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:10.20944/preprints201609.0046.v1

Cited by 14 publications

(6 citation statements)

References 49 publications

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“…Paloscia et al [36] developed an approach based on an artificial neural network to estimate soil moisture in nearly-real-time for Sentinel-1 data. Dabrowska-Zielinska et al [37] demonstrated the capability of Sentinel-1 data in estimating soil moisture and net ecosystem exchange in wetlands. These results are encouraging for the application of Sentinel-1A data to the estimation of soil moisture over grasslands.…”

Section: Introductionmentioning

confidence: 99%

First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

Bai

et al. 2017

Remote Sensing

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Abstract:The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ • ) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson's correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m 3 /m 3 for the ascending data, and are 0.012, 0.026, and 0.055 m 3 /m 3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.

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Section: Introductionmentioning

confidence: 99%

First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

Bai

et al. 2017

Remote Sensing

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“…In-situ measurement of soil moisture is considered as the most accurate method for drought monitoring but available for only small scales and limited areas [6]. Analyzes carried out by other researchers shows high relevance of using the spectral data for assessing the plants condition and biomass production [4,8,18]. The well-described indicator, NDVI was used in this work [5,22].…”

Section: Discussionmentioning

confidence: 99%

“…Research on the assessment of plants' condition and yield amount using NDVI was conducted in Poland by Dąbrowska-Zielińska [8], however, using spectral satellite data with a spatial resolution of 1 km. It cannot be clearly determined which environmental parameter effects in the best way on the plants condition represented by the NDVI.…”

Section: Discussionmentioning

confidence: 99%

“…At the same time, a set of hydrological and climatic data, regarding the conditions of environment in the study area were analyzed. The procedure included two steps: 1) selection of grasslands from other types of land cover and calculation of the NDVI for grasslands, 2) comparison and description of the relationship between time series of spectral and in situ data and condition of plants represented by NDVI values [8,14,22].…”

Section: Methodsmentioning

confidence: 99%

“…Connecting the reflection of light with the biophysical characteristics of plants allow to use the spectral indicators for assessing the plants' condition, biomass production (including forecasting of yields) and susceptibility of plants organisms to the influence of stressors such as water shortages in the soil. The plants are used as the indicators of moisture conditions in the soil-plant environment in the spectral research [4,8].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of moisture conditions in the lowland areas using high resolution spectral data from the Sentinel-2 satellite and the GIS tools

2018

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Abstract.Research concerning agricultural drought issue mainly focuses on the methods based on long-term atmospheric data, temperature, precipitation and evaporation measurements. On the other hand, the scientific bibliography shows the possibilities of using spectral data for description the state of plants. The general availability and increasing resolution of the spectral and temporal data create a chance for monitoring and forecasting deficiencies of soil moisture based on spectral images. Paper presents the results of analysis of the moisture conditions in soilplant environment in the lowland catchment area using the spectral data obtained from the Sentinel-2 European Space Agency satellite for period February-November 2016. These spectral data were used for the calculation the Normalized Differential Vegetation Index (NDVI) which provided information about moisture conditions in the soil-plant environment. Then, the values of NDVI index were compared with the data obtained from the field investigations. The analyses have showed the spatial and temporal variability of moisture conditions in the soil-plant environment determined on the background of the spectral indicators and the existence of some dependences between climatic and spectral indicators characterizing soil-plant environment.

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TerraSAR-X and Wetlands: A Review

et al. 2018

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Since its launch in 2007, TerraSAR-X observations have been widely used in a broad range of scientific applications. Particularly in wetland research, TerraSAR-X's shortwave X-band synthetic aperture radar (SAR) possesses unique capabilities, such as high spatial and temporal resolution, for delineating and characterizing the inherent spatially and temporally complex and heterogeneous structure of wetland ecosystems and their dynamics. As transitional areas, wetlands comprise characteristics of both terrestrial and aquatic features, forming a large diversity of wetland types. This study reviews all published articles incorporating TerraSAR-X information into wetland research to provide a comprehensive study of how this sensor has been used with regard to polarization, and the function of the data, time-series analyses, or the assessment of specific wetland ecosystem types. What is evident throughout this literature review is the synergistic fusion of multi-frequency and multi-polarization SAR sensors, sometimes optical sensors, in almost all investigated studies to attain improved wetland classification results. Due to the short revisiting time of the TerraSAR-X sensor, it is possible to compute dense SAR time-series, allowing for a more precise observation of the seasonality in dynamic wetland areas as demonstrated in many of the reviewed studies.

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

Cited by 14 publications

References 49 publications

First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

Analysis of moisture conditions in the lowland areas using high resolution spectral data from the Sentinel-2 satellite and the GIS tools

TerraSAR-X and Wetlands: A Review

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