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

2021 ◽  
Vol 15 (01) ◽  
Author(s):  
Sadegh Ranjbar ◽  
Arastou Zarei ◽  
Mahdi Hasanlou ◽  
Mehdi Akhoondzadeh ◽  
Jalal Amini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Integral Equation ◽  
Cloud Model ◽  
Equation Model ◽  
Parameters Estimation ◽  
Soil Parameters ◽  
Water Cloud ◽  
Agricultural Areas

scholarly journals ESTIMATION OF BIOPHYSICAL PARAMETERS OF WHEAT CROP THROUGH MODIFIED WATER CLOUD MODEL USING SATELLITE DATA

2018 ◽  
Vol IV-5 ◽  
pp. 239-244 ◽  
Author(s):  
V. P. Yadav ◽  
R. Prasad ◽  
R. Bala ◽  
A. K. Vishwakarma ◽  
S. A. Yadav
Keyword(s):  
Satellite Data ◽  
Cloud Model ◽  
Parameters Estimation ◽  
Optical Data ◽  
Wheat Crop ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Area Index ◽  
Vegetation Fraction ◽  
Water Cloud

<p><strong>Abstract.</strong> A modified water cloud model (WCM) was used to estimate the biophysical parameters of wheat crop using Sentinel-1A and Landsat-8 satellite images. The approach of combining the potential of SAR and optical data provided a new technique for the estimation of biophysical parameters of wheat crop. The biophysical parameters estimation was done using non-linear least squares optimization technique by minimizing the cost function between the backscattering coefficients (&amp;sigma;<sup>0</sup>) computed from the Sentinel-1A image and simulated by the modified WCM followed by look up table algorithm(LUT). The modified WCM integrates the full account of backscattering response on vegetation and bare soil by adding vegetation fraction. The modified WCM was found more sensitive than the original WCM because of incorporation of vegetation fraction (f<sub>veg</sub>) derived from the Landsat-8 satellite data. The estimated values of leaf area index (LAI) by modified WCM at VV polarization shows good correlation (R<sup>2</sup><span class="thinspace"></span>=<span class="thinspace"></span>83.08<span class="thinspace"></span>% and RMSE<span class="thinspace"></span>=<span class="thinspace"></span>0.502<span class="thinspace"></span>m<sup>2</sup>/m<sup>2</sup>) with the observed values. Whereas, leaf water area index (LWAI) shows comparatively poor correspondence (R<sup>2</sup><span class="thinspace"></span>=<span class="thinspace"></span>76<span class="thinspace"></span>% and RMSE<span class="thinspace"></span>=<span class="thinspace"></span>0.560<span class="thinspace"></span>m<sup>2</sup>/m<sup>2</sup>) with the observed data in comparison to LAI estimation at VV polarization. The performance indices show that the modified WCM was found more accurate for the estimation of wheat crop parameters during the whole growth season in Varanasi district, India. Thus, the modified WCM shows significant potential for the accurate estimation of LAI and LWAI of wheat crop on incorporating both SAR and optical satellite data.</p>

scholarly journals IMPROVED PARAMETERIZATION OF WATER CLOUD MODEL FOR HYBRID-POLARIZED BACKSCATTER SIMULATION USING INTERACTION FACTOR

2017 ◽  
Vol XLII-4/W2 ◽  
pp. 61-66 ◽  
Author(s):  
S. Chauhan ◽  
H. S. Srivastava ◽  
P. Patel
Keyword(s):  
Cloud Model ◽  
Water Area ◽  
Wheat Crop ◽  
Soil Parameters ◽  
Growth Stages ◽  
Indian States ◽  
Area Index ◽  
Interaction Factor ◽  
Water Cloud ◽  
Semi Empirical

The prime aim of this study was to assess the potential of semi-empirical water cloud model (WCM) in simulating hybrid-polarized SAR backscatter signatures (RH and RV) retrieved from RISAT-1 data and integrate the results into a graphical user interface (GUI) to facilitate easy comprehension and interpretation. A predominant agricultural wheat growing area was selected in Mathura and Bharatpur districts located in the Indian states of Uttar Pradesh and Rajasthan respectively to carry out the study. The three-date datasets were acquired covering the crucial growth stages of the wheat crop. In synchrony, the fieldwork was organized to measure crop/soil parameters. The RH and RV backscattering coefficient images were extracted from the SAR data for all the three dates. The effect of four combinations of vegetation descriptors (<i>V<sub>1</sub></i> and <i>V<sub>2</sub></i>) viz., LAI-LAI, LAI-Plant water content (PWC), Leaf water area index (LWAI)-LWAI, and LAI-Interaction factor (IF) on the total RH and RV backscatter was analyzed. The results revealed that WCM calibrated with LAI and IF as the two vegetation descriptors simulated the total RH and RV backscatter values with highest R2 of 0.90 and 0.85 while the RMSE was lowest among the other tested models (1.18 and 1.25 dB, respectively). The theoretical considerations and interpretations have been discussed and examined in the paper. The novelty of this work emanates from the fact that it is a first step towards the modeling of hybrid-polarized backscatter data using an accurately parameterized semi-empirical approach.

scholarly journals Parameterizing the modified water cloud model to improve soil moisture data retrieval using vegetation models

2020 ◽  
Vol 69 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Kishan Singh Rawat ◽  
Sudhir Kumar Singh ◽  
Ram L. Ray ◽  
Szilárd Szabó ◽  
Sanjeev Kumar
Keyword(s):  
Cloud Model ◽  
Vegetation Indices ◽  
Data Retrieval ◽  
Soil Parameters ◽  
Landsat 8 ◽  
Vegetation Canopy ◽  
Water Cloud ◽  
Vegetation Models ◽  
Crop Coefficients ◽  
Sentinel 2

The objective was to parameterize a modified water cloud model using crop coefficients (A and B). These crop coefficients were derived from Landsat-8 and Sentinel-2 data. Whereas the coefficients C and D are of soil parameters. The water cloud model was modified using crop coefficients by minimizing the RMSE between observed VVσ0 and Sentinel-1 based simulated VVσ0. The comparison with observed and simulated VV polarized σ0 showed low RMSE (0.81 dB) and strong R2 of 0.98 for NDVI-EVI combination. However, based on other possible combinations of vegetation indices VVσ0 and simulated VVσ0 do not show a good statistical agreement. It was observed that the errors in crop coefficients (A and B) are sensitive to errors in initial vegetation/canopy descriptor parameters.

Risk assessment for health insurance using equation modeling and machine learning

2021 ◽  
Vol 25 (2) ◽  
pp. 201-225
Author(s):  
Amrik Singh ◽  
K.R. Ramkumar
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Risk Analysis ◽  
Real Life ◽  
Equation Model ◽  
Least Square ◽  
Parameter Analysis ◽  
Risk Indicators ◽  
Equation Modeling ◽  
Pairwise Correlation

Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as ‘inverse problem’ the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.

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