Modeling temporal changes in surface spatial heterogeneity over an agricultural site

High temporal frequency remote sensing observations are required to monitor vegetation functioning. These observations are currently provided by moderate resolution sensor (with pixel size ranging from 250 m to 10 km). However, the intra-pixel spatial heterogeneity which may be important at moderate resolution, induces a scaling bias on non-linear estimation processes of land surface variable. A possible strategy to correct this scaling bias consists in using variogram model of high spatial resolution data (e.g. SPOT/HRV 20 m) as a proxy for the spatial heterogeneity within moderate resolution pixel. However, ways have to be found to get prior knowledge of this intra-pixel spatial heterogeneity metric without systematic concurrent high spatial resolution images. This paper aims at proposing a spatio-temporal model of the variogram of high spatial resolution data, which enables us to retrieve the spatial heterogeneity within moderate spatial resolution pixel using a temporal sampling of few high spatial resolution scenes. It capitalizes on variogram modeling of a time series of high spatial resolution NDVI images to quantify and model the temporal changes in landscape spatial heterogeneity over a particular crop site (Fundulea, Romania).We first demonstrate that important temporal variations in surface spatial heterogeneity observed over an agricultural site mainly result from the shift in seasonal trajectories between crop classes (here winter versus summer crops). The mean length scale as measured by the variogram integral range is mainly influenced by the gathering of fields with similar NDVI values. The scene overall spatial variability and the spatial heterogeneity within moderate resolution pixels, as quantified by the variogram sill and the dispersion variance, respectively, increase with the difference in NDVI between winter and summer crops. The influence of surface spatial heterogeneity on the description of land surface processes is thus critical when the phenological variability between crop classes is maximum, suggesting that the number of high spatial resolution scenes should increase at these periods of the seasonal cycle.Then, based on these observations, we build a model describing the temporal course of surface spatial heterogeneity, i.e. the temporal trajectory of the variogram of high spatial resolution NDVI image, as a function of crop seasonality. Once calibrated from a temporal sampling of few high spatial resolution scenes, this model proves to be powerful to predict the variogram at a date at which the high spatial resolution scene is not available and thus to retrieve the spatial heterogeneity within moderate resolution pixels through the seasonal cycle within a mean relative uncertainty of 20%.