Forecasting Cropland Vegetation Condition Using Digital Earth Africa Data Cube
- Creators
- Digital Earth Africa
Description
What is it about
Background
This notebook conducts time-series forecasting of vegetation condition (NDVI) using SARIMAX, a variation on autoregressive-moving-average (ARMA) models which includes an integrated (I) component to difference the time-series so it becomes stationary, a seasonal (S) component, and can consider exogenous (X) variables. In this example, we will conduct a forecast on a univariate NDVI time series. That is our forecast built on temporal patterns in NDVI. Conversely, multivariate approaches can account for influences of variables such as soil moisture and rainfall.
This notebook allows for easy replication of analysis across Africa, by simply changing the analysis parameters. The user guide provides an overview of the research, resources used/supplied, and directions on reproducing the research.
This knowledge package includes the necessary data, computational resources, and instructions to reproduce the methodology used in Digital Earth Africa's 'Forecasting cropland vegetation condition' Jupyter Notebook.
The User's Guide provides an overview of the application.
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Additional details
- Created
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2022-11-29