Green Chlorophyll Index Research Articles

The potential of the satellite derived green chlorophyll index for estimating midday light use efficiency in maize, coniferous forest and grassland

Light use efficiency (LUE) is an important variable in carbon cycle and climate change research. We present an investigation of remotely estimating midday LUE using the green chlorophyll index (CI green) derived from the cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) images in maize, coniferous forest and grassland. Similar temporal patterns are observed in both canopy chlorophyll content and midday LUE which indicates that the chlorophyll content in the maize canopy servers as a proxy of midday LUE ( R 2 = 0.736, p < 0.001). Therefore, the CI green, tested as a good indicator of canopy chlorophyll content ( R 2 = 0.840, p < 0.001), has been demonstrated to be a reliable candidate in providing reasonable estimates of midday LUE with determination coefficient R 2 equals to 0.820 and a root mean square error (RMSE) of 0.002 mol CO 2 per mol incident photosynthetic photon flux density (PPFD). Further validation of the prediction model derived from the maize site demonstrates that the CI green has potential to be applied in the coniferous forest and grassland ecosystems with RMSE of 0.005 and 0.004 mol CO 2 mol −1 PPFD, respectively. A comparison analysis between different vegetation types is included and these results could be helpful in the development of future LUE and terrestrial models.

Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images

Remote sensing offers a nondestructive tool for the quick and precise estimation of canopy chlorophyll content that serves as an important indicator of the plant ecosystem. In this study, the canopy chlorophyll content of 26 samples in 2007 and 40 samples in 2008 of maize were nondestructively estimated by a set of vegetation indices (VIs; Normalized Difference Vegetation Index, NDVI; Green Chlorophyll Index, CIgreen; modified soil adjust vegetation index, MSAVI; and Enhanced Vegetation Index, EVI) derived from the hyperspectral Hyperion and Thematic Mapper (TM) images. The PROSPECT model was used for sensitivity analysis among the indices and results indicated that CIgreen had a large linear correlation with chlorophyll content ranging from 100–1000 mg m−2. EVI showed a moderate ability in avoiding saturation and reached a saturation of chlorophyll content above 600 mg m−2. Both of the other two indices, MSAVI and NDVI, showed a clear saturation at chlorophyll content of 400 mg m−2, which demonstrated they may be inappropriate for chlorophyll interpretation at high values. A validation study was also conducted with satellite observations (Hyperion and TM) and in-situ measurements of chlorophyll content in maize. Results indicated that canopy chlorophyll content can be remotely evaluated by VIs with r 2 ranging from the lowest of 0.73 for NDVI to the highest of 0.86 for CIgreen. EVI had a greater precision (r 2=0.81) than MASVI (r 2=0.75) in canopy chlorophyll content estimation. The results agreed well with the sensitivity study and will be helpful in developing future models for canopy chlorophyll evaluation.