Variations of Microwave Scattering Properties by Seasonal Freeze/Thaw Transition in the Permafrost Active Layer Observed by ALOS PALSAR Polarimetric Data

Park, Sang-Eun

doi:10.3390/rs71215874

Cited by 28 publications

(24 citation statements)

References 41 publications

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“…Scatterometer sensors operating at C-band show distinct differences in backscatter for frozen and thawed ground [13]. During transitional periods in spring and autumn a sudden change in backscatter values can be observed [13,14]. This phenomenon is caused by the sensitivity of the microwave signal to the change of the dielectric constant of the ground that is in turn caused by the state change of the water contained in the ground [15,16].…”

Section: Introductionmentioning

confidence: 99%

Dependence of C-Band Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic Permafrost Regions

et al. 2018

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Microwave remote sensing has found numerous applications in areas affected by permafrost and seasonally frozen ground. In this study, we focused on data obtained by the Advanced Scatterometer (ASCAT, C-band) during winter periods when the ground is assumed to be frozen. This paper discusses the relationships of ASCAT backscatter with snow depth, air and ground temperature through correlations and the analysis of covariance (ANCOVA) to quantify influences on backscatter values during situations of frozen ground. We studied sites in Alaska, Northern Canada, Scandinavia and Siberia. Air temperature and snow depth data were obtained from 19 World Meteorological Organization (WMO) and 4 Snow Telemetry (SNOTEL) stations. Ground temperature data were obtained from 36 boreholes through the Global Terrestrial Network for Permafrost Database (GTN-P) and additional records from central Yamal. Results suggest distinct differences between sites with and without underlying continuous permafrost. Sites characterized by high freezing indices (>4000 degree-days) have consistently stronger median correlations of ASCAT backscatter with ground temperature for all measurement depths. We show that the dynamics in winter-time backscatter cannot be solely explained through snow processes, but are also highly correlated with ground temperature up to a considerable depth (60 cm). These findings have important implications for both freeze/thaw and snow water equivalent retrieval algorithms based on C-band radar measurements.

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Section: Introductionmentioning

confidence: 99%

Dependence of C-Band Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic Permafrost Regions

et al. 2018

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“…Mironov and Muzalevsky (2013) used PALSAR data over the Arctic tundra and found VV polarization as the most sensitive to F/T dynamics but we did not have that VV polarisation to evaluate it for our study area. In addition, PALSAR backscattering in HV polarization measured the most significant seasonal variations between winter and spring by about 7-8 dB over Eastern Siberia (Park 2015). The HV polarisation shows the best classification results.…”

Section: Frozen and Thaw Reference Thresholdsmentioning

confidence: 94%

Landscape Freeze/Thaw Mapping from Active and Passive Microwave Earth Observations over the Tursujuq National Park, Quebec, Canada

et al. 2021

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We investigated the sensitivity to vegetation cover type of active (PALSAR) and passive (SMAP) freeze/thaw (F/T) classification. We also used F/T classification from high-resolution PALSAR data (30 m) to follow the evolution of frozen and thawed soil states obtained from an adaptive algorithm with low-resolution SMAP data (36 km). We used PALSAR and SMAP scenes acquired from June 2015 to January 2017 over the Tursujuq National Park (Umiujaq, Quebec, Canada). A new F/T algorithm with a specific reference threshold under each vegetation type (shrub, grass, lichen, wetland, and bare land) is proposed to classify PALSAR pixels. The validation of the PALSAR F/T classification with soil temperature at ~5 cm depth revealed a greater overall accuracy (> 80%), with horizontal transmitted and vertical received (HV) thresholds. The PALSAR F/T classification shows that a SMAP pixel is classified as frozen when more than 50% of its area is frozen at the surface. We confirmed the sensitivity to vegetation cover type of passive and active F/T classification with L-band sensor. RÉSUMÉ Nous avons examiné la sensibilité au couvert végétal de la classification gel/dégel (G/D) active (PALSAR) et passive (SMAP). Nous avons aussi utilisé une classification G/D à partir de données à haute résolution (30 m) PALSAR pour suivre l'évolution des états gelé et dégelé des sols provenant d'un algorithme adapté avec des données à faible résolution (36 km) SMAP. Nous avons utilisé des scènes SMAP et PALSAR acquises au-dessus du Parc national Tursujuq (Umiujaq, Quebec, Canada) entre juin 2015 et janvier 2017. Un nouvel algorithme G/D avec des seuils de référence spécifiques à chaque type de végétation (arbustes, herbacées, lichens, milieu humide, et terre nue) est proposé pour classifier les pixels PALSAR. La validation de la classification G/D PALSAR avec les données de température du sol à ~5 cm de la surface a révélé une meilleure précision (> 80%) avec les seuils en polarisation de transmission horizontale et de réception verticale (HV). La classification G/D PALSAR montre qu'un pixel SMAP est classifié comme gelé lorsque plus de 50% de sa surface est gelée. Nous avons confirmé la sensibilité au couvert végétal des classifications G/D passive et active en bande L.

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“…This leads to a lack of generalisation capability and a need to use extensive and representative reference data and SAR data. The latter means the need to account for not only all variation of SAR signatures for a specific class but also the need to consider seasonal effects, as changes in moisture of soil and vegetation, as well as frozen state of land [32] that strongly affect SAR backscatter. On the other hand, when using multitemporal approaches, such seasonal variation can be used as an effective discriminator among different land cover classes.…”

Section: A Land Cover Mapping With Sar Imagerymentioning

confidence: 99%

Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models

Šćepanović¹,

Antropov²,

Laurila³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Land cover (LC) mapping is essential for monitoring the environment and understanding the effects of human activities on it. Recent studies demonstrated successful applications of specific deep learning models to small-scale LC mapping tasks (e.g., wetland mapping). However, it is not readily clear which of the existing state-of-the-art models for natural images are the best candidates to be taken for the particular remote sensing task and data. In this study, we answer that question for mapping the fundamental LC classes using the satellite imaging radar data. We took ESA Sentinel-1 C-band SAR images acquired during the whole summer season of 2018 in Finland, which are representative of the land cover in the country. CORINE LC map was used as a reference, and the models were trained to distinguish between the 5 major CORINE based classes. We selected seven among the state-of-the-art semantic segmentation models so that they cover a diverse set of approaches: U-Net, DeepLabV3+, PSPNet, BiSeNet, SegNet, FC-DenseNet, and FRRN-B, and further fine-tuned them. Upon evaluation and benchmarking, all the models demonstrated solid performance with overall accuracy between 87.9% and 93.1%, with good to a very good agreement (kappa statistic between 0.75 and 0.86). The two best models were FC-DenseNet (Fully Convolutional DenseNets) and SegNet (Encoder-Decoder-Skip), with the latter having a much shorter inference time. Overall, our results indicate that the semantic segmentation models are suitable for efficient wide-area mapping using satellite SAR imagery and provide baseline accuracy against which the newly proposed models should be evaluated.

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Variations of Microwave Scattering Properties by Seasonal Freeze/Thaw Transition in the Permafrost Active Layer Observed by ALOS PALSAR Polarimetric Data

Cited by 28 publications

References 41 publications

Dependence of C-Band Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic Permafrost Regions

Dependence of C-Band Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic Permafrost Regions

Landscape Freeze/Thaw Mapping from Active and Passive Microwave Earth Observations over the Tursujuq National Park, Quebec, Canada

Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models

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