Search results for "Earth observation"
showing 10 items of 82 documents
Cloud detection on the Google Earth engine platform
2017
The vast amount of data acquired by current high resolution Earth observation satellites implies some technical challenges to be faced. Google Earth Engine (GEE) platform provides a framework for the development of algorithms and products built over this data in an easy and scalable manner. In this paper, we take advantage of the GEE platform capabilities to exploit the wealth of information in the temporal dimension by processing a long time series of satellite images. A cloud detection algorithm for Landsat-8, which uses previous images of the same location to detect clouds, is implemented and tested on the GEE platform.
Transferring deep learning models for cloud detection between Landsat-8 and Proba-V
2020
Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…
A unified vegetation index for quantifying the terrestrial biosphere
2021
[EN] Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross prim…
Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory
2018
International audience; Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability ('p-theory'), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types.…
A physiology-based Earth observation model indicates stagnation in the global gross primary production during recent decades
2020
Abstract Earth observation‐based estimates of global gross primary production (GPP) are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. In this study, we attempt an ecosystem‐level physiological approach of estimating GPP using an asymptotic light response function (LRF) between GPP and incoming photosynthetically active radiation (PAR) that better represents the response observed at high spatiotemporal resolutions than the conventional light use efficiency approach. Modelled GPP is thereafter constrained with meteorological and hydrological variables. The variability in field‐observed GPP, net primary productivity an…
Discovering Differential Equations from Earth Observation Data
2020
Modeling and understanding the Earth system is a constant and challenging scientific endeavour. When a clear mechanistic model is unavailable, complex or uncertain, learning from data can be an alternative. While machine learning has provided excellent methods for detection and retrieval, understanding the governing equations of the system from observational data seems an elusive problem. In this paper we introduce sparse regression to uncover a set of governing equations in the form of a system of ordinary differential equations (ODEs). The presented method is used to explicitly describe variable relations by identifying the most expressive and simplest ODEs explaining data to model releva…
Design of a generic end-to-end mission performance simulator and application to the performance analysis of the FLEX/Sentinel-3 mission
2016
La Observación de la Tierra mediante técnicas de teledetección con instrumentos ópticos en satélite tiene como objetivo monitorizar los procesos bio-geofísicos en la superficie y atmósfera terrestre, adquiriendo datos a diferentes longitudes de onda del espectro electromagnético. Con el fin de asegurar el mantenimiento de las observaciones y las capacidades para entender el sistema Tierra, nuevas misiones satelitales están siendo desarrolladas por agencias espaciales nacionales e internacionales así como organizaciones de investigación. En este contexto, los simuladores de misiones espaciales (E2ES por sus siglas en inglés, End-to-End Mission Performance Simulator) ofrecen a los científicos…
A method for the surface reflectance retrieval from PROBA/CHRIS data over land: application to ESA SPARC campaigns
2005
The Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (PROBA) platform system provides the first high spatial resolution hyper-spectral/multiangular remote sensing data from a satellite system, what represents a new source of information for Earth Observation purposes. A fully consistent radiative transfer approach is always preferred when dealing with the retrieval of surface reflectance from hyperspectral/multiangular data. However, due to the reported calibration anomalies for CHRIS data, a direct atmospheric correction based on physical radiative transfer modeling is not possible, and the method must somehow compensate for such calibration pr…
SUSTAINABLE AGRICULTURE MANAGEMENTS TO CONTROL SOIL EROSION
2021
[EN] High rates of soil erosion compromise sustainable agriculture. In rainfed agricultural fields, erosion rates several orders of magnitude higher than the erosion rates considered tolerable have been quantified. In Mediterranean rainfed crops such as vineyards, almonds and olive groves, and in the new sloping citrus and persimmon plantations, the rates of soil loss make it necessary to apply measures to reduce them to avoid collapse in agricultural production. Managements such as weeds, catch crops and mulches (straw and pruning remains) are viable options to achieve sustainability. This work applies measurements through plots, simulated rainfall experiments and ISUM (Improved Stock-Unea…
Robustified smoothing for enhancement of thermal image sequences affected by clouds
2015
Obtaining radiometric surface temperature information with both high acquisition rate and high spatial resolution is still not possible through a single sensor. However, in several earth observation applications, the fusion of data acquired by different sensors is a viable solution for so called image sharpening. A related issue is the presence of clouds, which may impair the performance of the data fusion algorithms. In this paper we propose a robustified setup for the sharpening of thermal images in a non real-time scenario, capable to deal with missing thermal data due to cloudy pixels, and robust with respect to cloud mask misclassifications. The effectiveness of the presented technique…