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

2026-01-22

AI Summary

This study addresses the challenge of accurately forecasting vegetation conditions one year in advance—a critical gap that hinders proactive agricultural and rangeland management. We propose a novel two-stage machine learning framework that, for the first time, enables high-resolution, probabilistic predictions of peak annual NDVI across the Four Corners region of the southwestern United States using climate predictors such as precipitation and maximum vapor pressure deficit. Our approach integrates a generalized co-kriging Gaussian process model to capture climate–NDVI relationships while preserving spatial dependence and interpretability, followed by a cascaded forecasting step leveraging historical climate data. The resulting open-source tool significantly outperforms existing methods at both regional and grid scales, offering reliable support for agricultural decision-making with a full year of lead time.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Machine learning and data science for the Web

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.

Problem

• long-term forecasting
• vegetation conditions
• NDVI
• climate attributes
• probabilistic forecast

Innovation

• long-term forecasting
• two-phase machine learning
• Gaussian process
• NDVI prediction
• climate attributes