Long-Term Probabilistic Forecast of Vegetation Conditions Using Climate Attributes in the Four Corners Region

TL;DR

This paper presents a novel two-phase machine learning approach to forecast one-year-ahead vegetation conditions using climate data, aiding agricultural planning in the Four Corners region.

Contribution

It introduces a new long-term NDVI forecasting model that combines climate attribute prediction with vegetation modeling, outperforming existing short-term methods.

Findings

Open-source tools outperform alternatives in NDVI forecasting

Accurate one-year-ahead vegetation predictions for regional planning

Effective use of climate attributes like precipitation and vapor pressure deficit

Abstract

Weather conditions can drastically alter the state of crops and rangelands, and in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate variables on regional and global scales. The annual peak Normalized Difference Vegetation Index (NDVI), derived from satellite observations, is closely associated with crop development, rangeland biomass, and vegetation growth. Although various machine learning methods have been developed to forecast NDVI over short time ranges, such as one-month-ahead predictions, long-term forecasting approaches, such as one-year-ahead predictions of vegetation conditions, are not yet available. To fill this gap, we develop a two-phase machine learning model to forecast the one-year-ahead peak NDVI over high-resolution grids, using the Four Corners region of the Southwestern United States as a testbed. In phase one, we identify informative climate attributes, including precipitation and maximum vapor pressure deficit, and develop the generalized parallel Gaussian process that captures the relationship between climate attributes and NDVI. In phase two, we forecast these climate attributes using historical data at least one year before the NDVI prediction month, which then serve as inputs to forecast the peak NDVI at each spatial grid. We developed open-source tools that outperform alternative methods for both gross NDVI and grid-based NDVI one-year forecasts, providing information that can help farmers and ranchers make actionable plans a year in advance.