Antoine Denis

The additional value of hyperspectral data for smart farming

The spectral information contained in hyperspectral data allows for a more detailed crop and soil parameter retrieval, in turn making possible to derive precise information about plant health (nutrient deficiencies, water stress, crop diseases) but also about soil status (from soil moisture to humus content). Thus, smart farming, that is farming practice that wants to account for the whole ecosystem on the field and react precisely to any challenges, can be supported by hyperspectral data with exactly the spatial information it needs.

Monitoring the evolution of irrigated areas with Landsat images using backward and forward change detection analysis in the Kou watershed

The Kou watershed is characterized by important water resources used for drinking, agriculture (especially in the irrigated areas), industry and the preservation of aquatic fauna and flora. For several decades, there has been increasing pressure on the Kous water resources, partly because of the expansion of the irrigated agricultural areas. This study was conducted to examine this issue, focusing on one specific irrigated area. In order to monitor the expansion of irrigated areas in developing countries, a low-cost remote sensing method based on Landsat images and aerial photographs was developed. The method is based on maximum-likelihood classification, followed by backward and forward change detection analysis requiring agronomic expertise. Using pixel trajectory analysis, the method connects all pixels to their consecutive states in order to correct their current states. The study showed that the irrigated area has expanded by almost 70% over 20 years, with most of this expansion occurring in the past 10 years. The method, if validated, could be used to obtain better information on past occupation in the rural irrigated areas for which there is currently no archived data, making temporal analyses impossible.

Improving Soil Organic Carbon (SOC) prediction by field spectrometry in bare cropland by reducing the disturbing effect of soil roughness

The spatial estimation of Soil Organic Carbon (SOC) at large scale in outdoor condition is an important issue. It has been largely demonstrated that diffuse reflectance spectroscopic techniques, are efficient for SOC determination in field conditions. However these methods are influenced by disturbing factors such as soil water content, vegetation residues and surface roughness, the later being the object of this study. Our laboratory experiments showed that the accuracy of SOC prediction from shadowed soil samples with spectroscopy techniques decreases with increasing soil shadow. In this study a new methodology using a digital camera for identifying and correcting the effect of soil shadow on field reflectance spectra measured with an Analytical Spectral Devices (ASD) during field campaign in bare crop lands has been elaborated and tested. Results showed that the proposed shadow correction method enables improving significantly SOC prediction accuracy and performs better than traditionally used methods consisting in automatic signal processing.