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

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 (V1 and V2) 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.

Download Full-text

Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data

Geocarto International ◽

10.1080/10106049.2019.1624984 ◽

2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Vijay Pratap Yadav ◽

Rajendra Prasad ◽

Ruchi Bala

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Satellite Data ◽

Cloud Model ◽

Wheat Crop ◽

Area Index ◽

Water Cloud ◽

Optical Satellite Data ◽

Index Estimation

Download Full-text

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-239-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 239-244 ◽

Cited By ~ 4

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

Abstract. 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 (&sigma;0) 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 (fveg) derived from the Landsat-8 satellite data. The estimated values of leaf area index (LAI) by modified WCM at VV polarization shows good correlation (R2=83.08% and RMSE=0.502m2/m2) with the observed values. Whereas, leaf water area index (LWAI) shows comparatively poor correspondence (R2=76% and RMSE=0.560m2/m2) 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.

Download Full-text

AN INSIGHT INTO THE VOLUME COMPONENT GENERATED FROM RISAT-1 HYBRID POLARIMETRIC SAR DATA FOR CROP BIOPHYSICAL PARAMETERS RETRIEVAL

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-209-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 209-214 ◽

Cited By ~ 1

Author(s):

H. S. Srivastava ◽

T. Sivasankar ◽

P. Patel

Keyword(s):

Cloud Model ◽

Incidence Angle ◽

Uttar Pradesh ◽

Wheat Crop ◽

Polarimetric Sar ◽

Biophysical Parameters ◽

Interaction Factor ◽

Significant Difference ◽

Water Cloud ◽

Sar Data

Abstract. Polarimetric parameters have been extensively used for target parameters retrieval than backscattering coefficients. In previous studies, volume component generated from polarimetric SAR data has been considered as the return signal component from vegetation and intern used this for biophysical parameters retrieval. Un-polarized component of the return signal has been considered as volume component. The present study is mainly focused to analyze the volume component generated from C-band RISAT-1 hybrid polarimetric SAR data from wheat crop. Three temporal datasets acquired at &sim;31&deg; central incidence angle between Jan and Mar 2016 over parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India) have been used in this study. Water Cloud Model with Gaps has been considered for modeling the first Stokes parameter (g0), which represents total intensity of return signal, from wheat crop using LAI and Interaction factor as vegetation descriptors. The vegetation component derived using calibrated Water Cloud Model with Gaps has been analyzed with volume component derived from RISAT-1 hybrid polarimetric SAR data. The analyses observed that a significant difference during lower LAI values and shown comparably during higher LAI values. The higher values of volume component derived from RISAT-1 SAR data than modeled vegetation component indicates that the volume component can also be generated by underneath soil. It is also observed the difference in derived un-polarized component and modeled vegetation component has shown higher correlation with underneath soil moisture than directly correlating with derived un-polarized component. This study indicates that the volume component derived from hybrid polarimetric SAR data has return signals from vegetation as well as underneath soil.

Download Full-text

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

Remote Sensing ◽

10.3390/rs13204023 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4023

Author(s):

Veena Shashikant ◽

Abdul Rashid Mohamed Shariff ◽

Aimrun Wayayok ◽

Md Rowshon Kamal ◽

Yang Ping Lee ◽

...

Keyword(s):

Soil Moisture ◽

Oil Palm ◽

Cloud Model ◽

Water Area ◽

Moisture Determination ◽

Backscatter Coefficient ◽

Area Index ◽

Palm Trees ◽

Water Cloud ◽

High Yields

In oil palm crop, soil fertility is less important than the physical soil characteristics. It is important to have a balance and sufficient soil moisture to sustain high yields in oil palm plantations. However, conventional methods of soil moisture determination are laborious and time-consuming with limited coverage and accuracy. In this research, we evaluated synthetic aperture radar (SAR) and in-situ observations at an oil palm plantation to determine SAR signal sensitivity to oil palm crop by means of water cloud model (WCM) inversion for retrieving soil moisture from L-band HH and HV polarized data. The effects of vegetation on backscattering coefficients were evaluated by comparing Leaf Area Index (LAI), Leaf Water Area Index (LWAI) and Normalized Plant Water Content (NPWC). The results showed that HV polarization effectively simulated backscatter coefficient as compared to HH polarization where the best fit was obtained by taking the LAI as a vegetation descriptor. The HV polarization with the LAI indicator was able to retrieve soil moisture content with an accuracy of at least 80%.

Download Full-text

Using a Modified Water Cloud Model to Retrive Leaf Area Index (LAI) from Radarsat-2 SAR Data Over an Agriculture Area

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518645 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yichuan Ma ◽

Minfeng Xing ◽

Xiliang Ni ◽

Jinfei Wang ◽

Jiali Shang ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Cloud Model ◽

Area Index ◽

Water Cloud ◽

Sar Data

Download Full-text

Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

Remote Sensing ◽

10.3390/rs10091370 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1370 ◽

Cited By ~ 15

Author(s):

Junhua Li ◽

Shusen Wang

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Cloud Model ◽

Data Availability ◽

Agricultural Crop ◽

Sar Images ◽

Area Index ◽

Water Cloud ◽

The Impact

The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.

Download Full-text

Inversion of Rice Biophysical Parameters Using Simulated Compact Polarimetric SAR C-Band Data

Sensors ◽

10.3390/s18072271 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2271 ◽

Cited By ~ 5

Author(s):

Xianyu Guo ◽

Kun Li ◽

Yun Shao ◽

Zhiyong Wang ◽

Hongyu Li ◽

...

Keyword(s):

Water Content ◽

Cloud Model ◽

Volumetric Water Content ◽

Coefficient Of Determination ◽

Accurate Estimation ◽

Biophysical Parameters ◽

Area Index ◽

Water Cloud ◽

Sar Data ◽

New Generation

Timely and accurate estimation of rice parameters plays a significant role in rice monitoring and yield forecasting for ensuring food security. Compact-polarimetric (CP) synthetic aperture radar (SAR), a good compromise between the dual- and quad-polarized SARs, is an important part of the new generation of Earth observation systems. In this paper, the ability of CP SAR data to retrieve rice biophysical parameters was explored using a modified water cloud model. The results showed that S1 was superior to other CP variables in rice height inversion with a coefficient of determination (R2) of 0.92 and a root-mean-square error (RMSE) of 5.81 cm. RL was the most suitable for inverting the volumetric water content of the rice canopy, with an R2 of 0.95 and a RMSE of 0.31 kg/m3. The m-χ decomposition produced the highest accuracies for the ear biomass: R2 was 0.89 and RMSE was 0.17 kg/m2. The highest accuracy of leaf area index (LAI) retrieval was obtained for RH (right circular transmit and horizontal linear receive) with an R2 of 0.79 and a RMSE of 0.33. This study illustrated the capability of CP SAR data with respect to retrieval of rice biophysical parameters, especially for height, volumetric water content of the rice canopy, and ear biomass, and this mode may offer the best option for rice-monitoring applications because of swath coverage.

Download Full-text