Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data

S. Chauhan, Hari Shanker Srivastava (Corresponding Author), Parul Patel

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Abstract

The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σtotal 0) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R2=0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σveg 0) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σtotal 0 (σT_RH o, σT_RV o) and σveg 0 (σV_RH o, σV_RV o) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σtotal 0 as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σveg 0. The present study is a first step towards retrieving crop parameters from hybridpolarized data and thus possesses the potential to serve as a reference for further research initiatives.
Original languageEnglish
Pages (from-to)28-43
Number of pages16
JournalRemote sensing of environment
Volume216
Early online date29 Jun 2018
DOIs
Publication statusPublished - 1 Oct 2018

Fingerprint

Water content
Crops
backscatter
synthetic aperture radar
wheat
crop
crops
Soils
leaf area index
water content
Water
cloud water
soil cover
Multilayer neural networks
India
Refining
Time series
Biomass
Moisture
date (time)

Keywords

  • Wheat
  • RISAT-1
  • Water cloud model
  • Interaction factor (IF)
  • Vegetation backscatter
  • Biophysical parameters
  • Neural networks
  • ITC-ISI-JOURNAL-ARTICLE

Cite this

@article{64ad4cb5bbd64af2ad4fe558d37bfa15,
title = "Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data",
abstract = "The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σtotal 0) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R2=0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σveg 0) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σtotal 0 (σT_RH o, σT_RV o) and σveg 0 (σV_RH o, σV_RV o) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σtotal 0 as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σveg 0. The present study is a first step towards retrieving crop parameters from hybridpolarized data and thus possesses the potential to serve as a reference for further research initiatives.",
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author = "S. Chauhan and Srivastava, {Hari Shanker} and Parul Patel",
year = "2018",
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Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data. / Chauhan, S.; Srivastava, Hari Shanker (Corresponding Author); Patel, Parul.

In: Remote sensing of environment, Vol. 216, 01.10.2018, p. 28-43.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data

AU - Chauhan, S.

AU - Srivastava, Hari Shanker

AU - Patel, Parul

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N2 - The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σtotal 0) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R2=0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σveg 0) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σtotal 0 (σT_RH o, σT_RV o) and σveg 0 (σV_RH o, σV_RV o) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σtotal 0 as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σveg 0. The present study is a first step towards retrieving crop parameters from hybridpolarized data and thus possesses the potential to serve as a reference for further research initiatives.

AB - The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σtotal 0) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R2=0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σveg 0) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σtotal 0 (σT_RH o, σT_RV o) and σveg 0 (σV_RH o, σV_RV o) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σtotal 0 as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σveg 0. The present study is a first step towards retrieving crop parameters from hybridpolarized data and thus possesses the potential to serve as a reference for further research initiatives.

KW - Wheat

KW - RISAT-1

KW - Water cloud model

KW - Interaction factor (IF)

KW - Vegetation backscatter

KW - Biophysical parameters

KW - Neural networks

KW - ITC-ISI-JOURNAL-ARTICLE

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U2 - 10.1016/j.rse.2018.06.014

DO - 10.1016/j.rse.2018.06.014

M3 - Article

VL - 216

SP - 28

EP - 43

JO - Remote sensing of environment

JF - Remote sensing of environment

SN - 0034-4257

ER -