Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

Dąbrowska-Zielińska, K.; Budzynska, Maria; Tomaszewska, Monika; Malińska, Alicja; Gatkowska, Martyna; Bartold, Maciej; Malek, Iwona

doi:10.3390/rs8090756

Cited by 38 publications

(19 citation statements)

References 59 publications

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“…Although previous studies have identified relationships between S-1  and the surface soil moisture [16,17,18,19,23], this study, for the first time, to our knowledge, in the Biebrza Wetlands, demonstrates the relationships under an extreme range of SM conditions (from dry to wet) i.e. 27-90 vol.…”

Section: Discussioncontrasting

confidence: 51%

“…Santi et al [22] carried out an investigation in an agricultural area located in North-west Italy, and found that the soil moisture values retrieved from the C-band ENVISAT/ASAR simulated by a hydrological model, and the measured values in situ, were in good agreement. Dabrowska-Zielinska et al [23] conducted an investigation on soil moisture monitoring in the Biebrza Wetlands using Sentinel-1 data. There are not many studies for wetlands SM retrieval applying S-1 data, as can be seen from the literature review.…”

Section: Introductionmentioning

confidence: 99%

“…The authors are motivated to undertake this study due to the lack of operational methods for the monitoring of SM based on Sentinel-1 data in the Central European wetlands areas. The presented study is a new approach to the previous one [23] on SM modelling based on S-1 data. Due to the temporal frequency of the two S-1 satellites' (S-1A and S-1B) acquisitions, it is possible to monitor soil moisture changes every six days with high spatial resolution (10x10 m).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussioncontrasting

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dąbrowska-Zielińska

Musiał

Malińska

et al. 2018

Preprint

Self Cite

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“…Many researchers have attempted to identify wetland degradation. Current wetland degradation studies include wetland area degradation [7][8][9], wetland elements variation [10][11][12] and wetland ecosystem services [13]. The amount and pattern of wetland loss in China was analyzed from 1990 to 2010 based on land cover data [7].…”

Section: Introductionmentioning

confidence: 99%

Wetland Loss Identification and Evaluation Based on Landscape and Remote Sensing Indices in Xiong’an New Area

Jiang

Wang

et al. 2019

Remote Sensing

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Wetlands play a critical role in the environment. With the impacts of climate change and human activities, wetlands have suffered severe droughts and the area declined. For the wetland restoration and management, it is necessary to conduct a comprehensive analysis of wetland loss. In this study, the Xiong’an New Area was selected as the study area. For this site, we built a new method to identify the patterns of wetland loss integrated the landscape variation and wetland elements loss based on seven land use maps and Landsat series images from the 1980s to 2015. The calculated results revealed the following: (1) From the 1980s to 2015, wetland area decreased by 40.94 km2, with a reduction of 13.84%. The wetland loss was divided into three sub stages: the wet stage from 1980s to 2000, the reduction stage from 2000 to 2019 and the recovering stage from 2009 to 2015. The wetland area was mainly replaced by cropland and built-up land, accounting for 98.22% in the overall loss. The maximum wetland area was 369.43 km2 in the Xiong’an New Area. (2) From 1989 to 2015, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil moisture monitoring index (SMMI) showed a degradation, a slight improvement and degradation trend, respectively. The significantly degraded areas were 80.40 km2, 20.71 km2 and 80.05 km2 by the detection of the remote sensing indices, respectively. The wetland loss was mainly dominated by different elements in different periods. The water area (NDWI), soil moisture (SMMI) and vegetation (NDVI) caused the wetland loss in the three sub-periods (1980s–2000, 2000–2009 and 2009–2015). (3) According to the analysis in the landscape and elements, the wetland loss was summarized with three patterns. In the pattern 1, as water became scarce, the plants changed from aquatic to terrestrial species in sub-region G, which caused the wetland vegetation loss. In the pattern 2, due to the water area decrease in sub-regions B, C, D and E, the soil moisture decreased and then the aquatic plants grew up, which caused the wetland loss. In the pattern 3, in sub-region A, due to the reduction in water, terrestrial plants covered the region. The three patterns indicated the wetland loss process in the sub region scale. (4) The research integrated the landscape variation and element loss appears potential in the identification of the loss of wetland areas.

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“…C-band SAR (5.56-cm wavelength) is particularly suitable for separating/identifying emergent marsh vegetation based on biomass. Ramsey et al and Dabrowska-Zielinska et al both demonstrated strong statistical relationships between leaf area index (LAI) and cross-polarized C-band backscatter in emergent marsh wetlands [30,31]. With an increasing biomass of shrubs and trees, C-band signals saturate, limiting their ability to differentiate high biomass emergent marsh vegetation from forest-and shrub-dominated wetlands [32].…”

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confidence: 99%

Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays

2019

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The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and synthetic aperture radar (SAR) satellite imagery, we evaluated several approaches for mapping and characterization of wetlands of the Chesapeake and Delaware Bays. Sentinel-1A, Phased Array type L-band Synthetic Aperture Radar (PALSAR), PALSAR-2, Sentinel-2A, and Landsat 8 imagery were used to map wetlands, with an emphasis on mapping tidal marshes, inundation extents, and functional vegetation classes (persistent vs. non-persistent). We performed initial characterizations at three target wetlands study sites with distinct geomorphologies, hydrologic characteristics, and vegetation communities. We used findings from these target wetlands study sites to inform the selection of timeseries satellite imagery for a regional scale random forest-based classification of wetlands in the Chesapeake and Delaware Bays. Acquisition of satellite imagery, raster manipulations, and timeseries analyses were performed using Google Earth Engine. Random forest classifications were performed using the R programming language. In our regional scale classification, estuarine emergent wetlands were mapped with a producer’s accuracy greater than 88% and a user’s accuracy greater than 83%. Within target wetland sites, functional classes of vegetation were mapped with over 90% user’s and producer’s accuracy for all classes, and greater than 95% accuracy overall. The use of multitemporal SAR and multitemporal optical imagery discussed here provides a straightforward yet powerful approach for accurately mapping tidal freshwater wetlands through identification of non-persistent vegetation, as well as for mapping estuarine emergent wetlands, with direct applications to the improved management of coastal wetlands.

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Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

Cited by 38 publications

References 59 publications

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

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

Wetland Loss Identification and Evaluation Based on Landscape and Remote Sensing Indices in Xiong’an New Area

Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays

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