Gérard Dedieu

TL;DR: In this article, the authors describe a computationally fast and accurate technique for the atmospheric correction of satellite measurements in the solar spectrum, which is based on a set of equations with coefficients which depend on the spectral band of the sensor.

TL;DR: In this paper, the authors used the remote sensing of crop growth to estimate continental net primary productivity (NPP) as well as its seasonal and spatial variations, assuming a decomposition of NPP into independent parameters such as incident solar radiation, radiation absorption efficiency by canopies, and conversion efficiency of absorbed radiation into organic dry matter.

TL;DR: This work aims at demonstrating the ability of state-of-the-art classifiers, such as Random Forests (RF) or Support Vector Machines (SVM), to classify HR-SITS, and selecting the best feature set used as input data in order to establish the classifier accuracy over large areas.

TL;DR: In this article, the authors proposed a method to derive the start, the maximum, the end, and the length of the vegetation cycle, based on the analysis of temporal series of weekly vegetation index, at a resolution of 1° lat × 1° long for year 1986.

TL;DR: Time series of images from FORMOSAT-2 and LANDSAT are used to develop and test a Multi-Temporal Cloud Detection (MTCD) method and results show that the MTCD method provides a better discrimination of clouded and unclouded pixels than the usual methods based on thresholds applied to reflectances or reflectance ratios.