0000000000790776
AUTHOR
Juan Carlos Jimenez
MODIS probabilistic cloud masking over the Amazonian evergreen tropical forests: a comparison of machine learning-based methods
Amazonian tropical forests play a significant role in global water, carbon and energy cycles. Satellite remote sensing is presented as a feasible means in order to monitor these forests. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) is amongst major tools for studying this region. Nevertheless, MODIS operative surface variable retrieval was reported to be impacted by cloud contamination effects. A proper cloud masking is a major consideration in order to ensure accuracy when analysing Amazonian tropical forests current and future status. In the present study, the potential of supervised machine learning algorithms in order to overcome this issue is evaluated. In f…
Recent extreme drought events in the Amazon rainforest: assessment of different precipitation and evapotranspiration datasets and drought indicators
Over the last decades, the Amazon rainforest has been hit by multiple severe drought events. Here, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon region and their impacts on the regional carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (MCWD). Evaluating nine state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2.2 to 3.0 (mean =2.7) ×106 km2 (37 %–51 % of the Amazon basin, mean =45 %), where MCWD indicates at least moderate drought conditions (relative MCWD anomaly <-0…