scholarly journals FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

2019 ◽  
Vol 11 (9) ◽  
pp. 1124 ◽  
Author(s):  
David Frantz
Keyword(s):  
Land Surface ◽  
Data Availability ◽  
Large Area ◽  
Scientific Software ◽  
Radiometric Correction ◽  
Land Surface Phenology ◽  
Aggregation Techniques ◽  
Wide Range ◽  
Level 2 ◽  
Sentinel 2

Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.

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 Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
Author(s):  
Hanna Huryna ◽  
Yafit Cohen ◽  
Arnon Karnieli ◽  
Natalya Panov ◽  
William P. Kustas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Landsat 8 ◽  
Large Area ◽  
Primary Methods ◽  
Thermal Images ◽  
Spatially Distributed ◽  
The Difference ◽  
Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

Satellite-derived shorelines reveal fascinating dynamics for the last three decades on Danube Delta coast

2021 ◽  
Author(s):  
Florin Tatui ◽  
Georgiana Anghelin ◽  
Sorin Constantin
Keyword(s):  
Satellite Images ◽  
High Energy ◽  
Data Availability ◽  
Danube Delta ◽  
Dune System ◽  
Temporal Scales ◽  
Wide Range ◽  
The Impact ◽  
Sentinel 2

<p>Shoreline, as the interface between the upper shoreface and the beach-dune system, is sensitive to all changes from both the underwater and sub-aerial parts of the beach at a wide range of temporal scales (seconds to decades), making it a good indicator for coastal health. While more traditional techniques of shoreline monitoring present some shortcomings (low temporal resolution for photointerpretation, reduced spatial extension for video-based techniques, high costs for DGPS in-situ data acquisition), freely available satellite images can provide information for large areas (tens/hundreds of km) at very good temporal scales (days).</p><p>We employed a shoreline detection workflow for the dynamic environment of the Danube Delta coast (Black Sea). We focused on an index-based approach using the Automated Water Extraction Index (AWEI). A fully automated procedure was deployed for data processing and the waterline was estimated at sub-pixel level with an adapted image thresholding technique. For validation purposes, 5 Sentinel-2 and 5 Landsat based results were compared with both in-situ (D)GPS measurements and manually digitized shoreline positions from very high-resolution satellite images (Pleiades – 0.5 m and Spot 7 – 1.5 m). The overall accuracy of the methodology, expressed as mean absolute error, was found to be of approximately 7.5 m for Sentinel-2 and 4.7 m for Landsat data, respectively.</p><p>More than 200 Landsat (5 and 8) and Sentinel-2 images were processed and the corresponding satellite-derived shorelines between 1990 and 2020 were analysed for the whole Romanian Danube Delta coast (130 km). This high number of shorelines allowed us the discrimination of different patterns of coastline dynamic and behaviour which could not have been possible using usual surveying techniques: the extent of accumulation areas induced by the 2005-2006 historical river floods, the impact of different high-energy storms and the subsequent beach recovery after these events, the alongshore movement of erosional processes in accordance with the dominant direction of longshore sediment transport, multi-annual differences in both erosional and accumulation trends. Moreover, a very important result of our analysis is the zonation of Danube Delta coast based on multi-annual trends of shoreline dynamics at finer alongshore spatial resolution than before. This has significant implications for future studies dealing with different scenarios of Danube Delta response to projected sea level rise and increased storminess.</p><p>The presented approach and resulting products offer optimal combination of data availability, accuracy and frequency necessary to meet the monitoring and management needs of the increasing number of stakeholders involved in the coastal zone protection activities.</p>

scholarly journals Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery

2020 ◽  
Vol 240 ◽  
pp. 111685 ◽  
Author(s):  
Douglas K. Bolton ◽  
Josh M. Gray ◽  
Eli K. Melaas ◽  
Minkyu Moon ◽  
Lars Eklundh ◽  
...  
Keyword(s):  
Land Surface ◽  
Landsat 8 ◽  
Land Surface Phenology ◽  
Continental Scale ◽  
Sentinel 2

scholarly journals Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network

2021 ◽  
Vol 13 (8) ◽  
pp. 1512
Author(s):  
Quan Xiong ◽  
Liping Di ◽  
Quanlong Feng ◽  
Diyou Liu ◽  
Wei Liu ◽  
...  
Keyword(s):  
Land Surface ◽  
Near Infrared ◽  
Similarity Index ◽  
Data Availability ◽  
Optical Data ◽  
Generative Adversarial Network ◽  
Spectral Angle Mapper ◽  
Adversarial Network ◽  
Target Date ◽  
Sentinel 2

Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like images for facilitating land surface applications. In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert images from Domain A to Domain B. With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs. In order to demonstrate the superiority of our method, we also compared it with other state-of-the-art methods using the same data. To make the evaluation more objective and reliable, we calculated the root-mean-square-error (RSME), R2, Kling–Gupta efficiency (KGE), structural similarity index (SSIM), spectral angle mapper (SAM), and peak signal-to-noise ratio (PSNR) of the simulated Sentinel-2 images generated by different methods. The results show that the simulated Sentinel-2 images generated by the MCcGAN have a higher quality and accuracy than those produced via the previous methods.

scholarly journals BRDF Estimations and Normalizations of Sentinel 2 Level 2 Data Using a Kalman-Filtering Approach and Comparisons with RadCalNet Measurements

2021 ◽  
Vol 13 (17) ◽  
pp. 3373
Author(s):  
Bertrand Saulquin
Keyword(s):  
Kalman Filtering ◽  
Land Surface ◽  
Regional Climate ◽  
Atmospheric Correction ◽  
Theoretical Models ◽  
Climate Modelling ◽  
Linear Inversion ◽  
Level 2 ◽  
Filtering Approach ◽  
Sentinel 2

BRDF estimation aims to characterize the anisotropic behaviour of the observed surface, which is directly related to the type of surface. BRDF theoretical models are then used in the normalization of the satellite-derived observations to virtually replace the sensor at the nadir. Such normalization reinforces the homogeneity within and between satellite-derived time series. Nevertheless, the inversion of the necessary BRDF parameters for the normalization requires the implementation of robust methods to account for the noise in the Level 2 surface reflectances caused by the atmospheric correction process. Here, we compare normalized reflectances obtained with a Kalman filtering approach with i/the classical weighted linear inversion and ii/a normalization performed using the coefficients of the NASA-MODIS BRDF MCD43A1 band 2 product. We show, using the RadCalNet nadir-view reflectances, that the Kalman filtering approach is a more suitable approach for the Sen2Cor level 2 and the selected sites. Using the proposed approach, daily global maps of land surface BRDF coefficients and the derived normalized Sentinel 2 reflectances would be extremely useful to the global and regional climate modelling communities and for the world’s cover monitoring.

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