Estimation of Evapotranspiration through Open Access Earth Observation Data Sets and Its Validation with Ground Observation

2018 ◽  
pp. 173-189 ◽  
Author(s):  
Kishan Singh Rawat ◽  
Sudhir Kumar Singh ◽  
Anju Bala
Keyword(s):  
Open Access ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Ground Observation ◽  
Earth Observation Data

scholarly journals Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery

2019 ◽  
Vol 11 (8) ◽  
pp. 907 ◽  
Author(s):  
Vasileios Syrris ◽  
Paul Hasenohr ◽  
Blagoj Delipetrev ◽  
Alexander Kotsev ◽  
Pieter Kempeneers ◽  
...  
Keyword(s):  
Random Forests ◽  
Computational Models ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Computational Aspects ◽  
Learning Set ◽  
Earth Observation Data ◽  
Class Segmentation ◽  
Sentinel 2

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union’s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models’ deployment and their transferability.

Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India

2017 ◽  
Vol 33 (11) ◽  
pp. 1202-1222 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Prosper Basommi Laari ◽  
Sk. Mustak ◽  
Prashant K. Srivastava ◽  
Szilárd Szabó
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Earth Observation Data ◽  
Tons River Basin

scholarly journals Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets

2016 ◽  
pp. 1-15 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Prashant K. Srivastava ◽  
Szilárd Szabó ◽  
George P. Petropoulos ◽  
Manika Gupta ◽  
...  
Keyword(s):  
Land Cover ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Spatial Metrics ◽  
Land Cover Dynamics ◽  
Earth Observation Data

scholarly journals A Self-Calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-Access Earth Observation Data

2017 ◽  
Vol 9 (1) ◽  
pp. 86 ◽  
Author(s):  
Ate Poortinga ◽  
Wim Bastiaanssen ◽  
Gijs Simons ◽  
David Saah ◽  
Gabriel Senay ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Open Access ◽  
Earth Observation ◽  
Observation Data ◽  
Ungauged Basins ◽  
Remote Sensing Model ◽  
Sensing Model ◽  
Earth Observation Data

scholarly journals Technical note: A view from space on global flux towers by MODIS and Landsat: The FluxnetEO dataset

2021 ◽  
Author(s):  
Sophia Walther ◽  
Simon Besnard ◽  
Jacob A. Nelson ◽  
Tarek S. El-Madany ◽  
Mirco Migliavacca ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Eddy Covariance ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Earth Observation ◽  
Research Network ◽  
Observation System ◽  
Data Sets ◽  
Observation Data ◽  
Earth Observation Data

Abstract. The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at several hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, and Terrestrial Ecosystem Research Network (TERN) / OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the training and validation of ecosystem models. However, insufficient quality, frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions drawn from them. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40° off-nadir. We offer to the community pre-processed Earth observation data in a radius of 2 km around 338 flux sites based on the MCD43A4/A2, MxD11A1 MODIS products and Landsat collection~1 Tier1 and Tier2 products. The data sets we provide can widely facilitate the integration of activities in the fields of eddy-covariance, remote sensing and modelling.

scholarly journals A Python Library for the Jupyteo IDE Earth Observation Processing Tool Enabling Interoperability with the QGIS System for Use in Data Science

2021 ◽  
Vol 16 (1) ◽  
pp. 117-144
Author(s):  
Michał Bednarczyk
Keyword(s):  
Data Science ◽  
Large Data ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Development Environment ◽  
Scientific Disciplines ◽  
Earth Observation Data ◽  
Test Use ◽  
Analytical Tools

This paper describes JupyQgis – a new Python library for Jupyteo IDE enabling interoperability with the QGIS system. Jupyteo is an online integrated development environment for earth observation data processing and is available on a cloud platform. It is targeted at remote sensing experts, scientists and users who can develop the Jupyter notebook by reusing embedded open-source tools, WPS interfaces and existing notebooks. In recent years, there has been an increasing popularity of data science methods that have become the focus of many organizations. Many scientific disciplines are facing a significant transformation due to data-driven solutions. This is especially true of geodesy, environmental sciences, and Earth sciences, where large data sets, such as Earth observation satellite data (EO data) and GIS data are used. The previous experience in using Jupyteo, both among the users of this platform and its creators, indicates the need to supplement its functionality with GIS analytical tools. This study analyzed the most efficient way to combine the functionality of the QGIS system with the functionality of the Jupyteo platform in one tool. It was found that the most suitable solution is to create a custom library providing an API for collaboration between both environments. The resulting library makes the work much easier and simplifies the source code of the created Python scripts. The functionality of the developed solution was illustrated with a test use case.

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