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Compton J Tucker - One of the best experts on this subject based on the ideXlab platform.
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seasonality and trends of snow cover Vegetation Index and temperature in northern eurasia
Geophysical Research Letters, 2003Co-Authors: Dennis G Dye, Compton J TuckerAbstract:[1] We examine seasonal variability in snow-cover, normalized difference Vegetation Index (NDVI), and temperature in a broad region of northern Eurasia, and the spatial and temporal correspondence among trends in these variables between 1982 and 1999. Our results support the contention that the previously reported springtime “greening” trend in northern Eurasian land areas arises from a combination of: (1) the direct effects of declining snow-cover on surface spectral reflectance and NDVI, and (2) enhanced Vegetation growth and green biomass stimulated by warmer air temperatures and potentially greater Vegetation absorption of photosynthetically active radiation (PAR) during the period of annual peak solar irradiance.
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higher northern latitude normalized difference Vegetation Index and growing season trends from 1982 to 1999
International Journal of Biometeorology, 2001Co-Authors: Compton J Tucker, Daniel Slayback, Jorge E Pinzon, S O Los, Ranga B Myneni, Malinda G TaylorAbstract:Normalized difference Vegetation Index data from the polar-orbiting National Oceanic and Atmospheric Administration meteorological satellites from 1982 to 1999 show significant variations in photosynthetic activity and growing season length at latitudes above 35 degrees N. Two distinct periods of increasing plant growth are apparent: 1982-1991 and 1992-1999, separated by a reduction from 1991 to 1992 associated with global cooling resulting from the volcanic eruption of Mt. Pinatubo in June 1991. The average May to September normalized difference Vegetation Index from 45 degrees N to 75 degrees N increased by 9% from 1982 to 1991, decreased by 5% from 1991 to 1992, and increased by 8% from 1992 to 1999. Variations in the normalized difference Vegetation Index were associated with variations in the start of the growing season of -5.6, +3.9, and -1.7 days respectively, for the three time periods. Our results support surface temperature increases within the same period at higher northern latitudes where temperature limits plant growth.
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interannual variations in satellite sensed Vegetation Index data from 1981 to 1991
Journal of Geophysical Research, 1998Co-Authors: Ranga B Myneni, Compton J Tucker, G Asrar, Charles D KeelingAbstract:Normalized difference Vegetation Index (NDVI) data processed from measurements of advanced very high resolution radiometers (AVHRR) onboard the afternoon-viewing NOAA series satellites (NOAA 7, 9, and 11) were analyzed for spatial and temporal patterns comparable to those observed in atmospheric CO2, near-surface air temperature, and sea surface temperature (SST) data during the 1981–1991 time period. Two global data sets of NDVI were analyzed for consistency: (1) the land segment of the joint NOAA/NASA Earth Observing System AVHRR Pathfinder data set and (2) the Global Inventory Monitoring and Modeling Studies AVHRR NDVI data set. The impact of SST events was found to be confined mostly to the tropical latitudes but was generally dominant enough to be manifest in the global NDVI anomaly. The Vegetation Index anomalies at latitudes north of 45°N were found to exhibit an increasing trend. This linear trend corresponds to a 10% increase in seasonal NDVI amplitude over a 9 year period (1981–1990). During the same time period, annual amplitude in the record of atmosphere CO2 measured at Point Barrow, Alaska, was reported to have increased by about 14%. The increase in Vegetation Index data between years was especially consistent through the spring and early summer time periods. When this increase was translated into an advance in the timing of spring green-up, the measure (8±3 days) was similar to the recently published estimate of about 7 days in the advance of the midpoint of CO2 drawdown between spring and summer at Point Barrow, Alaska. The geographical distribution of the increase in Vegetation activity was consistent with the reported patterns in springtime warming and decline of snow cover extent over the northern hemisphere land area.
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satellite based identification of linked Vegetation Index and sea surface temperature anomaly areas from 1982 1990 for africa australia and south america
Geophysical Research Letters, 1996Co-Authors: Ranga B Myneni, S O Los, Compton J TuckerAbstract:Meteorological satellite data from 1982 to 1990 were used to identify areas of significant association between tropical Pacific sea surface temperature (SST) and remotely sensed normalized difference Vegetation Index (NDVI) ano- malies, here taken as a surrogate for rainfall anomalies. Dur- ing this period, large areas of arid and semi-arid Africa, Australia and South America experienced NDVI anomalies directly correlated to tropical Pacific SST anomalies. The results are limited by the relatively short time period of analysis. However, they confirm the disruptive effects of large-scale tropical Pacific SST variations on arid and semi- arid continental rainfall patterns in Africa, Australia, and South America, as reported previously.
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investigation of soil influences in avhrr red and near infrared Vegetation Index imagery
International Journal of Remote Sensing, 1991Co-Authors: Alfredo Huete, Compton J TuckerAbstract:Abstract The effects of soil optical properties on Vegetation Index imagery are analysed with ground-based spectral measurements and both simulated and actual AVHRR data from the NOAA satellites. Soil effects on Vegetation indices were divided into primary variations associated with the brightness of bare soils, secondary variations attributed to 'colour’ differences among bare soils, and soil-Vegetation spectral mixing. Primary variations were attributed to shifts in the soil line owing to atmosphere or soil composition. Secondary soil variance was responsible for the Saharan desert 'artefact’ areas of increased Vegetation Index response in AVHRR imagery. The impact of soil effects is discussed with a transect of Vegetation Index data derived from NOAA data from desert to equatorial forest.
Alfredo Huete - One of the best experts on this subject based on the ideXlab platform.
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development of a two band enhanced Vegetation Index without a blue band
Remote Sensing of Environment, 2008Co-Authors: Zhangyan Jiang, Alfredo Huete, Kamel Didan, Tomoaki MiuraAbstract:Abstract The enhanced Vegetation Index (EVI) was developed as a standard satellite Vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a blue band, in addition to the red and near-infrared bands, making it difficult to generate long-term EVI time series as the normalized difference Vegetation Index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-band EVI (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor β is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted Vegetation Index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, β and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of Vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within ± 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different Vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied.
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analysis of ndvi and scaled difference Vegetation Index retrievals of Vegetation fraction
Remote Sensing of Environment, 2006Co-Authors: Zhangyan Jiang, Jin Chen, Alfredo Huete, Guangjian Yan, Yunhao Chen, Xiaoyu ZhangAbstract:Abstract The normalized difference Vegetation Index (NDVI) is the most widely used Vegetation Index for retrieval of Vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional Vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional Vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer Vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference Vegetation Index (SDVI), a scale-invariant Index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of Vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed.
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modis Vegetation Index compositing approach a prototype with avhrr data
Remote Sensing of Environment, 1999Co-Authors: Wim Van Leeuwen, Alfredo Huete, Trevor LaingAbstract:Abstract In this study, the 16-day MODIS (MODerate resolution Imaging Spectroradiometer) Vegetation Index (VI) compositing algorithm and product were described, evaluated, and compared with the current AVHRR (Advanced Very High Resolution Spectroradiometer) maximum value composite (MVC) approach. The MVC method selects the highest NDVI (normalized difference Vegetation Index) over a certain time interval. The MODIS VI compositing algorithm emphasizes a global and operational view angle standardization approach: a reflectance-based BRDF (Bidirectional Reflectance Distribution Function) model, succeeded by a back-up MVC algorithm that includes a view angle constraint. A year's worth of daily global AVHRR data was used to prototype the MODIS Vegetation Index compositing algorithm. The composite scenarios were evaluated with respect to: 1) temporal evolution of the VI for different continents and Vegetation types, 2) spatial continuity of the VI, 3) quality flags related to data integrity, cloud cover, and composite method, and 4) view angle distribution of the composited data. On a continental scale, the composited NDVI values from the MODIS algorithm were as much as 30% lower than the mostly, off-nadir NDVI results based on the MVC criterion. The temporal evolution of the NDVI values derived with the MODIS algorithm were similar to the NDVI values derived from the MVC algorithm. A simple BRDF model was adequate to produce nadir equivalent reflectance values from which the NDVI could be computed. Application of the BRDF and “back-up” components in the MODIS algorithm were dependent on geographic location and season, for example, the BRDF interpolation was most frequently applied in arid and semiarid regions, and during the dry season over humid climate Vegetation types. Examples of a MODIS-like global NDVI map and associated quality flags were displayed using a pseudo color bit mapping scheme.
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a modified soil adjusted Vegetation Index
Remote Sensing of Environment, 1994Co-Authors: A Chehbouni, Alfredo Huete, Yann Kerr, Soroosh SorooshianAbstract:There is currently a great deal of interest in the quantitative characterization of temporal and spatial Vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve Vegetation sensitivity by accounting for atmosphere and soil effects. The soil-adjusted Vegetation Index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L into the denominator of the normalized difference Vegetation Index (NDVI) equation. For optimal adjustment of the soil effect, however, the L factor should vary inversely with the amount of Vegetation present. A modified SAVI (MSAVI) that replaces the constant L in the SAVI equation with a variable L function is presented in this article. The L function may be derived by induction or by using the product of the NDVI and weighted difference Vegetation Index (WDVI). Results based on ground and aircraft-measured cotton canopies are presented. The MSAVI is shown to increase the dynamic range of the Vegetation signal while further minimizing the soil background influences, resulting in greater Vegetation sensitivity as defined by a “Vegetation signal” to “soil noise” ratio.
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investigation of soil influences in avhrr red and near infrared Vegetation Index imagery
International Journal of Remote Sensing, 1991Co-Authors: Alfredo Huete, Compton J TuckerAbstract:Abstract The effects of soil optical properties on Vegetation Index imagery are analysed with ground-based spectral measurements and both simulated and actual AVHRR data from the NOAA satellites. Soil effects on Vegetation indices were divided into primary variations associated with the brightness of bare soils, secondary variations attributed to 'colour’ differences among bare soils, and soil-Vegetation spectral mixing. Primary variations were attributed to shifts in the soil line owing to atmosphere or soil composition. Secondary soil variance was responsible for the Saharan desert 'artefact’ areas of increased Vegetation Index response in AVHRR imagery. The impact of soil effects is discussed with a transect of Vegetation Index data derived from NOAA data from desert to equatorial forest.
Vineet Kumar - One of the best experts on this subject based on the ideXlab platform.
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Vegetation monitoring using a new dual pol radar Vegetation Index a preliminary study with simulated nasa isro sar nisar l band data
International Geoscience and Remote Sensing Symposium, 2020Co-Authors: Dipankar Mandai, Debanshu Ratha, Vineet Kumar, Heather Mcnairn, Avik Bhattacharya, Subhadip Dey, Narayanarao Bhogapurapu, Juan M Lopersanchez, Y S RaoAbstract:In this study, we propose a new Vegetation Index (DpRVI) for dual polarimetric synthetic aperture radar (SAR) data. The evaluation of this new Index is performed with a particular attention towards the preparation of the NASA-ISRO SAR (NISAR) L-band system science objective. The proposed Vegetation Index is derived for two dual-pol (HH-HV and VV-VH) modes obtained through a simulation from L-band full-pol UAVSAR data. Time-series simulated NISAR data are obtained from the UAVSAR data acquired during the SMAPVEX12 campaign over the CAL/VAL test site in Winnipeg (Canada), to assess the proposed Vegetation Index. The temporal trend of DpRVI follows the growth stages of canola with a promising correlation of DpRVI with several biophysical variables. Correlation analysis indicates that DpRVI derived for VV-VH mode correlates better with the canola biophysical parameters than the HH-HV mode.
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dual polarimetric radar Vegetation Index for crop growth monitoring using sentinel 1 sar data
Remote Sensing of Environment, 2020Co-Authors: Dipankar Mandal, Debanshu Ratha, Vineet Kumar, Heather Mcnairn, Avik Bhattacharya, Subhadip Dey, Juan M Lopezsanchez, Y S RaoAbstract:Abstract Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of Vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new Vegetation Index from dual-pol (DpRVI) SAR data. We assess the utility of this Index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four Vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.
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a generalized volume scattering model based Vegetation Index from polarimetric sar data
IEEE Geoscience and Remote Sensing Letters, 2019Co-Authors: Debanshu Ratha, Dipankar Mandal, Vineet Kumar, Heather Mcnairn, Avik Bhattacharya, Alejandro C FreryAbstract:In this letter, we propose a novel Vegetation Index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on a unit sphere proposed by Ratha et al. is used in this letter. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and generalized volume scattering models. A factor is estimated corresponding to the ratio of the minimum to the maximum geodesic distances between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. This factor is then scaled and multiplied with the similarity measure to obtain the novel Vegetation Index. The proposed Vegetation Index is compared with the radar Vegetation Index (RVI) proposed by Kim and van Zyl. A time series of RADARSAT-2 data acquired during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, is used to assessing the proposed RVI.
Dipankar Mandal - One of the best experts on this subject based on the ideXlab platform.
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dual polarimetric radar Vegetation Index for crop growth monitoring using sentinel 1 sar data
Remote Sensing of Environment, 2020Co-Authors: Dipankar Mandal, Debanshu Ratha, Vineet Kumar, Heather Mcnairn, Avik Bhattacharya, Subhadip Dey, Juan M Lopezsanchez, Y S RaoAbstract:Abstract Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of Vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new Vegetation Index from dual-pol (DpRVI) SAR data. We assess the utility of this Index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four Vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.
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a generalized volume scattering model based Vegetation Index from polarimetric sar data
IEEE Geoscience and Remote Sensing Letters, 2019Co-Authors: Debanshu Ratha, Dipankar Mandal, Vineet Kumar, Heather Mcnairn, Avik Bhattacharya, Alejandro C FreryAbstract:In this letter, we propose a novel Vegetation Index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on a unit sphere proposed by Ratha et al. is used in this letter. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and generalized volume scattering models. A factor is estimated corresponding to the ratio of the minimum to the maximum geodesic distances between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. This factor is then scaled and multiplied with the similarity measure to obtain the novel Vegetation Index. The proposed Vegetation Index is compared with the radar Vegetation Index (RVI) proposed by Kim and van Zyl. A time series of RADARSAT-2 data acquired during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, is used to assessing the proposed RVI.
Y S Rao - One of the best experts on this subject based on the ideXlab platform.
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Vegetation monitoring using a new dual pol radar Vegetation Index a preliminary study with simulated nasa isro sar nisar l band data
International Geoscience and Remote Sensing Symposium, 2020Co-Authors: Dipankar Mandai, Debanshu Ratha, Vineet Kumar, Heather Mcnairn, Avik Bhattacharya, Subhadip Dey, Narayanarao Bhogapurapu, Juan M Lopersanchez, Y S RaoAbstract:In this study, we propose a new Vegetation Index (DpRVI) for dual polarimetric synthetic aperture radar (SAR) data. The evaluation of this new Index is performed with a particular attention towards the preparation of the NASA-ISRO SAR (NISAR) L-band system science objective. The proposed Vegetation Index is derived for two dual-pol (HH-HV and VV-VH) modes obtained through a simulation from L-band full-pol UAVSAR data. Time-series simulated NISAR data are obtained from the UAVSAR data acquired during the SMAPVEX12 campaign over the CAL/VAL test site in Winnipeg (Canada), to assess the proposed Vegetation Index. The temporal trend of DpRVI follows the growth stages of canola with a promising correlation of DpRVI with several biophysical variables. Correlation analysis indicates that DpRVI derived for VV-VH mode correlates better with the canola biophysical parameters than the HH-HV mode.
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dual polarimetric radar Vegetation Index for crop growth monitoring using sentinel 1 sar data
Remote Sensing of Environment, 2020Co-Authors: Dipankar Mandal, Debanshu Ratha, Vineet Kumar, Heather Mcnairn, Avik Bhattacharya, Subhadip Dey, Juan M Lopezsanchez, Y S RaoAbstract:Abstract Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of Vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new Vegetation Index from dual-pol (DpRVI) SAR data. We assess the utility of this Index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four Vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.
