Determining the spatial and temporal dynamics of the green vegetation fraction of croplands using high-resolution RapidEye satellite images

Publikations-Art

Zeitschriftenbeitrag (peer-reviewed)

Autoren

Imukova, K., Ingwersen, J., Streck, T.

Erscheinungsjahr

2015

Veröffentlicht in

Agricultural and Forest Meteorology

Band/Volume

206/

DOI

10.1016/j.agrformet.2015.03.003

Seite (von - bis)

113-123

The green vegetation fraction (GVF) is a key input variable of the evapotranspiration scheme applied in the widely used NOAH-MP land surface model (LSM). In standard applications of the NOAH-MP, the GVF is taken from a global map with a 15 km×15 km resolution. The central objective of the present study was (a) to derive gridded GVF data of a region in Southwest Germany in a high spatial resolution (5 m×5 m) from RapidEye satellite images, and (b) to improve the representation of the GVF dynamics of croplands in the NOAH-MP for a more accurate simulation of water and energy exchange between land surface and atmosphere. The GVF dynamics were determined based on the normalized difference vegetation index (NDVI) calculated from the red and near-infrared bands of the satellite images. The satellite GVF data were calibrated and validated against ground truth measurements. Based on the obtained calibration scheme, GVF maps were derived in a monthly resolution for the region. Our results confirm a linear relationship between GVF and NDVI and demonstrate that it is possible to determine the GVF of croplands from RapidEye images based on a simple two end-member mixing model. Our data highlight the high variability of the GVF in time and space. At the field scale, variability was mainly caused by soil heterogeneities and management differences. At the regional scale, the GVF showed a bimodal distribution formed by the different phenology of crops. We suggest to divide croplands according to their distinctly different temporal dynamics of the GVF into “early-covering” (winter rape, winter wheat, spring barley) and “late-covering” crops (sugar beet, silage maize). Based on our results, we recommend that simulations with LSM should take into account this differentiation of croplands, since it is to be expected that these two crop groups produce pronounced differences in energy partitioning at the land surface.