scholarly journals END-TO-END PHYSICS-INFORMED REPRESENTATION LEARNING FOR SATELLITE OCEAN REMOTE SENSING DATA: APPLICATIONS TO SATELLITE ALTIMETRY AND SEA SURFACE CURRENTS

2021 ◽  
Vol V-3-2021 ◽  
pp. 295-302
Author(s):  
R. Fablet ◽  
M. M. Amar ◽  
Q. Febvre ◽  
M. Beauchamp ◽  
B. Chapron
Keyword(s):  
Remote Sensing ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Representation Learning ◽  
Space Time ◽  
Satellite Orbits ◽  
Sensing Data ◽  
End To End ◽  
Ocean Remote Sensing ◽  
Learning Schemes

Abstract. This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors’ characteristics and satellite orbits. With a focus on satellite altimetry, we show that end-to-end learning schemes based on variational formulations provide new means to explore and exploit such observation datasets. Through Observing System Simulation Experiments (OSSE) using numerical ocean simulations and real nadir and wide-swath altimeter sampling patterns, we demonstrate their relevance w.r.t. state-of-the-art and operational methods for space-time interpolation and short-term forecasting issues. We also stress and discuss how they could contribute to the design and calibration of ocean observing systems.

Test of specific methods for reduction of spectral dimension of ocean remote sensing data

1993 ◽  
Vol 29 (1) ◽  
pp. 61-66
Author(s):  
A. Neumann ◽  
G. Zimmermann ◽  
H. Krawczyk ◽  
T. Walzel
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Spectral Dimension ◽  
Sensing Data ◽  
Ocean Remote Sensing

scholarly journals Indirect Validation of Ocean Remote Sensing Data via Numerical Model: An Example of Wave Heights from Altimeter

2020 ◽  
Vol 12 (16) ◽  
pp. 2627 ◽  
Author(s):  
Haoyu Jiang
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Numerical Models ◽  
Remote Sensing Data ◽  
Validation Method ◽  
Wave Hindcast ◽  
Sensing Data ◽  
Data Pair ◽  
Wave Heights ◽  
Ocean Remote Sensing

Using numerical model outputs as a bridge, an indirect validation method for remote sensing data was developed to increase the number of effective collocations between remote sensing data to be validated and reference data. The underlying idea for this method is that the local spatial-temporal variability of specific parameters provided by numerical models can compensate for the representativeness error induced by differences of spatial-temporal locations of the collocated data pair. Using this method, the spatial-temporal window for collocation can be enlarged for a given error tolerance. To test the effectiveness of this indirect validation approach, significant wave height (SWH) data from Envisat were indirectly compared against buoy and Jason-2 SWHs, using the SWH gradient information from a numerical wave hindcast as a bridge. The results indicated that this simple indirect validation method is superior to “direct” validation.

scholarly journals Deep-learning-based information mining from ocean remote-sensing imagery

2020 ◽  
Vol 7 (10) ◽  
pp. 1584-1605 ◽  
Author(s):  
Xiaofeng Li ◽  
Bin Liu ◽  
Gang Zheng ◽  
Yibin Ren ◽  
Shuangshang Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Remote Sensing Data ◽  
Information Mining ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
Sensing Applications ◽  
Data Archives ◽  
Ocean Remote Sensing ◽  
Learning Frameworks

Abstract With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning—a powerful technology recently emerging in the machine-learning field—has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery.

scholarly journals Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3132
Author(s):  
Emmanouil A. Varouchakis ◽  
Anna Kamińska-Chuchmała ◽  
Grzegorz Kowalik ◽  
Katerina Spanoudaki ◽  
Manuel Graña
Keyword(s):  
Remote Sensing ◽  
Wide Spectrum ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Spatiotemporal Analysis ◽  
Space Time ◽  
Attractive Alternative ◽  
Kriging Method ◽  
Sensing Data ◽  
Crete Island

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

scholarly journals Improving the Efficiency of Remote Sensing Data Interpretation by Analyzing Neighborhood Descriptors

2018 ◽  
Vol 28 (3) ◽  
pp. 352-365
Author(s):  
Stanislav A. Yamashkin ◽  
Anatoliy A. Yamashkin
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
System Analysis ◽  
Remote Sensing Data ◽  
Automated Analysis ◽  
Data Interpretation ◽  
Space Time ◽  
The State ◽  
Survey Analysis ◽  
Sensing Data

Introduction. In evaluating the space-time structure of the Earth’s surface, the data of remote sensing of the Earth become more important. Increasing the effectiveness of space survey analysis tools is possible through studying the problem of obtaining an integrated space-time characterization of the state of lands. The purpose of this study is to improve the accuracy of the automated analysis of remote sensing data by taking into account the invariant and dynamic descriptors of the vicinity. Materials and Methods. In order to improve the accuracy of the remote sensing data classification, a computation of complex space-time characteristics of the state of the lands was conducted based on the system analysis of data characterizing the dynamic and invariant states of the territory surrounding the geophysical site. The formalization of this process includes methods for calculating a set of numerical descriptors of the neighborhood: local entropy, local range, standard deviation, color moment, histogram of hues, and color cortege. A technique for calculating a complex descriptor based on the Fisher vector is described. To approbate the solution, a plan for the experiment was drawn up and a sample of the initial data was sampled. Results. The approbation of the methodology and the algorithm developed on its basis, implemented as a set of programs, on the test polygon system showed a variation in the classification accuracy in the range of 81–89% (without regard to the neighborhood), and taking into account the neighborhood, it increases to 91–97%. It is revealed that a significant increase in the radius of the analyzed neighborhood leads to a decrease in the classification accuracy. Conclusions. The application of the developed set of programs allows for the rapid implementation of modeling of spatial systems for the purpose of thematic mapping of land use and analyzing the development of emergency situations. The developed methodology for analyzing lands with regard to the descriptors of the neighborhood makes it possible to improve the accuracy of classification.

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>

Sign in / Sign up

Export Citation Format

Share Document