Loris, V, Damiano, G (2006). Mapping the green herbage ratio of grasslands using both aerial and satellite-derived spectral reflectance. AGRICULTURE ECOSYSTEMS & ENVIRONMENT, 115(4-Jan), 141-149.

Green herbage ratio (GR), the equivalent of the biomass/(biomass + necromass), is an important biophysical parameter as it is a fundamental indicator of photosynthetic activity of vegetation components, pedoclimatic conditions and the phenological state of vegetation. GR is strongly correlated with photosynthesis and respiration rates, the major processes that drive ecosystem simulation models. To compare grasslands GR predictability from remote sensing data and in order to test the possibility of producing spatially distributed maps, the GR estimation technique has been tested with data produced from sensors on aerial and satellite platforms. For this purpose, ASPIS (Advanced SPectroscopic Imaging System) aircraft sensor and IRS satellite LISS-III sensor imagery of the Viote of Monte Bondone (Italian Alps) grassland area has been compared. Ten differently managed grasslands reflectance was measured in the field and calculated from aircraft and satellite platforms. From these data 10 different vegetation indices were calculated to estimate GR predictability from aircraft and satellite platforms. At the aircraft platform, a significant linear regression could be found between GR and the calculated indices; nine of the 10 investigated indices showed an R-2 > 0.70, all values being included within a small range of R-2. With aircraft data, Green-NDVI (normalized difference vegetation index calculated using NIR and green bands) was one of the most correlated indices, the R-2 value (0.74) being comparable to those found at ground level (R-2 = 0.80). For satellite-derived data, only Green-NDVI showed a significant correlation (R-2 = 0.63, p < 0.05). Green-NDVI aircraft and satellite-derived values correlated well with Green-NDVI ground values. According to these results, Green-NDVI was shown to be the only index that produced significant correlations with all the analyzed datasets. (c) 2006 Elsevier B.V. All rights reserved.