Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy

Information on vegetation status can be retrieved from satellite observations by modelling and inverting canopy radiative transfer. Agricultural monitoring and yield forecasting could greatly benefit from such techniques by coupling crop growth models with crop specific information through data assimilation. An indicator which would be particularly interesting to obtain from remote sensing is the total surface of photosynthetically active plant tissue, or green area index (GAI). Currently, the major limitation is that the imagery that can be used operationally and economically over large areas with high temporal frequency has a coarse spatial resolution. This paper demonstrates how it is possible to characterise the regional crop specific GAI range along with its temporal dynamic 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 modelling the instrument's point spread function and by filtering out less reliable GAI estimations in both the spatial and temporal dimensions using thresholds on 3 variables: pixel purity, observation coverage and view zenith angle. The difference in performance between MODIS and fine spatial resolution to estimate the median GAI of a given crop over a 40 × 40 km study region can be reduced to a RMSE of 0.053 m2/m2. The consistency between fine and coarse spatial resolution GAI estimations suggests a possible instrument synergy whereby the high temporal resolution of MODIS provides the general GAI trajectory and while high spatial resolution can be used to estimate the local GAI spatial heterogeneity.

Research highlights
► Point spread function model is used to calculate MODIS crop specific pixel purity.
► Pixel-target adequacy controlled using VZA, observation coverage and pixel purity.
► Regional GAI spatial range and temporal dynamic is grasped with MODIS.
► Temporally rich MODIS GAI can be used to correct high spatial resolution GAI bias.
► Consistency in GAI retrieval algorithm opens possibility for instrument synergy.