New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10-15min. Our analysis of NDVI data collected over the Amazon during 2018-19 shows that ABI provides 21-35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites. Cloud cover and scarcity of ground-based validation hinder remote sensing of forest dynamics in the Amazon basin. Here, the authors analyse imagery from a high-frequency geostationary satellite sensor to study monthly NDVI patterns in the Amazon forest, finding support for spatially extensive seasonality.

Automated summary

The study demonstrates that using a new generation of geostationary satellite sensors for studying seasonal patterns of the Amazon forest is more effective than traditional polar orbiting satellites, providing more cloud-free observations and significant findings on the seasonality changes in the Amazon forest.