Dynamic Lake Ice Movement on Lake Khovsgol, Mongolia, Revealed by Time Series Displacements from Pixel Offset with Sentinel-2 Optical Images

As one of the most sensitive indicators of global climate change, seasonal ice-covered lakes are attracting gaining attention worldwide. As a large seasonal ice-covered lake located in Northern Mongolia, Lake Khovsgol not only provides important freshwater resources for the local population but also serves as a means of water transportation in summer and an important land-based activity for residents in winter. In this study, we used the sub-pixel offset technique with multi-temporal Sentinel-2 optical images to estimate the time-series displacement of lake ice in Lake Khovsgol from 7 December 2020 to 17 June 2021. With the processing of 112 Sentinel-2 images, we obtained 27 pairs of displacement results at intervals of 5, 10, and 15 days. These lake ice movement results covered three stages from ice-on to ice-off. The first stage was the lake ice growth period, which lasted 26 days from 7 December 2020 to 3 January 2021. Ice formation started from the south and extended northward, with a displacement of up to 10 m in 5 days. The second stage was the active phase of the ice cover, which took place from 3 January 2021 to 18 April 2021. Maximum displacement values reached 12 m in the east and 11 m in the north among all observations. The value of the lake ice movement in the north–south direction (NS) was found to be larger than in the east–west direction (EW). The third stage was the melting period, which closed on 17 June 2021. In comparison to the freezing date of November in past years, our results demonstrate the ice-on date of Lake Khovsgol has been delayed to December, suggesting a possible reason that the seasonal ice-covered lake located at the middle latitude has been affected by global warming. In addition, the lake ice movement of our results can reveal the regional climate characteristic. This study is one of the few cases to reveal the distribution characteristics and changing trends of lake ice on the Mongolia Plateau, providing a rare reference for lake ice research in this region.

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Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Remote Sensing ◽

10.3390/rs11070820 ◽

2019 ◽

Vol 11 (7) ◽

pp. 820 ◽

Cited By ~ 8

Author(s):

Haifeng Tian ◽

Ni Huang ◽

Zheng Niu ◽

Yuchu Qin ◽

Jie Pei ◽

...

Keyword(s):

Time Series ◽

Large Scale ◽

Time Windows ◽

Vegetation Indices ◽

Optical Images ◽

Landsat 7 ◽

Multi Temporal ◽

Winter Crop ◽

Winter Crops ◽

Sentinel 2

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

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Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine

Remote Sensing ◽

10.3390/rs12101622 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1622 ◽

Cited By ~ 3

Author(s):

Shimpei Inoue ◽

Akihiko Ito ◽

Chinatsu Yonezawa

Keyword(s):

Time Series ◽

Paddy Field ◽

Water Resource Management ◽

Mapping Method ◽

Google Earth ◽

Paddy Fields ◽

Optical Images ◽

Sar Data ◽

Decision Tree Method ◽

Sentinel 2

Paddy fields play very important environmental roles in food security, water resource management, biodiversity conservation, and climate change. Therefore, reliable broad-scale paddy field maps are essential for understanding these issues related to rice and paddy fields. Here, we propose a novel paddy field mapping method that uses Sentinel-1 synthetic aperture radar (SAR) time series that are robust for cloud cover, supplemented by Sentinel-2 optical images that are more reliable than SAR data for extracting irrigated paddy fields. Paddy fields were provisionally specified by using the Sentinel-1 SAR data and a conventional decision tree method. Then, an additional mask using water and vegetation indexes based on Sentinel-2 optical images was overlaid to remove non-paddy field areas. We used the proposed method to develop a paddy field map for Japan in 2018 with a 30 m spatial resolution. The producer’s accuracy of this map (92.4%) for non-paddy reference agricultural fields was much higher than that of a map developed by the conventional method (57.0%) using only Sentinel-1 data. Our proposed method also reproduced paddy field areas at the prefecture scale better than existing paddy field maps developed by a remote sensing approach.

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Trends and Characteristics of Hydrological Time Series in Finland

Hydrology Research ◽

10.2166/nh.2003.0029 ◽

2003 ◽

Vol 34 (1-2) ◽

pp. 71-90 ◽

Cited By ~ 42

Author(s):

Veli Hyvärinen

Keyword(s):

Time Series ◽

Water Temperature ◽

The South ◽

Lake Ice ◽

The North ◽

Long Term Trends ◽

Time Series Analyses ◽

Water Stage ◽

In The Beginning

Hydrological time series analyses made in Finland up to 2001 show the following: 1) Precipitation has been increasing in southern and central Finland, and also in the north in winter, during the period 1911-2000. There are, however, no harmonized analyses of areal precipitation to show the exact increase. 2) The annual maximum of the areal water equivalent of snow has been increasing in eastern and northern Finland but decreasing in the south and west during the period 1947-2001. 3) The winter runoff has generally been increasing strongly in southern and slightly in central Finland during the 20th century. In northern Lapland there are no signs of increase in winter or annual flow. Annual discharge in the south and west has also increased to some extent. 4) The existing analyses show no signs of long-term trends in annual evapotranspiration. 5) Long-term fluctuations of water stage have been observed in the major groundwater formations. 6) The series of the date of ice break-up in the river Tornionjoki - starting in 1693 – shows that in recent decades the ice cover of the river has broken up about two weeks earlier than in the beginning of the period. 6) Lake ice maximum thickness series show no noticeable trend. 7) Lake water temperature in south-eastern Finland seems to have been increasing slightly during the period starting in 1924; in central and northern Finland no trends in water temperature have been observed.

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Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

Remote Sensing ◽

10.3390/rs12223784 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3784

Author(s):

Kaupo Voormansik ◽

Karlis Zalite ◽

Indrek Sünter ◽

Tanel Tamm ◽

Kalev Koppel ◽

...

Keyword(s):

Time Series ◽

Management Practices ◽

Coherence Time ◽

Sar Interferometry ◽

Large Dataset ◽

Wide Area Monitoring ◽

Detection Algorithms ◽

Optical Images ◽

Area Monitoring ◽

Sentinel 2

Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications.

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Fine-Resolution Mapping of Pan-Arctic Lake Ice-Off Phenology Based on Dense Sentinel-2 Time Series Data

Remote Sensing ◽

10.3390/rs13142742 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2742

Author(s):

Chong Liu ◽

Huabing Huang ◽

Fengming Hui ◽

Ziqian Zhang ◽

Xiao Cheng

Keyword(s):

Time Series ◽

Google Earth ◽

Arctic Lakes ◽

Series Data ◽

Lake Ice ◽

Arctic Lake ◽

Resolution Mapping ◽

Break Up ◽

Fine Resolution ◽

Sentinel 2

The timing of lake ice-off regulates biotic and abiotic processes in Arctic ecosystems. Due to the coarse spatial and temporal resolution of available satellite data, previous studies mainly focused on lake-scale investigations of melting/freezing, hindering the detection of subtle patterns within heterogeneous landscapes. To fill this knowledge gap, we developed a new approach for fine-resolution mapping of Pan-Arctic lake ice-off phenology. Using the Scene Classification Layer data derived from dense Sentinel-2 time series images, we estimated the pixel-by-pixel ice break-up end date information by seeking the transition time point when the pixel is completely free of ice. Applying this approach on the Google Earth Engine platform, we mapped the spatial distribution of the break-up end date for 45,532 lakes across the entire Arctic (except for Greenland) for the year 2019. The evaluation results suggested that our estimations matched well with both in situ measurements and an existing lake ice phenology product. Based on the generated map, we estimated that the average break-up end time of Pan-Arctic lakes is 172 ± 13.4 (measured in day of year) for the year 2019. The mapped lake ice-off phenology exhibits a latitudinal gradient, with a linear slope of 1.02 days per degree from 55°N onward. We also demonstrated the importance of lake and landscape characteristics in affecting spring lake ice melting. The proposed approach offers new possibilities for monitoring the seasonal Arctic lake ice freeze–thaw cycle, benefiting the ongoing efforts of combating and adapting to climate change.

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Earthquake-induced landslides monitoring and survey by means of InSAR

10.5194/nhess-2021-208 ◽

2021 ◽

Author(s):

Tayeb Smail ◽

Mohamed Abed ◽

Ahmed Mebarki ◽

Milan Lazecky

Keyword(s):

Time Series ◽

Pilot Study ◽

Proper Time ◽

Line Of Sight ◽

Sar Images ◽

Ground Deformations ◽

Site Investigations ◽

Optical Images ◽

Interferometric Sar ◽

Sentinel 2

Abstract. This study uses interferometric SAR techniques to identify landslides and lands prone to landslides, detect fringes and changes in areas struck by earthquakes. The pilot study investigates the Mila region (Algeria) which suffered significant landslides and structural damages (earthquake: Mw 5, 2020-08-07): the study checks ground deformations and tracks earthquake-induced landslides. DInSAR analysis shows normal interferograms, with atmospheric contribution, and slight fringes. However, it identifies many landslides, the most important (2.5 m displacement) being located in Kherba neighborhood, causing severe damages to dwellings. In addition, SAR images and optical images (Sentinel-2) confirm site investigations. Although in Grarem City, optical images could not detect any disorder, the DInSAR analysis detected some coherence decays and small fringes (3.94 km2 area). These unnoticed ground disorders were confirmed during fields inspection. Such results have key importance since they can serve as an alert to monitor the zone at the proper time. Furthermore, Displacement time series analysis of many interferograms (April 2015 to September 2020) using LiCSBAS were performed to investigate the pre-event conditions and precursors of the slopes instabilities., LiCSBAS detects a line-of-sight subsidence velocity of −110 mm/y in the back hillside of Kherba, and high displacement velocity at specific points in Grarem region.

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Combination of high frequency (Sentinel-2) and high resolution (Pléiades) satellite images for the monitoring of clayey landslide reactivations, application to the Harmalière landslide (French Alps).

10.5194/egusphere-egu2020-9368 ◽

2020 ◽

Author(s):

Denis Jongmans ◽

Sylvain Fiolleau ◽

Gregory Bièvre ◽

Guillaume Chambon ◽

Pascal Lacroix

Keyword(s):

Remote Sensing ◽

Time Series ◽

High Resolution ◽

High Frequency ◽

Satellite Images ◽

Cross Correlation ◽

Temperate Climate ◽

Optical Images ◽

Slow Moving ◽

Sentinel 2

Many regions of the world are exposed to landslides in clay deposits, which poses major problems for land management and population safety. In recent years, optical satellite imaging has emerged as a major and inexpensive tool for understanding and monitoring the kinematics of slow moving landslides, such as earthflows/earthslides, through easy access of data and reliable calibration.The Sentinel-2 optical satellites provide a global coverage of land surfaces with a 5-day revisit time at the Equator. We studied the ability of these freely available optical images to detect landslide reactivations in a zone of 25 km2 around the Harmali&#232;re landslide in the Tri&#232;ves area (western Alps, France). This area is characterized by the presence of a thick lacustrine clay layer that is affected by numerous landslides. Using a 9-month time-series of displacement derived from Sentinel-2 data, Lacroix et al. 2018 recently evidenced a precursor displacement of a major reactivation of the Harmali&#232;re landslide that occurred in June 2016.In this study, we attempted to detect following reactivations using the medium resolution high frequency satellite images (Sentinel 2) coupled with high resolution images (Pl&#233;iades) over a longer period (2016- 2019). We used an inversion strategy of redundant cross-correlation images to produce a robust time-series of displacement from Sentinel 2 data (Bontemps et al. 2018). By applying this technique, we were able to identify a reactivation of the same order of magnitude as the previous one, which affected the headscarp in January 2017. The reactivation signal is validated by the cross-correlation of Pl&#233;iades images taken at 2 years interval. We quantified this reactivation in time and space. We have also identified an area of 30x103 m2 located at the foot of the landslide, which was simultaneously accelerated by 10 m/month during this event. This information contributes to better understand the dynamics of the landslide that evolves from a solid to fluid behavior from the headscarp to the toe. However, a smaller slide that occurred in January 2018 at the headscarp was not detected by this method despite its significant size (10x103 m2). We attribute this non-detection to a major reshaping of the surface following reactivation.This study identified the possibilities and limitations of the proposed treatment method to detect and monitor landslides on a low-slope area located in clayey soils in a temperate climate.&#160;Bontemps, N., Lacroix, P. & Doin, M.-P. (2018) Inversion of deformation fields time-series from optical images, and application to the long term kinematics of slow-moving landslides in Peru. Remote Sensing of Environment, 210, 144&#8211;158. doi:10.1016/j.rse.2018.02.023Lacroix, P., Bi&#232;vre, G., Pathier, E., Kniess, U. & Jongmans, D. (2018) Use of Sentinel-2 images for the detection of precursory motions before landslide failures. Remote Sensing of Environment, 215, 507&#8211;516. doi:10.1016/j.rse.2018.03.042

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Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

Remote Sensing ◽

10.3390/rs11151836 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1836 ◽

Cited By ~ 15

Author(s):

Hassan Bazzi ◽

Nicolas Baghdadi ◽

Dino Ienco ◽

Mohammad El Hajj ◽

Mehrez Zribi ◽

...

Keyword(s):

Time Series ◽

Water Resource Management ◽

Vegetation Index ◽

Scale Up ◽

Principal Component ◽

Radar Data ◽

Temporal Series ◽

Optical Images ◽

Combined Use ◽

Sentinel 2

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

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The evolving response of mesopelagic fishes to declining midwater oxygen concentrations in the southern and central California Current

ICES Journal of Marine Science ◽

10.1093/icesjms/fsy154 ◽

2018 ◽

Vol 76 (3) ◽

pp. 626-638 ◽

Cited By ~ 2

Author(s):

J Anthony Koslow ◽

Pete Davison ◽

Erica Ferrer ◽

S Patricia A Jiménez Rosenberg ◽

Gerardo Aceves-Medina ◽

...

Keyword(s):

Baja California ◽

Southern Oscillation ◽

Global Climate ◽

California Current ◽

Deep Ocean ◽

Near Surface ◽

Oxygen Minimum Zones ◽

The North ◽

The Pacific ◽

Oxygen Concentrations

Abstract Declining oxygen concentrations in the deep ocean, particularly in areas with pronounced oxygen minimum zones (OMZs), are a growing global concern related to global climate change. Its potential impacts on marine life remain poorly understood. A previous study suggested that the abundance of a diverse suite of mesopelagic fishes off southern California was closely linked to trends in midwater oxygen concentration. This study expands the spatial and temporal scale of that analysis to examine how mesopelagic fishes are responding to declining oxygen levels in the California Current (CC) off central, southern, and Baja California. Several warm-water mesopelagic species, apparently adapted to the shallower, more intense OMZ off Baja California, are shown to be increasing despite declining midwater oxygen concentrations and becoming increasingly dominant, initially off Baja California and subsequently in the CC region to the north. Their increased abundance is associated with warming near-surface ocean temperature, the warm phase of the Pacific Decadal oscillation and Multivariate El Niño-Southern Oscillation Index, and the increased flux of Pacific Equatorial Water into the southern CC.

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