Assessing the Potential of Fully Polarimetric Mono- and Bistatic SAR Acquisitions in L-Band for Crop and Soil Monitoring

Bouchat, Jean; Tronquo, Emma; Orban, Anne; Verhoest, Niko; Defourny, Pierre

doi:10.1109/jstars.2022.3162911

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…The linear and linear-log regression lines, depicted in Figure 6, suggest through their coefficients of determinations, r 2 lin and r 2 lin-log , that the L-band backscattering coefficients σ 0 from BELSAR are moderately sensitive to the GAI, especially in HV, with r 2 lin = 0.61 and r 2 lin-log = 0.73, and in VV, with r 2 lin = 0.63 and r 2 lin-log = 0.70, but have no sensitivity to the surface soil moisture under the canopy, with r 2 lin = 0.01 and r 2 lin-log = 0.01 in HV, and r 2 lin = 0.02 and r 2 lin-log = 0.01 in VV. This finding is discussed in more detail by Bouchat et al [50] in their study on the potential of the BELSAR airborne SAR acquisitions for agricultural applications, where the GAI is reported as the crop biophysical variable that is the most linearly correlated with the L-band backscattering coefficients used in this work, amongst all the crop variables measured in situ during the BELSAR campaign, i.e., GAI, plant height, wet and dry biomass, and vegetation water content which are reported in Table 2.…”

Section: Sensitivity Of L-band σ 0 To Gai and V Mmentioning

confidence: 56%

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Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model

Bouchat

Tronquo²,

Orban³

et al. 2022

Remote Sensing

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The green area index (GAI) and the soil moisture under the canopy are two key variables for agricultural monitoring. The current most accurate GAI estimation methods exploit optical data and are rendered ineffective in the case of frequent cloud cover. Synthetic aperture radar (SAR) measurements could allow the remote estimation of both variables at the parcel level, on a large scale and regardless of clouds. In this study, several methods were implemented and tested for the simultaneous estimation of both variables using the water cloud model (WCM) and dual-polarized radar backscatter measurements. The methods were tested on the BELSAR-Campaign data set consisting of in-situ measurements of bio-geophysical variables of vegetation and soil in maize fields combined with multi-polarized C- and L-band SAR data from Sentinel-1 and BELSAR. Accurate GAI estimates were obtained using a random forest regressor for the inversion of a pair of WCMs calibrated using cross and vertical co-polarized SAR data in L- and C-band, with correlation coefficients of 0.79 and 0.65 and RMSEs of 0.77 m2 m−2 and 0.98 m2 m−2, respectively, between estimates and in-situ measurements. The WCM, however, proved inadequate for soil moisture monitoring in the conditions of the campaign. These promising results indicate that GAI retrieval in maize crops using only dual-polarized radar data could successfully substitute for estimates derived from optical data.

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Section: Sensitivity Of L-band σ 0 To Gai and V Mmentioning

confidence: 56%

“…These observations were described using the BBCH-scale [53]. A number of other maize biophysical variables were also collected during the BELSAR campaign [50]. They are partly reported in Table 2.…”

Section: In-situ Measurements Of Vegetation and Soil Variablesmentioning

confidence: 99%

Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model

Bouchat

Tronquo²,

Orban³

et al. 2022

Remote Sensing

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“…Bouchat et al . 11 have used the dataset to assess the potential of simultaneous mono- and bistatic SAR acquisitions for agriculture and soil moisture monitoring applications, as well as the impact of maize row structure on the SAR backscatter. Tronquo et al .…”

Section: Background and Summarymentioning

confidence: 99%

The BELSAR dataset: Mono- and bistatic full-pol L-band SAR for agriculture and hydrology

Bouchat,

Tronquo,

Orban

et al. 2024

Sci Data

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The BELSAR dataset consists of high-resolution multitemporal airborne mono- and bistatic fully-polarimetric synthetic aperture radar (SAR) data in L-band, alongside concurrent measurements of vegetation and soil biogeophysical variables measured in maize and winter wheat fields during the summer of 2018 in Belgium. Its collection was funded by the European Space Agency (ESA) to address the lack of publicly-accessible experimental datasets combining multistatic SAR and in situ measurements. As such, it offers an opportunity to advance the development of SAR remote sensing science and applications for agricultural monitoring and hydrology. This paper aims to facilitate its adoption and exploration by offering comprehensive documentation and integrating its multiple data sources into a unified, analysis-ready dataset.

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“…Crops will experience significant changes to their shape at important phenological periods. However, many farmlands are obscured by clouds, making it more likely that the optical sensor will fail to capture images during the crucial phenological period for crops [14,15]. Synthetic aperture radar (SAR) is an active microwave sensor, which cannot be affected by cloudy and foggy weather [16].…”

Section: Introductionmentioning

confidence: 99%

Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

Tian

et al. 2022

Remote Sensing

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Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.

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Assessing the Potential of Fully Polarimetric Mono- and Bistatic SAR Acquisitions in L-Band for Crop and Soil Monitoring

Cited by 10 publications

References 29 publications

Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model

Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model

The BELSAR dataset: Mono- and bistatic full-pol L-band SAR for agriculture and hydrology

Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

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