High-Temporal-Resolution Remote Sensing of Vegetation and Interactions with Air Quality

We present new methods for quantifying daily and sub-daily vegetation characteristics that are critical to accurate agricultural management and understanding air quality from current and future satellite instruments. Due to factors such as cloud contamination and dependence on viewing geometry, current measurements of vegetation from space are limited to 8-day and 16-day composites that cannot capture crop behavior at shorter time-scales. Our new method uses daily shortwave-infrared and visible red measurements from MODIS to clear clouds more effectively: resulting daily vegetation index time-series are smooth and show clear differences in timing with time-series constructed from the 16-day composites. We also account for errors introduced by surface reflectance anisotropy. As a case study, we use these data to resolve the interannual shifts of the summer and winter growing seasons over the Indo-Gangetic Plain of India, one of the most agriculturally productive regions in the world.

The importance of moving from 16-day to daily observations of vegetation motivates investigation of potential gains from future sub-daily measurements. TEMPO (Tropospheric Emissions: Monitoring of Pollution) will provided hourly visible measurements at very high spectral resolution (0.6 nm). We are developing new indices for vegetation health that exploit the capability to resolve spectral features and will show comparisons to currently used indices. To do this we use both existing remotely sensed measurements in the visible (e.g. from GOME-2) and simulated TEMPO measurements. As the TEMPO mission will be targeted primarily at air quality, we will present simulations to estimate the capability of TEMPO to quantify the interactions of vegetation growth and air quality.