  Droughts are a recurring hazard in sub-Saharan Africa, that can wreak huge
socioeconomic costs.Acting early based on alerts provided by early warning
systems (EWS) can potentially provide substantial mitigation, reducing the
financial and human cost. However, existing EWS tend only to monitor current,
rather than forecast future, environmental and socioeconomic indicators of
drought, and hence are not always sufficiently timely to be effective in
practice. Here we present a novel method for forecasting satellite-based
indicators of vegetation condition. Specifically, we focused on the 3-month
Vegetation Condition Index (VCI3M) over pastoral livelihood zones in Kenya,
which is the indicator used by the Kenyan National Drought Management
Authority(NDMA). Using data from MODIS and Landsat, we apply linear
autoregression and Gaussian process modeling methods and demonstrate high
forecasting skill several weeks ahead. As a benchmark we predicted the drought
alert marker used by NDMA (VCI3M<35). Both of our models were able to predict
this alert marker four weeks ahead with a hit rate of around 89% and a false
alarm rate of around 4%, or 81% and 6% respectively six weeks ahead. The
methods developed here can thus identify a deteriorating vegetation condition
well and sufficiently in advance to help disaster risk managers act early to
support vulnerable communities and limit the impact of a drought hazard.
