Showing papers by "Allard de Wit published in 2010"

Exploring the potential of crop specific green area index time series to improve yield estimation at regional scale

Abstract: Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the growing season can potentially improve crop growth modelling and yield estimation. However, such approaches have proven difficult to apply on coarse resolution satellite data due to the fragmented land cover in many parts of the World. Advances in operational crop mapping will sooner or later allow the production of crop maps relatively early in the crop growth season, thereby providing an opportunity to sample pixels from medium/coarse spatial resolution data with relatively high cover fraction of a particular crop type to derive crop specific GAI time series. This research explores how to use such time series derived from MODIS to produce indicators of crop yield using two approaches over part of Belgium. The first method consists in looking at metrics of the decreasing part of the GAI curves when senescence occurs. Such metrics, like the position of the inflexion point, have been shown to be significantly correlated to yield. The second approach is to optimize the WOFOST model used in the European Crop Growth Monitoring System (CGMS) based on the GAI time series. Results show that, although the optimized model shows considerably better performance than the model running on the default parameter, the model is sometimes outperformed by the simpler metric approach. In all cases, indicators including remote sensing information provide better estimates that the average yield of previous years.

Combining remote sensing imagery of both fine and coarse spatial resolution to estimate crop evapotranspiration and quantifying its influence on crop growth monitoring

Abstract: (1) Institut Royal Meteorologique de Belgique (KMI-RMI), Bruxelles, Belgium (Guadalupe.SepulcreCanto@oma.be)., (2) Department of Environmental Sciences and Land Use Planning, Universite catholique de Louvain (UCL), Louvain-la-Neuve, Belgium., (3) The Flemish institute for technological research (VITO), MOL, Belgium., (4) Alterra, Wageningen-UR, The Netherlands., (5) Departament des sciencies de gestion de l’environnement, University of Liege, Arlon, Belgium.

Retrieving crop specific green area index from remote sensing data when the spatial resolution is close to the target field size

Abstract: Remote sensing observations can be used to estimate biophysical variables, such as the Green Area Index (GAI), which is a key variable in the photosynthetic processes of the canopy. For crop growth monitoring, high observation frequency is mandatory, especially when anomalies due to climatic variability must be detected. Wide geographic coverage is a further requisite to monitor specific crops at regional/continental scales. Nowadays, the instruments satisfying these requirements have coarse spatial resolutions for which crop specific GAI retrieval approaches have proven difficult to apply due to the fragmented land cover of many parts of the world. This paper demonstrates how it is possible to characterize the regional crop specific GAI dynamics using MODIS imagery by controlling the degree at which the observation footprints of the coarse pixels fall within the crop-specific mask delineating the target. This control is done by filtering out less reliable GAI estimations in both the spatial and temporal dimensions using thresholds on 3 proxy variables: pixel purity, observation coverage and view zenith angle. The discrepancies in results between using MODIS or SPOT/HRV 20m imagery to estimate the median GAI of winter wheat all along growing over a 40 by 40 km study region can be reduced to an RMSE of 0.055 by choosing adequate thresholds.