0000000000170819
AUTHOR
Javier Garcia-haro
Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine
High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODI…
Prototyping algorithm for retrieving FAPAR using MSG data in the context of the LSA SAF project
This paper describes the prototyping algorithm developed for retrieving the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using MSG data in the framework of satellite application facility on land surface analysis (LSA SAF). The prototyping relies on the Roujean and Breon (1995) method, which is based on simulations of visible and near infrared reflectance values in an optimal geometry. A relationship is found between a vegetation index and daily FAPAR The algorithm has been applied to one year of MSG BRDF data since August 2005, using a temporal frequency of 5-days, and then validated against a set of operational satellite FAPAR products such as MODIS, MERIS, SeaWiFS and …
Direct validation of FVC, LAI and FAPAR VEGETATION/SPOT derived products using LSA SAF methodology
The aim of this work is to perform a direct validation of fraction of vegetation cover (FVC), leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) resulting products from applying the LSA SAF methodology to VEGETATION BRDF data. LSA SAF adapted algorithms were tested in adequate test sites comprising different continental biomes covering a wide range of FVC, LAI and FAPAR values. Results seem to indicate the competitiveness of LSA SAF proposed methodology to retrieve remotely sensed biophysical parameters. A noticeable good agreement regarding the ground measurements was found. The overall accuracy (RAISE) is around 20% for FVC and FAPAR and around 15% …
Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties
products (R 2 >0.74), with typical deviations of<0.5 for nonforest and<1.0 for forest biomes. JRC-TIP, the only effective LAI product, is about half the values of the other LAI products. The average uncertainties and relative uncertainties are in the following order: MODIS (0.17, 11.5%)<GEOV1 (0.24, 26.6%)<Land-SAF (0.36, 37.8%) <JRC-TIP (0.43, 114.3%). The highest relative uncertainties usually appear in ecological transition zones. More than 75% of MODIS, GEOV1, JRC-TIP, and Land-SAF pixels are within the absolute uncertainty requirements (� 0.5) set by the Global Climate Observing System (GCOS), whereas more than 78.5% of MODIS and 44.6% of GEOV1 pixels are within the threshold for relat…
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer.
The Satellite Application Facility for Land Surface Analysis
Information on land surface properties finds applications in a range of areas related to weather forecasting, environmental research, hazard management and climate monitoring. Remotely sensed observations yield the only means of supplying land surface information with adequate time sampling and a wide spatial coverage. The aim of the Satellite Application Facility for Land Surface Analysis (Land-SAF) is to take full advantage of remotely sensed data to support land, land-atmosphere and biosphere applications, with emphasis on the development and implementation of algorithms that allow operational use of data from European Organization for the Exploitation of Meteorological Satellites (EUMET…