Evaluation and Calibration of Vegetation Index Time Series Measurements for Multisensor ESDR/CDR
Alfredo
R
Huete, University of Arizona, ahuete@cals.arizona.edu
(Presenting)
Kamel
Didan, University of Arizona, kamel@ag.arizona.edu
Zhangyan
Jiang, University of Arizona, zjiang@email.arizona.edu
Tomoaki
Miura, University of Hawaii, tomoakim@hawaii.edu
Vegetation indices are among the most widely used satellite data products providing key measurements of vegetation parameters and biophysical processes. Compared with other land products and due to their simplicity, VIs are more readily fused across sensor systems facilitating an underlying need to ensure continuity of critical data sets to study climate-related processes. The recent availability of continuous flux tower measures of photosynthesis has provided opportunities for testing and evaluating the performance of VIs at inter- and intra-annual time scales and across sensor systems. An accurate depiction of seasonal vegetation dynamics is a desired prerequisite for ecosystem models and provides confidence in model capabilities to predict inter-annual vegetation responses to climate variability. In this project we aim to extend the advanced capabilities of the Terra and Aqua MODIS VI data record into the NPOESS Preparatory Project (NPP) at moderate resolution (<1 km) and evaluate VI measurement suitability as Earth System and Climate Data Records (ESDR/ CDR) through in-situ coupling and analysis with flux tower data under a wide array of climate and vegetation conditions. Specifically, we aim to (1) assess key determinants in the translation and extension of MODIS VI products across current sensor systems and to the NPOESS (VIIRS) era and (2) evaluate calibration of satellite VI time series with in-situ flux tower time series measurements of photosynthesis. This includes the assessment and optimization of the spatial and temporal scales needed to capture the important spatial and temporal complexities associated with vegetation dynamics, biogeochemical and hydrologic processes, and for quantifying ecosystem responses to climate variability.
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