scholarly journals Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2

2019 ◽  
Vol 11 (9) ◽  
pp. 1010 ◽  
Author(s):  
Christian Schwatke ◽  
Daniel Scherer ◽  
Denise Dettmering
Keyword(s):  
Time Series ◽  
Surface Area ◽  
Time Variable ◽  
New Approach ◽  
Data Gaps ◽  
Lakes And Reservoirs ◽  
Variable Water ◽  
Land Water ◽  
Sentinel 2

In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km 2 are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI).

Monitoring of water table dynamics in peatlands with OPTRAM: Towards globally applicable algorithms in Google Earth Engine using Landsat and Sentinel-2

2021 ◽  
Author(s):  
Iuliia Burdun ◽  
Michel Bechtold ◽  
Viacheslav Komisarenko ◽  
Annalea Lohila ◽  
Elyn Humphreys ◽  
...  
Keyword(s):  
Time Series ◽  
Water Table ◽  
Input Data ◽  
Short Wave ◽  
Google Earth ◽  
Google Earth Engine ◽  
Short Wave Infrared ◽  
Northern Peatlands ◽  
Sentinel 2

<p>Fluctuations of water table depth (WTD) affect many processes in peatlands, such as vegetation development and emissions of greenhouse gases. Here, we present the OPtical TRApezoid Model (OPTRAM) as a new method for satellite-based monitoring of the temporal variation of WTD in peatlands. OPTRAM is based on the response of short-wave infrared reflectance to the vegetation water status. For five northern peatlands with long-term in-situ WTD records, and with diverse vegetation cover and hydrological regimes, we generate a suite of OPTRAM index time series using (a) different procedures to parametrise OPTRAM (peatland-specific manual vs. globally applicable automatic parametrisation in Google Earth Engine), and (b) different satellite input data (Landsat vs. Sentinel-2). The results based on the manual parametrisation of OPTRAM indicate a high correlation with in-situ WTD time-series for pixels with most suitable vegetation for OPTRAM application (mean Pearson correlation of 0.7 across sites), and we will present the performance differences when moving from a manual to an automatic procedure. Furthermore, for the overlap period of Landsat and Sentinel-2, which have different ranges and widths of short-wave infrared bands used for OPTRAM calculation, the impact of the satellite input data to OPTRAM will be analysed. Eventually, the challenge of merging different satellite missions in the derivation of OPTRAM time series will be explored as an important step towards a global application of OPTRAM for the monitoring of WTD dynamics in northern peatlands.</p>

scholarly journals Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions

2020 ◽  
Vol 12 (15) ◽  
pp. 2471
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
High Latitude ◽  
High Latitudes ◽  
Series Analysis ◽  
Data Gaps ◽  
Northern High Latitude ◽  
Study Sites ◽  
Sentinel 2 ◽  
Permafrost Regions

Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances.

scholarly journals Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1469 ◽  
Author(s):  
Marcela Pereira-Sandoval ◽  
Ana Ruescas ◽  
Patricia Urrego ◽  
Antonio Ruiz-Verdú ◽  
Jesús Delegido ◽  
...  
Keyword(s):  
Near Infrared ◽  
Atmospheric Correction ◽  
Inland Waters ◽  
Atmospheric Effects ◽  
Image Correction ◽  
Lakes And Reservoirs ◽  
Dark Pixel ◽  
Organic Particles ◽  
Sentinel 2

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

scholarly journals Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa

2021 ◽  
Vol 13 (17) ◽  
pp. 3342
Author(s):  
Marcel Urban ◽  
Konstantin Schellenberg ◽  
Theunis Morgenthal ◽  
Clémence Dubois ◽  
Andreas Hirner ◽  
...  
Keyword(s):  
South Africa ◽  
Time Series ◽  
Random Forest ◽  
Expert Knowledge ◽  
Arid Ecosystems ◽  
Free State ◽  
Time Step ◽  
Free State Province ◽  
Sentinel 2

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be the major factors driving land degradation. This study focuses on mapping the distribution of the slangbos shrub (Seriphium plumosum) in a test region in the Free State Province of South Africa. The goal of this study is to monitor the slangbos encroachment on cultivated land by synergistically combining Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Sentinel-2) Earth observation information. Both optical and radar satellite data are sensitive to different vegetation properties and surface scattering or reflection mechanisms caused by the specific sensor characteristics. We used a supervised random forest classification to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were derived based on expert knowledge and in situ information from the Department of Agriculture, Land Reform and Rural Development (DALRRD). We found that the Sentinel-1 VH (cross-polarization) and Sentinel-2 SAVI (Soil Adjusted Vegetation Index) time series information have the highest importance for the random forest classifier among all input parameters. The modelling results confirm the in situ observations that pastures are most affected by slangbos encroachment. The estimation of the model accuracy was accomplished via spatial cross-validation (SpCV) and resulted in a classification precision of around 80% for the slangbos class within each time step.

scholarly journals DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry

2015 ◽  
Vol 19 (10) ◽  
pp. 4345-4364 ◽  
Author(s):  
C. Schwatke ◽  
D. Dettmering ◽  
W. Bosch ◽  
F. Seitz
Keyword(s):  
Time Series ◽  
Water Level ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Altimeter Data ◽  
Inland Waters ◽  
New Approach ◽  
Inland Water ◽  
Rivers And Lakes

Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, for some years, this technology has also been used to retrieve water levels from reservoirs, wetlands and in general any inland water body, although the radar altimetry technique has been especially applied to rivers and lakes. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series of rivers and lakes available through the web service "Database for Hydrological Time Series over Inland Waters" (DAHITI). The new method is based on an extended outlier rejection and a Kalman filter approach incorporating cross-calibrated multi-mission altimeter data from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, including their uncertainties. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparisons with in situ gauge data and results from external inland altimeter databases. The new approach yields rms differences with respect to in situ data between 4 and 36 cm for lakes and 8 and 114 cm for rivers. For most study cases, more accurate height information than from other available altimeter databases can be achieved.

scholarly journals Volume Variations of Small Inland Water Bodies from a Combination of Satellite Altimetry and Optical Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1606 ◽  
Author(s):  
Christian Schwatke ◽  
Denise Dettmering ◽  
Florian Seitz
Keyword(s):  
Time Series ◽  
Water Level ◽  
Surface Area ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Inland Waters ◽  
Data Sets ◽  
Surface Areas ◽  
Lakes And Reservoirs ◽  
Optical Imagery

In this study, a new approach for estimating volume variations of lakes and reservoirs using water levels from satellite altimetry and surface areas from optical imagery is presented. Both input data sets, namely water level time series and surface area time series, are provided by the Database of Hydrological Time Series of Inland Waters (DAHITI), developed and maintained by the Deutsches Geodätisches Forschungsinsitut der Technischen Universität München (DGFI-TUM). The approach is divided into three parts. In the first part, a hypsometry model based on the new modified Strahler approach is computed by combining water levels and surface areas. The hypsometry model describes the dependency between water levels and surface areas of lakes and reservoirs. In the second part, a bathymetry between minimum and maximum surface area is computed. For this purpose, DAHITI land-water masks are stacked using water levels derived from the hypsometry model. Finally, water levels and surface areas are intersected with the bathymetry to estimate a time series of volume variations in relation to the minimum observed surface area. The results are validated with volume time series derived from in-situ water levels in combination with bathymetric surveys. In this study, 28 lakes and reservoirs located in Texas are investigated. The absolute volumes of the investigated lakes and reservoirs vary between 0.062 km 3 and 6.041 km 3 . The correlation coefficients of the resulting volume variation time series with validation data vary between 0.80 and 0.99. Overall, the relative errors with respect to volume variations vary between 2.8% and 14.9% with an average of 8.3% for all 28 investigated lakes and reservoirs. When comparing the resulting RMSE with absolute volumes, the absolute errors vary between 1.5% and 6.4% with an average of 3.1%. This study shows that volume variations can be calculated with a high accuracy which depends essentially on the quality of the used water levels and surface areas. In addition, this study provides a hypsometry model, high-resolution bathymetry and water level time series derived from surface areas based on the hypsometry model. All data sets are publicly available on the Database of Hydrological Time Series of Inland Waters.

scholarly journals Diffuse Attenuation of Clear Water Tropical Reservoir: A Remote Sensing Semi-Analytical Approach

2020 ◽  
Vol 12 (17) ◽  
pp. 2828
Author(s):  
Victor Pedroso Curtarelli ◽  
Cláudio Clemente Faria Barbosa ◽  
Daniel Andrade Maciel ◽  
Rogério Flores Júnior ◽  
Felipe Menino Carlos ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Tropical Reservoir ◽  
Kd Values ◽  
Analytical Algorithm ◽  
Essential Parameter ◽  
Reservoir Watershed ◽  
Sentinel 2 ◽  
Três Marias Reservoir

The diffuse attenuation coefficient of downwelling irradiance (Kd) is an essential parameter for inland waters research by remotely sensing the water transparency. Lately, Kd semi-analytical algorithms substituted the empirical algorithms widely employed. The purpose of this research was to reparametrize a semi-analytical algorithm to estimate Kd and then apply it to a Sentinel-2 MSI time-series (2017–2019) for the Três Marias reservoir, Brazil. The results for the Kd semi-analytical reparametrization achieved good accuracies, reaching mean absolute percentage errors (MAPE) for bands B2, B3 and B4 (492, 560 and 665 nm), lower than 21% when derived from in-situ remote sensing reflectance (Rrs), while for MSI Data, a derived MAPE of 12% and 38% for B2 and B3, respectively. After the application of the algorithm to Sentinel-2 images time-series, seasonal patterns were observed in the results, showing high Kd values at 492 nm during the rainy periods, mainly in the tributary mouths, possibly due to an increase in the surface runoff and inflows and outflow rates in the reservoir watershed.

scholarly journals Kalman filter approach for estimating water level time series over inland water using multi-mission satellite altimetry

2015 ◽  
Vol 12 (5) ◽  
pp. 4813-4855 ◽  
Author(s):  
C. Schwatke ◽  
D. Dettmering ◽  
W. Bosch ◽  
F. Seitz
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Water Level ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Altimeter Data ◽  
New Approach ◽  
Inland Water ◽  
Filter Approach

Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, since some years, this technology is also used for observing inland water levels of lakes and rivers. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series available through the web service "Database for Hydrological Time Series over Inland Water" (DAHITI). The method is based on a Kalman filter approach incorporating multi-mission altimeter observations and their uncertainties. As input data, cross-calibrated altimeter data from Envisat, ERS-2, Jason-1, Jason-2, Topex/Poseidon, and SARAL/AltiKa are used. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparison with in-situ gauge data and results from external inland altimeter databases. The new approach yields RMS differences with respect to in-situ data between 4 and 38 cm for lakes and 12 and 139 cm for rivers, respectively. For most study cases, more accurate height information than from available other altimeter data bases can be achieved.

scholarly journals Monitoring Reservoir Drought Dynamics with Landsat and Radar/Lidar Altimetry Time Series in Persistently Cloudy Eastern Brazil

2019 ◽  
Vol 11 (7) ◽  
pp. 827 ◽  
Author(s):  
Jamon Van Den Hoek ◽  
Augusto Getirana ◽  
Hahn Jung ◽  
Modurodoluwa Okeowo ◽  
Hyongki Lee
Keyword(s):  
Time Series ◽  
Surface Area ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Volume Changes ◽  
Landsat Images ◽  
Reservoir Volume ◽  
Eastern Brazil

Tropical reservoirs are critical infrastructure for managing drinking and irrigation water and generating hydroelectric power. However, long-term spaceborne monitoring of reservoir storage is challenged by data scarcity from near-persistent cloud cover and drought, which may reduce volumes below those in the observational record. In evaluating our ability to accurately monitor long-term reservoir volume dynamics using spaceborne data and overcome such observational challenges, we integrated optical, lidar, and radar time series to estimate reservoir volume dynamics across 13 reservoirs in eastern Brazil over a 12-year (2003–2014) period affected by historic drought. We (i) used 1560 Landsat images to measure reservoir surface area; (ii) built reservoir-specific regression models relating surface area and elevation from ICESat GLAS and Envisat RA-2 data; (iii) modeled volume changes for each reservoir; and (iv) compared modeled and in situ reservoir volume changes. Regression models had high goodness-of-fit (median RMSE = 0.89 m and r = 0.88) across reservoirs. Even though 88% of an average reservoir’s volume time series was based on modeled area–elevation relationships, we found exceptional agreement (RMSE = 0.31 km3 and r = 0.95) with in situ volume time series, and accurately captured seasonal recharge/depletion dynamics and the drought’s prolonged drawdown. Disagreements in volume dynamics were neither driven by wet/dry season conditions nor reservoir capacity, indicating analytical efficacy across a range of monitoring scenarios.

scholarly journals An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2584
Author(s):  
Hassan Bazzi ◽  
Nicolas Baghdadi ◽  
Ghaith Amin ◽  
Ibrahim Fayad ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Time Series ◽  
Selection Criteria ◽  
Vegetation Index ◽  
Data Availability ◽  
Training Data ◽  
Classification Model ◽  
In Situ Data ◽  
Ndvi Time Series ◽  
Sentinel 2

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

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