Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands

Baghdadi, Nicolas; Hajj, Mohamad El; Zribi, Mehrez; Fayad, Ibrahim

doi:10.1109/jstars.2015.2464698

Cited by 89 publications

(40 citation statements)

References 70 publications

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“…Table 12 presents the mean RMSE errors for both of these tracks separately. Figures 15 and 16 present a comparison between the soil moisture retrieved by the Model 2 inversion according to Equation (12), and the soil moisture measured at a 5 cm depth by the Decagons GS3 sensors at the grassland and marshland sites. As can be seen in the figures, high compatibility occurred between the SM values that were modeled and measured; however, it was higher for the marshland site.…”

Section: Soil Moisture Retrieval Using the σ° Indices From Sentinel-1mentioning

confidence: 99%

Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

et al. 2018

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The objective of the study was to estimate soil moisture (SM) from Sentinel-1 (S-1) satellite images acquired over wetlands. The study was carried out during the years 2015-2017 in the Biebrza Wetlands, situated in north-eastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to SM retrieval for a broad range of vegetation and soil moisture conditions. The methodology is based on research into the effect of vegetation on backscatter (σ • ) changes under different soil moisture and Normalized Difference Vegetation Index (NDVI) values. The NDVI was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. It was found that the state of the vegetation expressed by NDVI can be described by the indices such as the difference between σ • VH and VV, or the ratio of σ • VV/VH, as calculated from the Sentinel-1 images in the logarithmic domain. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the newly developed models using Water Cloud Model (WCM) that includes the derived indices based on S-1, allowed the estimation of SM for wetlands with reasonable accuracy (10 vol. %). The developed soil moisture retrieval algorithms based on S-1 data are suited for wetland ecosystems, where soil moisture values are several times higher than in agricultural areas.

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Section: Soil Moisture Retrieval Using the σ° Indices From Sentinel-1mentioning

confidence: 99%

Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

et al. 2018

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“…Comparison between the soil moisture retrieved by the inversion of Model 2 according to Equation12 (IGiK (Institute of Geodesy and Cartography) product), and soil moisture measured at a 5 cm depth (sm) by the Decagons GS3 sensors at the marshland site.…”

mentioning

confidence: 99%

Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

Dąbrowska-Zielińska

Musiał

Malińska

et al. 2018

Preprint

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Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites’ (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

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“…They represent a perfect complement to optical remote sensing images, because of their completely unrelated imaging mechanisms and their ability to ensure all-time all-weather coverage. SAR-optical fusion is arguably a major topic in remote sensing image processing [1][2][3][4]. Unfortunately, extracting reliable information from full-resolution (single-look) SAR images is a very difficult task due to the presence of intense multiplicative speckle noise.…”

Section: Introductionmentioning

confidence: 99%

Nonlocal CNN SAR Image Despeckling

et al. 2020

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We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling.

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Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands

Cited by 89 publications

References 70 publications

Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

Nonlocal CNN SAR Image Despeckling

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