss.  Remote Sensing Lab , 2009 .

[20]  Yisok Oh,et al.  Condition for precise measurement of soil surface roughness , 1998, IEEE Trans. Geosci. Remote. Sens..

[21]  Pascale C. Dubois,et al.  Measuring soil moisture with imaging radars , 1995, IEEE Trans. Geosci. Remote. Sens..

[22]  Heather McNairn,et al.  Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm , 2019, Remote Sensing of Environment.

[23]  Mojtaba Dehmollaian,et al.  Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data , 2017, Remote. Sens..

[24]  Mehrez Zribi,et al.  Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands , 2017, Remote. Sens..

[25]  Li-Chiu Chang,et al.  Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting. , 2019, The Science of the total environment.

[26]  Mahmod Reza Sahebi,et al.  Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks , 2019, Sensors.

[27]  Mehrez Zribi,et al.  Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications , 2016, Remote. Sens..

[28]  Frédéric Baup,et al.  Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[29]  Xiaojing Bai,et al.  A Synergistic Methodology for Soil Moisture Estimation in an Alpine Prairie Using Radar and Optical Satellite Data , 2014, Remote. Sens..

[30]  P. Paillou,et al.  Relationship between profile length and roughness variables for natural surfaces , 2000 .

[31]  Kamal Sarabandi,et al.  An empirical model and an inversion technique for radar scattering from bare soil surfaces , 1992, IEEE Trans. Geosci. Remote. Sens..

[32]  Mehrez Zribi,et al.  Calibration of the Integral Equation Model for SAR data in C‐band and HH and VV polarizations , 2006 .

[33]  Xiliang Ni,et al.  Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data , 2019, Remote. Sens..

[34]  F. Ulaby,et al.  Vegetation modeled as a water cloud , 1978 .

[35]  N. Baghdadi,et al.  Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks , 2012 .

[36]  Imen Gherboudj,et al.  Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data , 2011 .

[37]  Mehrez Zribi,et al.  Evaluation of radar backscatter models IEM, OH and Dubois using experimental observations , 2006 .

[38]  Kazem Abhary,et al.  An innovative framework for designing genetic algorithm structures , 2017, Expert Syst. Appl..

[39]  M. Zribi,et al.  A new empirical model to retrieve soil moisture and roughness from C-band radar data , 2003 .

[40]  Mehrez Zribi,et al.  Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data , 2019, Remote. Sens..

[41]  Kamal Sarabandi,et al.  Michigan microwave canopy scattering model , 1990 .

[42]  F. Ulaby,et al.  Active Microwave Soil Moisture Research , 1986, IEEE Transactions on Geoscience and Remote Sensing.

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

[44]  S. Simonovic,et al.  An Artificial Neural Network model for generating hydrograph from hydro-meteorological parameters , 2005 .

[45]  M. Sahebi,et al.  Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements , 2004 .

[46]  Sudhir Kumar Singh,et al.  Synergetic methodology for estimation of soil moisture over agricultural area using Landsat-8 and Sentinel-1 satellite data , 2019, Remote Sensing Applications: Society and Environment.

[47]  Mehrez Zribi,et al.  New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion , 2005 .

[48]  Frédéric Baup,et al.  A New Empirical Model for Radar Scattering from Bare Soil Surfaces , 2016, Remote. Sens..

[49]  Xiang Zhang,et al.  Surface soil moisture estimation at high spatial resolution by fusing synthetic aperture radar and optical remote sensing data , 2020 .

[50]  Xi Zhang,et al.  Iterative multi-task learning for time-series modeling of solar panel PV outputs , 2018 .

[51]  Y. S. Rao,et al.  Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model , 2020, International Journal of Remote Sensing.

[52]  Mehdi Hosseini,et al.  Soil moisture estimation based on integration of optical and SAR images , 2011 .

[53]  Lorenzo Bruzzone,et al.  Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Sukumar Bandopadhyay,et al.  Prediction of Arsenic in Bedrock Derived Stream Sediments at a Gold Mine Site Under Conditions of Sparse Data , 2006 .

[55]  Brian W. Barrett,et al.  Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques , 2009, Remote. Sens..

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

[57]  Jeffrey P. Walker,et al.  Evaluation of IEM, Dubois, and Oh Radar Backscatter Models Using Airborne L-Band SAR , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[58]  Adrian K. Fung,et al.  Backscattering from a randomly rough dielectric surface , 1992, IEEE Trans. Geosci. Remote. Sens..

[59]  Lingmei Jiang,et al.  Retrieval of bare soil surface parameters from simulated data using neural networks combined with IEM , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[60]  M. Zribi,et al.  Characterisation of the Soil Structure and Microwave Backscattering Based on Numerical Three-Dimensional Surface Representation: Analysis with a Fractional Brownian Model , 2000 .

[61]  Mahmod Reza Sahebi,et al.  An inversion method based on multi-angular approaches for estimating bare soil surface parameters from RADARSAT-1 , 2009 .

[62]  Lorenzo Bruzzone,et al.  Estimating Soil Moisture With the Support Vector Regression Technique , 2011, IEEE Geoscience and Remote Sensing Letters.

[63]  Mehrez Zribi,et al.  Semiempirical Calibration of the Integral Equation Model for SAR Data in C-Band and Cross Polarization Using Radar Images and Field Measurements , 2011, IEEE Geoscience and Remote Sensing Letters.

[64]  M. Dehmollaian,et al.  Measuring the surface roughness of geological rock surfaces in SAR data using fractal geometry , 2017 .

[65]  M. Sahebi,et al.  New empirical backscattering models for estimating bare soil surface parameters , 2021 .

[66]  Jon Atli Benediktsson,et al.  Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization , 2015, IEEE Geoscience and Remote Sensing Letters.

[67]  George P. Petropoulos,et al.  Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model , 2018, Int. J. Appl. Earth Obs. Geoinformation.