Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series

Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach (S1-omnibus) using Sentinel-1 imagery of two wetlands with different ecological characteristics; a seasonal isolated wetland in southern Spain and a coastal wetland in the south of France. We test the S1-omnibus method against a commonly-used pairwise comparison of consecutive images to demonstrate its advantages. Additionally, we compare it with a pairwise change detection method using a subset of consecutive Landsat images for the same period of time. The results show how S1-omnibus is capable of capturing in space and time changes produced by water surface dynamics, as well as by agricultural practices, whether they are sudden changes, as well as gradual. S1-omnibus is capable of detecting a wider array of short-term changes than when using consecutive pairs of Sentinel-1 images. When compared to the Landsat-based change detection method, both show an overall good agreement, although certain landscape changes are detected only by either the Landsat-based or the S1-omnibus method. The S1-omnibus method shows a great potential for an automated monitoring of short time changes and accurate delineation of areas of high variability and of slow and gradual changes

Learning Speckle Suppression in Sar Images Without Ground Truth: Application to Sentinel-1 Time-Series

International audienceThis paper proposes a method of denoising SAR images, using a deep learning method, which takes advantage of the abundance of data to learn on large stacks of images of the same scene. The approach is based on the use of convolu-tional networks, used as auto-encoders. Learning is led on a large pile of images acquired on the same area, and assumes that the images of this stack differ only by the speckle noise. Several pairs of images are chosen randomly in the stack, and the network tries to predict the slave image from the master image. In this prediction, the network can not predict the noise because of its random nature. Also the application of this network to a new image fulfills the speckle filtering function. Results are given on Sentinel 1 images. They show that this approach is qualitatively competitive with literature

Monitoring of an embankment dam in southern Spain based on Sentinel-1 Time-series InSAR

Sentinel-1A/B data were freely provided by ESA through Copernicus Programme. Data have been processed by SARPROZ (Copyright (c) 2009-2020 Daniele Perissin). The satellite orbits are from ESA Quality Control Group of Sentinel-1. Research was supported by: (a) ESA Research and Service Support for providing hardware resources employed in this work, (b) ReMoDams project ESP2017-89344-R (AEI/FEDER, UE) from Spanish Ministry of Economy, Industry and Competitiveness, PAIUJA-2019/2020 and CEACTEMA from University of Jaen (Spain), and RNM-282 research group from the Junta de Andalucia (Spain), (c) ERDF through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme within project >, and by National Funds through the FCT -Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013, (d) The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project > (Czech Republic), and (e) Slovak Grant Agency VEGA under projects No. 2/0100/20.Deformation monitoring is a common practice in most of dams to ensure their structural health and safety status. Systematic monitoring is frequently carried out by means of geotechnical sensors and geodetic techniques that, although very precise an accurate, can be time-consuming and economically costly. Remote sensing techniques are proved to be very effective in assessing deformation. Changes in the structure, shell or associated infrastructures of dams, including adjacent slopes, can be efficiently recorded by using satellite Synthetic Aperture Radar Inteferometry (InSAR) techniques, in particular, Muti-Temporal InSAR time-series analyses. This is a mature technology nowadays but not very common as a routine procedure for dam monitoring. Today, thanks to the availability of spaceborne satellites with high spatial resolution SAR images and short revisit times, this technology is a powerful cost-effective way to monitor millimeter-level displacements of the dam structure and its surroundings. What is more, the potential of the technique is increased since the Copernicus C-band SAR Sentinel-1 satellites are in orbit, due to the high revisit time of 6 days and the free data availability. ReMoDams is a Spanish research project devoted to provide the deformation monitoring of several embankments dams using advances time-series InSAR techniques. One of these dams is The Arenoso dam, located in the province of Cordova (southern Spain). This dam has been monitored using Sentinel-1 SAR data since the beginning of the mission in 2014. In this paper, we show the processing of 382 SLC SAR images both in ascending and descending tracks until March 2019. The results indicate that the main displacement of the dam in this period is in the vertical direction with a rate in the order of -1 cm/year in the central part of the dam body.ESA Research and ServiceReMoDams project (AEI/FEDER, UE) from Spanish Ministry of Economy, Industry and Competitiveness ESP2017-89344-RCEACTEMA from University of Jaen (Spain)Junta de Andalucia European Commission RNM-282ERDF through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme POCI-01-0145FEDER-006961National Funds through the FCT -Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) UID/EEA/50014/2013Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project "IT4Innovations excellence in science" (Czech Republic) LQ1602Vedecka grantova agentura MSVVaS SR a SAV (VEGA) 2/0100/20PAIUJA-2019/202

Deriving wheat crop productivity indicators using Sentinel-1 time series

High-frequency Earth observation (EO) data have been shown to be effective in identifying crops and monitoring their development. The purpose of this paper is to derive quantitative indicators of crop productivity using synthetic aperture radar (SAR). This study shows that the field-specific SAR time series can be used to characterise growth and maturation periods and to estimate the performance of cereals. Winter wheat fields on the Rothamsted Research farm in Harpenden (UK) were selected for the analysis during three cropping seasons (2017 to 2019). Average SAR backscatter from Sentinel-1 satellites was extracted for each field and temporal analysis was applied to the backscatter cross-polarisation ratio (VH/VV). The calculation of the different curve parameters during the growing period involves (i) fitting of two logistic curves to the dynamics of the SAR time series, which describe timing and intensity of growth and maturation, respectively; (ii) plotting the associated first and second derivative in order to assist the determination of key stages in the crop development; and (iii) exploring the correlation matrix for the derived indicators and their predictive power for yield. The results show that the day of the year of the maximum VH/VV value was negatively correlated with yield (r = −0.56), and the duration of “full” vegetation was positively correlated with yield (r = 0.61). Significant seasonal variation in the timing of peak vegetation (p = 0.042), the midpoint of growth (p = 0.037), the duration of the growing season (p = 0.039) and yield (p = 0.016) were observed and were consistent with observations of crop phenology. Further research is required to obtain a more detailed picture of the uncertainty of the presented novel methodology, as well as its validity across a wider range of agroecosystem

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV

Using sentinel-1 time series for monitoring deforestation in regions with high precipitation rate - Study case: Chocó-Colombia

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesDespite nowadays there are many optical sensors out there, meteorological conditions in some places on the Earth makes very difficult to have access to images without clouds. Some of those places have unique ecosystems and landscape with natural forest that should be taken care of. SAR images has proven its capabilities for monitoring deforestation since the first sensors were deployed. Sentinel-1 allows to have free access to SAR data with high temporal resolution. Therefore, this study explores the use of SAR data for monitoring deforestation in places where the precipitation rate is too high. A time-series approach is used as framework to detect forest disturbances; the work tests if performing a combination of the Sentinel-1 bands through a modified version from the RFDI gets better results than the original bands; two methods for detecting changes along the time focus on deforestation are compared. The results show that VH band is the best input with similar overall accuracy with the two methods, around 80%, the mRFDI showed acceptable results but it does not prove any improvement on the deforestation events detected. It was concluded that with a workflow optimization, it can be used to overcome the optical images problem to monitor deforestation events

Analysis of Min-Trees over Sentinel-1 Time Series for Flood Detection

International audienceMonitoring flood is an important task for disaster management. It requires to distinguish between changes related to water from the other changes. We address such an issue by relying on both spatial and intensity information. To do so, we exploit min-tree that emphasize intensity extrema in a multiscale, efficient framework. We thus suggest a two-step approach operating on satellite image time series. We first perform a temporal analysis to identify images containing possible floods. Then a spatial analysis is achieved to detect flood areas on the selected images. Both steps relies on the analysis of component attributes extracted from the min-tree representation. We conduct some experiments on a flooded scene observed through Sentinel-1 SAR imagery. The results show that flood areas can be efficiently and accurately characterized with spatial component attributes extracted from hierarchical representations from SAR time series