Machine learning and global vegetation: random forests for downscaling and gap filling

• Published in: Hydrology and earth system sciences 2024-06, Vol.28 (11), p.2357-2374
• Main Authors: van Jaarsveld, Barry, Hauswirth, Sandra M, Wanders, Niko
• Format: Article
• Language: English
• Subjects: Algorithms; Climate change; Drought; Droughts; Ecosystems; Energy balance; Environmental impact; Error analysis; Hydrologic studies; Land cover; Learning algorithms; Machine learning; Natural disasters; New products; Precipitation; Remote sensing; Satellites; Spatial discrimination; Spatial resolution; Temporal resolution; Vegetation; Vegetation dynamics; Vegetation index; Water shortages; Water-supply
• ISSN: 1027-5606; 1607-7938
• DOI: 10.5194/hess-28-2357-2024

Drought is a devastating natural disaster, during which water shortage often manifests itself in the health of vegetation. Unfortunately, it is difficult to obtain high-resolution vegetation drought impact information that is spatially and temporally consistent. While remotely sensed products can provide part of this information, they often suffer from data gaps and limitations with respect to their spatial or temporal resolution. A persistent feature among remote-sensing products is the trade-off between the spatial resolution and revisit time: high temporal resolution is met with coarse spatial resolution and vice versa. Machine learning methods have been successfully applied in a wide range of remote-sensing and hydrological studies. However, global applications to resolve drought impacts on vegetation dynamics still need to be made available, as there is significant potential for such a product to aid with improved drought impact monitoring. To this end, this study predicted global vegetation dynamics based on the enhanced vegetation index (evi) and the popular Random forest (RF) regressor algorithm at 0.1°. We assessed the applicability of RF as a gap-filling and downscaling tool to generate global evi estimates that are spatially and temporally consistent. To do this, we trained an RF regressor with 0.1° evi data, using a host of features indicative of the water and energy balances experienced by vegetation, and evaluated the performance of this new product. Next, to test whether the RF is robust in terms of spatial resolution, we downscale the global evi: the model trained on 0.1° data is used to predict evi at a 0.01° resolution. The results show that the RF can capture global evi dynamics at both a 0.1° resolution (RMSE: 0.02-0.4) and at a finer 0.01° resolution (RMSE: 0.04-0.6). Overall errors were higher in the downscaled 0.01° product compared with the 0.1° product. Nevertheless, relative increases remained small, demonstrating that RF can be used to create downscaled and temporally consistent evi products. Additional error analysis revealed that errors vary spatiotemporally, with underrepresented land cover types and periods of extreme vegetation conditions having the highest errors. Finally, this model is used to produce global, spatially continuous evi products at both a 0.1 and 0.01° spatial resolution for 2003-2013 at an 8 d frequency.