scholarly journals Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2

2020 ◽  
Vol 12 (12) ◽  
pp. 1935
Author(s):  
Anna Derkacheva ◽  
Jeremie Mouginot ◽  
Romain Millan ◽  
Nathan Maier ◽  
Fabien Gillet-Chaulet
Keyword(s):  
Time Series ◽  
Greenland Ice Sheet ◽  
Landsat 8 ◽  
Post Processing ◽  
Local Regression ◽  
Ice Flow ◽  
Ice Velocity ◽  
Noisy Measurements ◽  
Different Characteristics ◽  
Sentinel 2

During the last decade, the number of available satellite observations has increased significantly, allowing for far more frequent measurements of the glacier speed. Appropriate methods of post-processing need to be developed to efficiently deal with the large volumes of data generated and relatively large intrinsic errors associated with the measurements. Here, we process and combine together measurements of ice velocity of Russell Gletscher in Greenland from three satellites—Sentinel-1, Sentinel-2, and Landsat-8, creating a multi-year velocity database with high temporal and spatial resolution. We then investigate post-processing methodologies with the aim of generating corrected, ordered, and simplified time series. We tested rolling mean and median, cubic spline regression, and linear non-parametric local regression (LOWESS) smoothing algorithms to reduce data noise, evaluated the results against ground-based GPS in one location, and compared the results between two locations with different characteristics. We found that LOWESS provides the best solution for noisy measurements that are unevenly distributed in time. Using this methodology with these sensors, we can robustly derive time series with temporal resolution of 2–3 weeks and improve the accuracy on the ice velocity to about 10 m/yr, or a factor of three compared to the initial measurements. The presented methodology could be applied to the entire Greenland ice sheet with an aim of reconstructing comprehensive sub-seasonal ice flow dynamics and mass balance.

Processing and analysis of dense ice velocity time series to reconstruct seasonal fluctuations of 3 greenlandic glaciers : Russell Gletscher, Upernavik Isstrom, Petermann Gletscher.

2020 ◽  
Author(s):  
Anna Derkacheva ◽  
Jeremie Mouginot ◽  
Romain Millan ◽  
Fabien Gillet-Chaulet
Keyword(s):  
Time Series ◽  
Seasonal Variations ◽  
Seasonal Changes ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Post Processing ◽  
Seasonal Fluctuations ◽  
Ice Flow ◽  
Ice Velocity ◽  
Surface Ice

<p>Significant seasonal changes in ice flow have been reported for outlet glaciers in Greenland. Understanding the mechanisms that control these rapid intra-annual changes in dynamics could potentially help to clarify Greenland's long-term evolution and climate change response.</p><p>In this study, we investigate seasonal changes in ice flow velocity in order to better understand the processes controlling them. We focus on 3 Greenlandic glaciers of different types: Russell which is a land-terminating glacier with speed ranging from 50 to 350 m/yr,  Upernavik Isstrøm which is a marine-terminating tidewater glacier with speeds up to 4 km/yr, and Petermann Gletscher that has a large ice shelf and with speed at the order of 1 km/yr. Since 2014, the number of spaceborne observations over the ice sheet has increased dramatically with the launch of Landsat-8, Sentinel-1 and -2, providing almost continuous monitoring of glacier dynamics.</p><p>Here, we develop an automatic processing chain to derive dense time series of surface ice flow from radar sensors, Sentinel -1a/b, and optical sensors, Landsat-7/8 and Sentinel-2, using speckle or feature tracking algorithms. We construct a post-processing analysis based on local polynomial regression to filter our multi-sensor time series and create a velocity database with high temporal resolution and reduced noise. The database allows us to reconstruct the continuous evolution of surface ice velocity with frequency intervals ranging from monthly for the entire glacial basin to weekly for the downstream parts. </p><p>Using this methodology, we obtain velocity fields for 4 years between 2015 and 2019 of the entire basins of Russell, Upernavik and Petermann glaciers. Our results clearly show the seasonal variations in flow to which these glaciers are subjected. We analyze the average seasonal fluctuations during the 4 years, as well as particular behavior in different years. These results are then compared and discussed in relation to potential external forcings such as subglacial hydrology (change in basal friction), fluctuations in the ice front or grounding line positions (change in buttressing) and the presence of sea ice or ice melange in front of the glaciers. </p><p>Finally, we conclude on the benefits of our post-processing approach for the analysis of dense ice flow time series and provide first insights on the causes of seasonal variations observed on these 3 glaciers.</p>

scholarly journals Greenland Ice Mapping Project: Ice Flow Velocity Variation at submonthly to decadal time scales

2018 ◽  
Author(s):  
Ian Joughin ◽  
Ben E. Smith ◽  
Ian Howat
Keyword(s):  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Velocity Variation ◽  
Landsat 8 ◽  
Ice Flow ◽  
Speed Up ◽  
Ice Velocity ◽  
Year 2000 ◽  
The Impact ◽  
Ice Sheet Mapping

Abstract. We describe several new ice velocity maps produced by the Greenland Ice Sheet Mapping Project (GIMP) using Landsat 8 and Copernicus Sentinel 1A/B data. We then focus on several sites where we analyse these data in conjunction with earlier data from this project, which extend back to the year 2000. At Jakobshavn Isbrae and Koge Bugt, we find good agreement when comparing results from different sensors. In a change from recent behaviour, Jakobshavn Isbrae began slowing substantially in 2017, with a mid-summer peak that was even slower than some previous winter minimums. Over the last decade, we identify two major slowdown events at Koge Bugt that coincide with short-term advances of the terminus. We also examined populations of glaciers in northwest and southwest Greenland to produce a record of speedup since 2000. Collectively these glaciers continue to speed up, but there are regional differences in the timing of periods of peak speedup. In addition, we computed trends for much of the southwest margin of the ice sheet where other work has suggested slowing ice flow in response to increased melting. Contrary to the earlier results, we find no evidence for a slowdown distributed over a wide area. Finally, although consistency of the data generally is good through time and across sensors, our analysis indicates substantial differences can arise in regions with high strain rates (e.g., shear margins) where sensor resolution can become a factor. For applications such as constraining model inversions, users should factor in the impact that the data's resolution has on their results.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Cloud Cover ◽  
Landsat 8 ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Essential Information ◽  
Land Surface Phenology ◽  
The Earth ◽  
Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

scholarly journals Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

2020 ◽  
Vol 12 (21) ◽  
pp. 3478
Author(s):  
Ofer Beeri ◽  
Yishai Netzer ◽  
Sarel Munitz ◽  
Danielle Ferman Mintz ◽  
Ran Pelta ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Crop Coefficient ◽  
Landsat 8 ◽  
Area Index ◽  
Management Zone ◽  
Growing Seasons ◽  
Crop Parameters ◽  
Better Than ◽  
Sentinel 2

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

scholarly journals Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

2019 ◽  
Vol 11 (14) ◽  
pp. 1730 ◽  
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Spectral Band ◽  
Ordinary Least Squares ◽  
Landsat 8 ◽  
Eastern Siberia ◽  
Landscape Changes ◽  
Least Squares Regression ◽  
Spectral Bandpass ◽  
Sentinel 2

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.

Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin

2019 ◽  
Vol 171 ◽  
pp. 36-50 ◽  
Author(s):  
Laura Piedelobo ◽  
David Hernández-López ◽  
Rocío Ballesteros ◽  
Amal Chakhar ◽  
Susana Del Pozo ◽  
...  
Keyword(s):  
Time Series ◽  
River Basin ◽  
Landsat 8 ◽  
Crop Classification ◽  
Sentinel 2
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