Retrieving freeze/thaw-cycles using Sentinel 1 data in Eastern Nunavik (Québec, Canada)

2021 ◽  
Author(s):  
Yueli Chen ◽  
Lingxiao Wang ◽  
Monique Bernier ◽  
Ralf Ludwig
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Retrieval Algorithm ◽  
State Transitions ◽  
Freeze Thaw ◽  
High Quality Information ◽  
Different Depths ◽  
Spatio Temporal

<p>In the terrestrial cryosphere, freeze/thaw (FT) state transitions play an important and measurable role for climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. Satellite active and passive microwave remote sensing has shown its principal capacity to provide effective monitoring of landscape FT dynamics. Sentinel-1 continues to deliver high-resolution microwave remote sensing than ever before and has therefore a large potential of usage for monitoring. In light of this, the capability and responses of its radar backscatter to landscape FT processes in different surface soil depths should be examined to provide a thorough grounding for a robust application of the F/T retrieval algorithm.</p><p>This study presents a seasonal threshold approach, which examines the time series progression of remote sensing measurements relative to signatures acquired during seasonal reference frozen and thawed states. It is developed to estimate the FT-state from the Sentinel 1 database and applied and evaluated for the region of Eastern Nunavik (Québec, Canada). In this course, the FT state transitions derived from Sentinel 1 data are compared to temporally overlapping situ measurements of soil moisture from different depths within the top 20cm soil. This work allows to explore differences in the sensitivity of the Sentinel 1 at different surface soil depths in higher detail; this information is used to examine the penetration performance of the Sentinel 1 under different conditions of permafrost and permafrost-dominated landscapes.</p><p>This work is dedicated to providing more accurate data to capture the spatio-temporal heterogeneity of freeze/thaw transitions. As Sentinel-1 continues to deliver high-quality information, the provided threshold algorithm delivers an extended time series to analyze FT-states and improve our understanding of related processes in permafrost landscapes.</p>

scholarly journals Estimating annual water storage variations in medium-scale (2000–10 000 km<sup>2</sup>) basins using microwave-based soil moisture retrievals

2017 ◽  
Vol 21 (3) ◽  
pp. 1849-1862 ◽  
Author(s):  
Wade T. Crow ◽  
Eunjin Han ◽  
Dongryeol Ryu ◽  
Christopher R. Hain ◽  
Martha C. Anderson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Budget ◽  
Surface Soil ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
The United States ◽  
Surface Soil Moisture ◽  
Medium Scale ◽  
Annual Water

Abstract. Due to their shallow vertical support, remotely sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (dS ∕ dt). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying dS ∕ dt via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2000–10 000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain statistically significant information concerning dS ∕ dt. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing products to enhance the spatial resolution of dS ∕ dt estimates acquired from gravity remote sensing.

scholarly journals An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

2021 ◽  
Vol 13 (1) ◽  
pp. 1-31
Author(s):  
Yongzhe Chen ◽  
Xiaoming Feng ◽  
Bojie Fu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Soil Moisture ◽  
Data Fusion ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Satellite Monitoring ◽  
Surface Soil Moisture ◽  
Spatial Coverage

Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m3 m−3), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30∘ S–30∘ N) but mostly in winter over the mid-latitudes (30–60∘ N, 30–60∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

scholarly journals Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

2020 ◽  
Vol 12 (21) ◽  
pp. 3513
Author(s):  
Jonas Koehler ◽  
Claudia Kuenzer
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Land Surface ◽  
Input Data ◽  
Earth Observation ◽  
Series Data ◽  
Climate Projections ◽  
Auto Regressive ◽  
Spatio Temporal

Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

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