Improved parameterization of water cloud model for hybrid-polarized backscatter simulation using interaction factor

Sugandh Chauhan, Hari Shanker Srivastava, Parul Patel

Research output: Contribution to journalConference articleAcademicpeer-review

3 Citations (Scopus)
12 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article numberhttps://doi.org/10.5194/isprs-archives-XLII-4-W2-61-2017
Pages (from-to)61
Number of pages66
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
VolumeXLII-4
Issue numberW2
Publication statusPublished - 22 Jun 2017
Externally publishedYes

Fingerprint

cloud water
leaf area index
backscatter
parameterization
simulation
synthetic aperture radar
wheat
crop
vegetation
synchrony
fieldwork
water content
water
modeling
soil

Keywords

  • Water cloud model, Vegetation descriptors, Hybrid-polarized backscatter, Interaction factor, Leaf area index

Cite this

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title = "Improved parameterization of water cloud model for hybrid-polarized backscatter simulation using interaction factor",
abstract = "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.",
keywords = "Water cloud model, Vegetation descriptors, Hybrid-polarized backscatter, Interaction factor, Leaf area index",
author = "Sugandh Chauhan and Srivastava, {Hari Shanker} and Parul Patel",
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Improved parameterization of water cloud model for hybrid-polarized backscatter simulation using interaction factor. / Chauhan, Sugandh ; Srivastava, Hari Shanker ; Patel, Parul.

In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-4, No. W2, https://doi.org/10.5194/isprs-archives-XLII-4-W2-61-2017, 22.06.2017, p. 61.

Research output: Contribution to journalConference articleAcademicpeer-review

TY - JOUR

T1 - Improved parameterization of water cloud model for hybrid-polarized backscatter simulation using interaction factor

AU - Chauhan, Sugandh

AU - Srivastava, Hari Shanker

AU - Patel, Parul

PY - 2017/6/22

Y1 - 2017/6/22

N2 - 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.

AB - 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.

KW - Water cloud model, Vegetation descriptors, Hybrid-polarized backscatter, Interaction factor, Leaf area index

M3 - Conference article

VL - XLII-4

SP - 61

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 1682-1750

IS - W2

M1 - https://doi.org/10.5194/isprs-archives-XLII-4-W2-61-2017

ER -