Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

2020 ◽  
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Sensing Data

<p>Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.</p><p>In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM’s “Database of Hydrological Time series of Inland Waters” (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.</p><p>Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.</p>

scholarly journals Building a model for predicting the water level in a river using remote sensing data from open sources

2020 ◽  
Vol 223 ◽  
pp. 02006
Author(s):  
Alexey Kolesnikov ◽  
Pavel Kikin ◽  
Anastasia Nungesser
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Water Level ◽  
Surface Area ◽  
Water Surface ◽  
Open Data ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Water Surface Area

The article discusses the possibility of predicting the water surface area of a river (and based on these values, the calculation of the water level) based on only open data of remote sensing. The area and depth of snow cover, the intensity of precipitation according to Landsat and Sentinel data and the monitoring indicators MODIS, Copernicus, REMSS are used as initial parameters. For the selected parameters, the degree of influence on the final forecast was assessed.

scholarly journals Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study

2014 ◽  
Vol 18 (3) ◽  
pp. 997-1007 ◽  
Author(s):  
C. I. Michailovsky ◽  
P. Bauer-Gottwein
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
River Basin ◽  
River Discharge ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Data Set ◽  
Radar Altimetry ◽  
Sensing Data

Abstract. River basin management can greatly benefit from short-term river discharge predictions. In order to improve model produced discharge forecasts, data assimilation allows for the integration of current observations of the hydrological system to produce improved forecasts and reduce prediction uncertainty. Data assimilation is widely used in operational applications to update hydrological models with in situ discharge or level measurements. In areas where timely access to in situ data is not possible, remote sensing data products can be used in assimilation schemes. While river discharge itself cannot be measured from space, radar altimetry can track surface water level variations at crossing locations between the satellite ground track and the river system called virtual stations (VS). Use of radar altimetry versus traditional monitoring in operational settings is complicated by the low temporal resolution of the data (between 10 and 35 days revisit time at a VS depending on the satellite) as well as the fact that the location of the measurements is not necessarily at the point of interest. However, combining radar altimetry from multiple VS with hydrological models can help overcome these limitations. In this study, a rainfall runoff model of the Zambezi River basin is built using remote sensing data sets and used to drive a routing scheme coupled to a simple floodplain model. The extended Kalman filter is used to update the states in the routing model with data from 9 Envisat VS. Model fit was improved through assimilation with the Nash–Sutcliffe model efficiencies increasing from 0.19 to 0.62 and from 0.82 to 0.88 at the outlets of two distinct watersheds, the initial NSE (Nash–Sutcliffe efficiency) being low at one outlet due to large errors in the precipitation data set. However, model reliability was poor in one watershed with only 58 and 44% of observations falling in the 90% confidence bounds, for the open loop and assimilation runs respectively, pointing to problems with the simple approach used to represent model error.

scholarly journals A framework for accurate, long-term, global and high resolution observations of tropospheric H<sub>2</sub>O-δD pairs – a MUSICA review

2016 ◽  
Author(s):  
M. Schneider ◽  
A. Wiegele ◽  
S. Barthlott ◽  
Y. González ◽  
E. Christner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Measurement Techniques ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Horizontal Resolution ◽  
Atmospheric Composition ◽  
Sensing Data ◽  
Middle Infrared

Abstract. Abstract. In the lower/middle troposphere H2O-δD pairs are good proxies for moisture pathways, however their observation is challenging. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating remote sensing with in-situ measurement techniques. The aim is to retrieve accurate tropospheric H2O-δD pairs from the middle infrared spectra measured from ground by the FTIR (Fourier Transform InfraRed) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper we review the MUSICA framework, present the final MUSICA products, and outline the NDACC/FTIR’s and METOP/IASI’s potential for observing accurate and consistent H2O-δD data pairs. First, we briefly resume the particularities of an H2O-δD pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H2O and δD in-situ profile references measured in the subtropics, between 0 and 7 km. Third, we empirically demonstrate that the calibrated remote sensing H2O-δD pairs can identify different lower/middle tropospheric moisture pathways and advert to the risk of misinterpretations caused by an incorrect processing of such remote sensing data. Fourth, we reveal that the different sensors (NDACC/FTIR instruments, MetOp/IASI-A, and MetOp/IASI-B) provide consistent H2O-δD pairs for very distinct atmospheric clear sky conditions. Fifth, we document the unique possibilities of the NDACC/FTIR instruments for providing long-term records (important for climatological studies) and of the MetOp/IASI sensors for observing diurnal signals on quasi global scale and with high horizontal resolution.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

Cloud-based Research Data Infrastructures Integrating In-Situ and Remote Sensing Data

2021 ◽  
Author(s):  
Simon Jirka ◽  
Benedikt Gräler ◽  
Matthes Rieke ◽  
Christian Autermann
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Research Data ◽  
Monitoring Networks ◽  
Sensing Data ◽  
Data Infrastructures ◽  
Practical Experiences ◽  
The One ◽  
Cloud Infrastructures

&lt;p&gt;For many scientific domains such as hydrology, ocean sciences, geophysics and social sciences, geospatial observations are an important source of information. Scientists conduct extensive measurement campaigns or operate comprehensive monitoring networks to collect data that helps to understand and to model current and past states of complex environment. The variety of data underpinning research stretches from in-situ observations to remote sensing data (e.g., from the European Copernicus programme) and contributes to rapidly increasing large volumes of geospatial data.&lt;/p&gt;&lt;p&gt;However, with the growing amount of available data, new challenges arise. Within our contribution, we will focus on two specific aspects: On the one hand, we will discuss the specific challenges which result from the large volumes of remote sensing data that have become available for answering scientific questions. For this purpose, we will share practical experiences with the use of cloud infrastructures such as the German platform CODE-DE and will discuss concepts that enable data processing close to the data stores. On the other hand, we will look into the question of interoperability in order to facilitate the integration and collaborative use of data from different sources. For this aspect, we will give special consideration to the currently emerging new generation of standards of the Open Geospatial Consortium (OGC) and will discuss how specifications such as the OGC API for Processes can help to provide flexible processing capabilities directly within Cloud-based research data infrastructures.&lt;/p&gt;

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