Validation of an automatic image ortho-rectification processing

Baillarin, Simon; Gleyzes, J.-P.; Latry, Christophe; Bouillon, A.; Breton, Eric; Cunin, L.; Vesco, C.; Delvit, Jean-Marc

doi:10.1109/igarss.2004.1368680

Cited by 10 publications

(9 citation statements)

References 1 publication

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“…Hence, ESA data were orthorectified using USGS data as a reference following the orthorectification methodology of the Centre National d'Etudes Spatiales (Baillarin et al, 2004). All images were projected to Lambert-93.…”

Section: Landsat Processingmentioning

confidence: 99%

A snow cover climatology for the Pyrenees from MODIS snow products

Gascoin

Hagolle

Huc

et al. 2015

Hydrol. Earth Syst. Sci.

149

118

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Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97 % (κ = 0.85) for MOD10A1 and 96 % (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96 %, κ = 0.77; MYD10A1: 95 %, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50 % of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011-2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

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Section: Landsat Processingmentioning

confidence: 99%

A snow cover climatology for the Pyrenees from MODIS snow products

Gascoin

Hagolle

Huc

et al. 2015

Hydrol. Earth Syst. Sci.

149

118

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“…During the 2006 experiment, thirty five FORMOSAT-2 images were acquired every 3 to 4 days during 8 months at 10:30 TU from March to October 2006, and with a constant viewing angle of 41 • over the Crau region. These images were first geolocated, registered and calibrated at CNES (Centre National d'Etudes Spatiales, France, according to the method described in Baillarin et al, 2004). Then the clouds and their shadows were discarded, and the images were corrected from atmospheric effects using the multi-temporal aerosol detection method developed by Hagolle et al (2008).…”

Section: Formosat-2 Data: Description Processing and Derived Productsmentioning

confidence: 99%

Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

Courault

Hadria²,

Ruget³

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. The aim of this study is to propose methods to improve crop and water management in Mediterranean regions. At landscape scale, there is a spatial variability of agricultural practices, particularly for grasslands irrigated by flooding. These grasslands are harvested three times per year and produce high quality hay, but their productions decreased significantly during the last few years because of the water scarcity. It is therefore important to assess the real water requirement for crops in order to predict productions in the case of agricultural practice modifications. Until now, the spatial variability of agricultural practices was obtained through surveys from farmers, but this method was tedious to describe an entire region. Thus, the specific aim of the study is to develop and assess a new method based on a crop model for estimating water balance and crop yield constrained by products derived from optical remote sensing data with high spatio-temporal resolution.A methodology, based on the combined use of FORMOSAT-2 images and the STICS crop model, was developed to estimate production, evapotranspiration and drainage of irrigated grasslands in "the Crau" region in the South Eastern France. Numerous surveys and ground measurements were performed during an experiment conducted in 2006. Simple algorithms were developed to retrieve the dynamic of Leaf Area Index (LAI) for each plot and the main agricultural practices such as mowing and irrigation dates. These variables computed from remote sensing were then used to parameterize STICS, applied at region scale to estimate the spatial variability of water budget associated with the biomass productions. Results are displayed at the farm Correspondence to: D. Courault (courault@avignon.inra.fr) scale. Satisfactory results were obtained when compared to ground measurements. The method for the extrapolation to other regions or crops is discussed as regard to data available.

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“…They were processed for geolocation, radiometric calibration and atmospheric perturbations following Hagolle et al (2008). Clouds and related shadows were discarded following Baillarin et al (2004). Over the 36 images collected between March and October, 30 images were cloudless, with a temporal gap spanning from April 17th to May 14th because of cloudy conditions.…”

Section: Formosat-2 Datamentioning

confidence: 99%