Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

Abstract

A key step in the processing of satellite imagery is the radiometric correction of images to account for reflectance that water vapor, atmospheric dust, and other atmospheric elements add to the images, causing imprecisions in variables of interest estimated at the earth's surface level. That issue is important when performing spatiotemporal analyses to determine ecosystems' productivity. In this study, three correction methods were applied to satellite images for the period 2010-2014. These methods were Atmospheric Correction for Flat Terrain 2 (ATCOR2), Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Dark Object Substract 1 (DOS1). The images included 12 sub-scenes from the Landsat Thematic Mapper (TM) and the Operational Land Imager (OLI) sensors. The images corresponded to three Permanent Monitoring Sites (PMS) of grasslands, 'Teseachi', 'Eden', and 'El Sitio', located in the state of Chihuahua, Mexico. After the corrections were applied to the images, they were evaluated in terms of their precision for biomass estimation. For that, biomass production was measured during the study period at the three PMS to calibrate production models developed with simple and multiple linear regression (SLR and MLR) techniques. When the estimations were made with MLR, DOS1 obtained an R 2 of 0.97 (p < 0.05) for 2012 and values greater than 0.70 (p < 0.05) during 2013-2014. The rest of the algorithms did not show significant results and DOS1, which is the simplest algorithm, resulted in the best biomass estimator. Thus, in the multitemporal analysis of grassland based on spectral information, it is not necessary to apply complex correction procedures. The maps of biomass production, elaborated from images corrected with DOS1, can be used as a reference point for the assessment of the grassland condition, as well as to determine the grazing capacity and thus the potential animal production in such ecosystems.

Retraction of invasiveSpartina alternifloraand its effect on the habitat loss of endangered migratory bird species and their decline in YNNR using remote sensing technology

“…First, we processed every image individually using radiometric calibration function to reduce errors associated with reflectance caused by atmospheric elements (water vapor, atmospheric dust, etc. ), which cause imprecisions in the satellite images (Prieto‐Amparan et al., 2018) and interpretability of data quality (Minarík et al, 2019; Beisl et al., 2010).…”

Evaluation of Atmospheric Correction Methods of Sentinel-2 for Monitoring Paddy Rice Growth in Cianjur and Klaten Regency

“…DOS is the most popular image-based atmospheric correction method and has very good accuracy compared to other image-based atmospheric correction methods [16]. This method is used as an alternative if atmospheric parameters are not available at the satellite acquisition date.…”

“…Included in the scene-based empirical approach method include Internal Average Reflectance (IAR), The flat field correction method, The empirical line method, and dark object subtraction (DOS) [15]. The most popular scene-based empirical approach method is DOS method [16]. To apply the scenebased empirical approach, the imaging system must be stable during the time of data recording [5].…”

Using Historical Archives and Landsat Imagery to Explore Changes in the Mangrove Cover of Peninsular Malaysia between 1853 and 2018

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