Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data

Haldar, Dipanwita; Dave, Viral A.; Misra, Arundhati; Bhattacharya, Bimal K.

doi:10.1080/10106049.2018.1516249

Cited by 18 publications

(6 citation statements)

References 15 publications

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“…The RVI was first introduced by Kim and van Zyl (2009) and is often used to monitor vegetation growth (Kim et al, 2011(Kim et al, , 2013bMandal et al, 2020a,b), map vegetation cover (Haldar et al, 2020;Mandal et al, 2020b), and monitor crop development (Kim et al, 2011;Huang et al, 2016). RVI is a (dimensionless) normalized index that ideally varies between zero (bare soil) and one (Szigarski et al, 2018).…”

Section: Radar Datamentioning

confidence: 99%

The influence of surface canopy water on the relationship between L-band backscatter and biophysical variables in agricultural monitoring

Khabbazan

Steele‐Dunne

Vermunt

et al. 2022

Remote Sensing of Environment

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The presence of surface water on the canopy affects radar backscatter. However, its influence on the relationship between radar backscatter and crop biophysical parameters has not been investigated. The aim of this study was to quantify the influence of surface canopy water (SCW) on the relationship between L-band radar backscatter and biophysical variables of interest in agricultural monitoring. In this study, we investigated the effect of SCW on the relationship between co-and cross-polarized radar backscatter, cross ratios (VH/VV and HV/HH), and radar vegetation index (RVI) and dry biomass, vegetation water content (VWC), plant height and leaf area index (LAI). In addition, the effect of SCW on estimated vegetation optical depth (VOD) and its relationship with internal VWC was investigated. The analysis was based on data collected during a field experiment in Florida, USA in 2018. A corn field was scanned with a truck-mounted, fully polarimetric, L-band radar along with continuous monitoring of SCW (dew, interception) and soil moisture every 15 min for 58 days. In addition, pre-dawn destructive sampling was conducted to measure internal vegetation water content and dry biomass. Results showed that the presence of SCW can increase the radar backscatter up to 2 dB and this effect was lower for cross ratios (CRs) and RVI. The Spearman's rank correlations between radar observables and biophysical parameters were, on average, 0.2 higher for dry vegetation compared to wet vegetation. The estimated VOD from wet vegetation was generally higher than those from dry vegetation, which led to different fitting parameter (socalled b) values in the linear fit between VOD and VWC. The results presented here underscore the importance of considering the influence of SCW on the retrieval of biophysical variables of interest in agricultural monitoring. In particular, they highlight the importance of overpass time, and the impact that daily patterns in dew and interception can have on the retrieval of biophysical variables of interest.

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Section: Radar Datamentioning

confidence: 99%

The influence of surface canopy water on the relationship between L-band backscatter and biophysical variables in agricultural monitoring

Khabbazan

Steele‐Dunne

Vermunt

et al. 2022

Remote Sensing of Environment

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“…Also the ideal and workable range of C‐band for wet biomass estimation has been proven to be around 4,000–5,000 gm −2 by Haldar et al. (2020). Thus the algorithm was first trained for the entire range and then range within 4,500 gm −2 .…”

Section: Resultsmentioning

confidence: 97%

“…Therefore, in order to ensure accurate prediction, the analysis specifically focused on the range within 4,500 gm −2 . Also the ideal and workable range of C-band for wet biomass estimation has been proven to be around 4,000-5,000 gm −2 by Haldar et al (2020). Thus the algorithm was first trained for the entire range and then range within 4,500 gm −2 .…”

Section: Wet Biomassmentioning

confidence: 99%

Pearl Millet Crop Biophysical Parameter Retrieval From Space Borne Polarimetric SAR Data Using Machine Learning

Thulasiraman,

Haldar,

Kumar

et al. 2024

Earth and Space Science

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The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm−2) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination (R2) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm−2) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm−2) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables.

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“…The RVI can be considered a measure of cross polarization due to volume scattering and it has been proposed as a method for monitoring the level of vegetation growth. High correlation exists between RVI with crop age and crop biomass [6]. It ranges between 0 and 1 with a value near 0 for a smooth bare soil surface and increases with vegetation growth [7].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Resonances of Natural Grasslands and Their Effects on Radar Vegetation Index

Soliman¹,

Hussein²,

Ammar³

2020

PIER B

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The present paper studies the characteristics of electromagnetic scattering from vegetation models constructed as random wire structures for the purpose of PolSAR imaging and ground surface cover recognition and classification. Radar vegetation index (RVI) has been developed for the purpose of vegetation growth monitoring. A new method is proposed to use the RVI as an accurate monitor for the natural grassland height taking into account the operational parameters such as the PolSAR look angle and the operating frequency. Also, the present paper addresses a problem that may lead to false indications of the RVI measured for grassland areas. It frequently occurs that some of the narrow long leaves of the grass cloud are quasi-parallel and of nearly equal lengths leading to the generation of internally resonant modes. The enhancement or diminishing of the backscattered field at such internal resonances may give false indication of the RVI, and hence, wrong information can be estimated such as the water content and the grass height. A new method is proposed to model the natural grasslands as clouds of electrically conductive random curly strips for the purpose of obtaining the backscatter coefficients and, hence, the corresponding RVI. The error in height estimation using the proposed method due to the existence of the internal resonances is numerically investigated.

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Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data

Cited by 18 publications

References 15 publications

The influence of surface canopy water on the relationship between L-band backscatter and biophysical variables in agricultural monitoring

The influence of surface canopy water on the relationship between L-band backscatter and biophysical variables in agricultural monitoring

Pearl Millet Crop Biophysical Parameter Retrieval From Space Borne Polarimetric SAR Data Using Machine Learning

Electromagnetic Resonances of Natural Grasslands and Their Effects on Radar Vegetation Index

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