Filters for Forecasting Crop Health: Analyzing and Projecting the Temporal Evolution of Landsat NDVI Data using Dynamic Linear Models and the Kalman Filter

Accurately forecasting food availability is a critical task. One approach involves utilizing remote sensing data, such as satellite images, to observe the health of crop fields using different Vegetation Indices (VI). The Normalized Difference Vegetation Index (NDVI) provides a sound metric to track the “greenness” of crops over time. In this research, we develop statistical models that capture the dynamics of NDVI time series data to make better predictions of its future values. The median NDVI of the pixels of a farm located in Edmunds County, South Dakota, is obtained using imagery from the Landsat 5 and Landsat 8 satellites, which produce images every 16 days. Furthermore, we obtained weather data, such as drought intensity and average temperature, to predict how NDVI evolves over time in our corn/soybean field of interest. We employ dynamic linear models and the Kalman Filter to model the NDVI time series using weather data. Our findings show that the Kalman Filter forecasts NDVI with significant promise for our crop field of interest. Keywords: NDVI, Remote Sensing, Precision Agriculture, Dynamic Linear Models, Kalman Filter