Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to Formosat-2 images

Abstract

This paper presents a new method developed for the atmospheric correction of the images that will be acquired by the Venμs satellite after its launch expected in early 2010. Every two days, the Venμs mission will provide 10 m resolution images of 50 sites, in 12 narrow spectral bands ranging from 415 nm to 910 nm. The sun-synchronous Venμs orbit will have a 2-day repeat cycle, and the images of a given site will always be acquired from the same place, at the same local hour, with constant observation angles. Thanks to these characteristics, the directional effects will be considerably reduced since only the solar angles will slowly vary with time.The algorithm that will be implemented for the atmospheric correction of Venμs data is being developed using both radiative transfer simulations and the actual data acquired by the Formosat-2 satellite. Because of its one-day sun-synchronous repeat cycle, Formosat-2 acquires images with a sun-viewing geometry close to the one Venμs will offer. With this geometry, reflectance time series are free from directional effects on the short term, a feature which reduces the number of unknowns to retrieve. The atmospheric corrections algorithm exploits this feature and the two following assumptions: -Aerosol optical properties vary quickly with time but slowly with location.-Surface reflectances vary quickly with location but slowly with time.Consequently, the top of atmosphere reflectance short term variations (10 to 15 days) are mainly due to the variations of aerosol optical properties, and it is thus possible to use these variations to characterise the atmospheric aerosols and to retrieve surface reflectances. This paper first describes the aerosol inversion method we developed and its results when applied to simulations. In the second part, we show the first tests of the method against three data sets acquired by Formosat-2 images with constant observation angles. Aeronet sun photometers measurements were available on all sites. Formosat-2 estimates of optical thickness compare favourably with Aeronet in situ measurements, leading to a noticeable improvement of the smoothness of time series of surface reflectances after atmospheric correction.

Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images

“…Accuracy in the geolocalization was estimated to about a half-pixel (4 m). The atmospheric correction was performed using the Simplified Method for Atmospheric Correction (SMAC) code [56] with an original method developed for the retrieval of aerosol optical thickness using constant values of atmospheric water vapor and ozone contents [29]. Finally, the Normalized Difference Vegetation Index (NDVI, [57]) was computed as the ratio of the difference between near-infrared and red reflectances from their sum.…”

“…It is particularly convenient to monitor agricultural areas, which are often made of a juxtaposition of small, more or less homogeneous units (fields) with a high temporal dynamics due to the vegetation phenological cycle (growth and senescence in a few months for annual crops) and agricultural operations (sowing, plowing, irrigation, harvest etc.). The high density of mono-directional FORMOSAT-2 observations has allowed improving image preprocessing (cloud detection and atmospheric correction) and obtaining very accurate time series of surface reflectances and vegetation indices [29]. A few studies have demonstrated the usefulness of time series of FORMOSAT-2 images for the mapping of land cover, the detection of agricultural operations and the monitoring of several key biophysical variables [30][31][32][33][34].…”

An information-based criterion to measure pixel-level thematic uncertainty in land cover classifications

“…Therefore, the Landsat-8 image is critical to discriminate between summer crops such as corn, potato and sugar beet. Both the SPOT-4 and the Landsat-8 data were calibrated, orthorectified and corrected for the atmosphere (Hagolle et al 2008(Hagolle et al , 2015. The Landsat-8 image was resampled to SPOT-4's resolution and only the first seven spectral bands were kept.…”

The global SMOS Level 3 daily soil moisture and brightness temperature maps

“…Retrieval using temporal series is becoming increasingly common in operational EO retrieval algorithms for optical and to some extent microwave technologies. Some examples in the optical domain are the correction of aerosol impact for visible images (Hagolle et al, 2008(Hagolle et al, , 2015, cloud detection (Hagolle et al, 2010) and the use of MO for land cover classification (Inglada and Mercier, 2007). The previous methodologies are being implemented for high-end level 2-A and level 3 products for the Copernicus Sentinel-2 mission.…”