C-band polarimetric indexes for maize monitoring based on a validated radiative transfer model

Blaes, Xavier; Defourny, Pierre; Wegmüller, U.; Vecchia, Andrea Della; Guerriero, L.; Ferrazzoli, P.

doi:10.1109/tgrs.2005.860969

Cited by 74 publications

(39 citation statements)

References 11 publications

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“…The validation dataset contained the four variables needed to calibrate the model, i.e., the LAI, the surface soil moisture (V m ), the SAR backscattering coefficient (σ o ) and the local incidence angle (θ). The model reproduces fairly well the VV temporal trend with an RMSE of 1.2 dB [30]. A good correspondence was observed also for the cross-polarization, although only one date was simulated, because a single SAR image was acquired in cross-polarization.…”

Section: Datasupporting

confidence: 56%

“…Then, the extinction cross-sections and bistatic scattering cross-sections of the single elements are computed. Finally, single element contributions are combined to simulate the overall backscattering coefficient [30].…”

Section: Datamentioning

confidence: 99%

“…In this case, the UTOV model was specifically adjusted to maize using dedicated field measurements collected over Belgium [6,30]. The radar responses were simulated for a range of configurations proposed by C-band sensors as (∼5.3 GHz) ERS, ENVISAT and RADARSAT-2 over a maize growing season up to an LAI of 4 m 2 /m 2 [30]. Simulations were systematically run for each incidence angle, ranging from 20 • to 35 • with a 5 • step.…”

Section: Datamentioning

confidence: 99%

“…As soil roughness significantly impacts the backscattering coefficient only at earlier stages of plant growth, it was measured only at the beginning of the season. The bi-dimensional soil roughness was measured by photogrammetry following the method of Blaes et al [31] and was estimated by its standard deviation and correlation length values, respectively 0.98 and 2.27 cm [30]. Each polarization has a specific response to canopy cover and water soil moisture (Figure 1).…”

Section: Datamentioning

confidence: 99%

“…As far as the HH polarization was concerned, the model was in good agreement with the data until an LAI of 4 m 2 /m 2 . For a more detailed description of the validation of the UTOV data, the readers are referred to Blaes et al [30].…”

Section: Datamentioning

confidence: 99%

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Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

et al. 2015

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Abstract:In order to monitor crop growth along the season with synthetic aperture radar (SAR) images, radiative transfer models were developed to retrieve key biophysical parameters, such as the Leaf Area Index (LAI). The semi-empirical water cloud model (WCM) can be used to estimate LAI values from SAR data and surface soil moisture information. Nevertheless, instability problems can occur during the model calibration, which subsequently reduce its transferability in both time and space. To avoid these ill-posed cases, three calibration methodologies are benchmarked in the present study. The accuracy of the retrieved LAI values for each methodology was analyzed, as well as the sensitivity of the signal to LAI for different soil moisture values. The sensitivity of the cross-polarization was highlighted especially for high LAI. The VV polarization was found sensitive for LAI values inferior to 2 m 2 /m 2 . Given the differential sensitivity of the C-band backscatter to maize canopies in each polarization, a Bayesian fusion of the LAI estimates in linear polarizations was developed. This fusion gives lower weights to estimates with a high uncertainty. This method systematically reduces the error and its associated variance. When considering all polarizations, the RMSE on LAI estimation decreased by 0.32 m 2 /m 2 , i.e., one fourth of the error value, as compared to the best estimation from a single polarization, and the associated uncertainty was reduced by a factor of two. Focusing on the two most sensitive polarizations to maize canopies (VV-HV), the error diminished by a third. This fusion framework shows thus a great potential to improve the accuracy and reliability of LAI retrieval of C-band quad-polarized data, as well as dual-polarized data, such as Sentinel-1.

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Section: Datasupporting

confidence: 56%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

et al. 2015

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Radar Vegetation Indices for Crop Growth Monitoring

Mandal

Bhattacharya

Rao

2021

Radar Remote Sensing for Crop Biophysical Parameter Estimation

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Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data

Ahmadian

Ullmann

Verrelst

et al. 2019

PFG

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The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R 2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R 2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R 2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other. KeywordsTerraSAR-X • Agricultural crop • Biomass • Stepwise regression • Water cloud model (WCM) • Random Forest • DEMMIN Zusammenfassung Biomassebewertung von landwirtschaftlichen Kulturen mit multitemporalen Methoden basierend auf dual-polarimetrischen TerraSAR-X-Daten. Die Studie zielt auf die Bestimmung von Biomasse dreier landwirtschaftlicher Kulturen, Winterweizen (Triticum aestivum L.), Gerste (Hordeum vulgare L.) und Raps (Brassica napus L.) mit multitemporalen dual-polarimetrischen TerraSAR-X-Daten. Der Radarrückstreuungskoeffizient Sigma Null der beiden Polarisationskanäle HH und VV wurde aus den Satellitenbildern extrahiert. Anschließend wurden Kombinationen von HH-und VV-Polarisationen berechnet (z. B. HH/VV, HH + VV, HH × VV), um Beziehungen zwischen den SAR-Daten und der frischen und der trockenen Biomasse jeder Kulturart unter Verwendung einer multiplen schrittweisen Regression zu bestimmen. Zusätzlich wurde das semi-empirische Water Cloud Model (WCM) verwendet, um die Wirkung von Pflanzenbiomasse auf Radarrückstreudaten abzuschätzen. Das Potenzial des maschinellen Lernens mit Random Forest (RF) wurde ebenfalls untersucht.

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C-band polarimetric indexes for maize monitoring based on a validated radiative transfer model

Cited by 74 publications

References 11 publications

Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

Radar Vegetation Indices for Crop Growth Monitoring

Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data

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