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East Africa has experienced a number of devastating droughts in recent decades, including the 2010/2011 drought. The National Drought Management Authority in Kenya relies on real-time information from MODIS satellites to monitor and respond to emerging drought conditions in the arid and semi-arid lands of Kenya. Providing accurate and timely information on vegetation conditions and health—and its probable near-term future evolution—is essential for minimising the risk of drought conditions evolving into disasters as the country’s herders directly rely on the conditions of grasslands. Methods from the field of machine learning are increasingly being used in hydrology, meteorology, and climatology. One particular method that has shown promise for rainfall-runoff modelling is the Long Short Term Memory (LSTM) network. In this study, we seek to test two LSTM architectures for vegetation health forecasting. We find that these models provide sufficiently accurate forecasts to be useful for drought monitoring and forecasting purposes, showing competitive performances with lower resolution ensemble methods and improved performances over a shallow neural network and a persistence baseline.

Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a natural approach is to use transfer learning from data-rich regions. While standard transfer learning approaches typically leverage only direct inputs and outputs, geospatial imagery and agricultural data are rich in metadata that can inform transfer learning algorithms, such as the spatial coordinates of datapoints or the class of task being learned. We build on previous work exploring the use of metalearning for agricultural contexts in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic metalearning which takes advantage of task-specific metadata. We apply TIML to crop type classification and yield estimation, and find that TIML significantly improves performance compared to a range of benchmarks in both contexts, across a diversity of model architectures. While we focus on tasks from agriculture, TIML could offer benefits to any meta-learning setup with task-specific metadata, such as classification of geo-tagged images and species distribution modelling.

<p>Tools from the field of deep learning are being used more widely in hydrological science. The potential of these methods lies in the ability to generate interpretable and physically realistic forecasts directly from data, by utilising specific neural network architectures. </p><p>This approach offers two advantages which complement physically-based models. First, the interpretations can be checked against our physical understanding to ensure that where deep learning models produce accurate forecasts they do so for physically-defensible reasons. Second, in domains where our physical understanding is limited, data-driven methods offer an opportunity to direct attention towards physical explanations that are consistent with data. Both are important in demonstrating the utility of deep learning as a tool in hydrological science.</p><p>This work uses an Entity Aware LSTM (EALSTM; cf. Kratzert et al., 2019) to predict a satellite-derived vegetation health metric, the Vegetation Condition Index (VCI). We use a variety of data sources including reanalysis data (ERA-5), satellite products (NOAA Vegetation Condition Index) and blended products (CHIRPS precipitation). The fundamental approach is to determine how well we can forecast vegetation health from hydro-meteorological variables. </p><p>In order to demonstrate the value of this method we undertook a series of experiments using observed data from Kenya to evaluate model performance. Kenya has experienced a number of devastating droughts in recent decades. Since the 1970s there have been more than 10 drought events in Kenya, including droughts in 2010-2011 and 2016 (Haile et al 2019). The National Drought Monitoring Authority (NDMA) use satellite-derived vegetation health to determine the drought status of regions in Kenya.</p><p>First, we compared our results to other statistical methods and a persistence-based baseline. Using RMSE and R-squared we demonstrate that the EALSTM is able to predict vegetation health with an improved accuracy compared with other approaches. We have also assessed the ability of the EALSTM to predict poor vegetation health conditions. While better than the persistence baseline the performance on the tails of the distribution requires further attention.</p><p>Second, we test the ability of our model to generalise results. We do this by training only with subsets of the data. This tests our model’s ability to make accurate forecasts when the model has not seen examples of the conditions we are predicting. Finally, we explore how we can use the EALSTM to better understand the physical realism of relations between hydro-climatic variables embedded within the trained neural network. </p><p> </p><p>References:</p><p>Gebremeskel, G., Tang, Q., Sun, S., Huang, Z., Zhang, X., & Liu, X. (2019, June 1). Droughts in East Africa: Causes, impacts and resilience. Earth-Science Reviews. Elsevier B.V. https://doi.org/10.1016/j.earscirev.2019.04.015</p><p>Klisch, A., & Atzberger, C. (2016). Operational drought monitoring in Kenya using MODIS NDVI time series. Remote Sensing, 8(4). https://doi.org/10.3390/rs8040267</p><p>Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., & Nearing, G. (2019). Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences, 23(12), 5089–5110. https://doi.org/10.5194/hess-23-5089-2019</p><p>Github Repository: https://github.com/esowc/ml_drought</p>

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