Pascal Keravec

Spatialization of crop leaf area index and biomass by combining a simple crop model SAFY and high spatial and temporal resolutions remote sensing data

The recent availability of high spatial resolution sensors offers new perspectives for terrestrial applications (agriculture, risks). The aim of this work is to develop a methodology for deriving biophysical variables (Green Leaf Area Index — GLAI, phytomass) from multi-temporal observations at high spatial resolution in order to run a crop model at a regional scale. Accurate predictive crop models require a large set of input parameters, which are not easily available over large area. Spatial upscaling of such models is thus difficult. The use of simple model avoids spatial upscaling issues. This study is focused on SAFY model (Simple Algorithm For Yield estimates) developed by [1]. Key SAFY parameters were calibrated using temporal GLAI profiles, empirically estimated from FORMOSAT-2 time series of images. Most of the SAFY parameters are crop related and have been fixed according to literature. However some parameters are more specific and have been calibrated based on GLAI derived from FORMOSAT-2 observations at a field scale. Two calibration strategies are evaluated as a function of sampling (frequency and temporal distribution) of remote sensing data. Spatial upscaling simulations are assessed based on biomass in-situ measurements taken over maize. Good agreement between modelled and measured phytomass have been found on maize (RMSE =3D 20 g.m−2).

Integrated method for cities air temperature estimation

Urban Heat Island (UHI) is defined as the air temperature difference between the city and its surrounding areas. This phenomenon varies spatially (depending on the type of urban fabric constituting each neighborhood) and temporally (depending on the time of the day, on the season and on the weather conditions). This contribution proposes a methodology to model the UHI spatially and temporally using simple models built with free and open sources softwares (orbisGIS and python language). Ten air temperature sensors have been implemented in several neighborhoods of the Nantes urban area (a west coast french conurbation). The difference of UHI is observed and modeled for each of those sites. Spatial differences are modeled according to geographical indicators characterizing the urban surroundings of each temperature station. Temporal variations are modeled according to weather conditions (such as wind speed, solar radiations, etc.) for different time scales : diurnal and nocturnal differences, daily variations and seasonal variations. The objective is to create a method which may be applied for any city in France. Geographical indicators are then calculated with OrbisGIS software from geographical data which are homogeneous and available at the french territory scale. Wheather conditions are recorded by MeteoFrance stations, which follow the same standard for the measurement of climatic parameters all around France. Climatic data analysis and modeling are performed with Python language using libraries such as Pandas and StatsModels. Modeled established according to the Nantes temperature dataset are verificated according to new air temperature networks implemented in the city of Nantes as well as other cities of west France (Angers, La Roche-sur-Yon).