Uncertainty of soil reflectance retrieval from SPOT and RapidEye multispectral satellite images using a per-pixel bootstrapped empirical line atmospheric correction over an agricultural region

Abstract Many authors have reported the use of empirical line regression between field target sites and image pixels in order to perform atmospheric correction of multispectral images. However few studies were dedicated to the specific reflectance retrieval for cultivated bare soils from multispectral satellite images, from a large number (≥15) of bare field targets spread over a region. Even fewer were oriented towards additional field targets for validation and uncertainty assessment of reflectance error. This study aimed at assessing ELM validation accuracy and uncertainty for predicting topsoil reflectance over a wide area (221 km 2 ) with contrasting soils and tillage practices using a set of six multispectral images at very high (supermode SPOT5, 2.5 m), high (RapidEye, 6.5 m) and medium (SPOT4, 20 m) spatial resolutions. For each image and each spectral band, linear regression (LR) models were constructed through a series of 1000 bootstrap datasets of training/validation samples generated amongst a total of about 30 field sites used as targets, the reflectance measurements of which were made between −6 days/+7 days around acquisition date. The achieved models had an average coefficient of variation of validation errors of ∼14%, which indicates that the composition of training field sites does influence performance results of ELM. However, according to median LR-models, our approach mostly resulted in accurate predictions with low standard errors of estimation around 1–2% reflectance, validation errors of 2–3% reflectance, low validation bias (  |20°|). The predictions obtained from median LR-models through per-pixel bootstrapped ELM approach were as accurate as the ATCOR2 predictions with default parameters for the RapidEye image and were slightly more accurate and less biased for the SPOT4 images.