Forecasting monthly soil moisture at broad spatial scales in sub-Saharan Africa using three time-series models: evidence from four decades of remotely sensed data

ABSTRACT

Soil moisture is a critical environmental variable that determines primary productivity and contributes to climatic processes. It is, therefore, important to forecast soil moisture to inform expectations of derivative outputs reliably. While forecasting soil moisture continues to advance, there is a need to extend it to different geoclimatic regions, including in sub-Saharan Africa, where livelihoods predominantly rely on subsistence agriculture. We used remotely sensed soil moisture data produced by the European Space Agency – Climate Change Initiative (ESA CCI). The data, which covered the period 1978 to 2019, were used to forecast monthly soil moisture in different agroecological zones and land cover types. The Seasonal Random Walk, Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting models were trained on 70% of the data (November 1978 – August 2007) and subsequently applied to a test dataset (September 2007 – December 2019). All models showed solid prediction accuracies for all agroecological zones (unbiased root mean square error, ubRMSE ≤ 0.05 m³ m⁻³) and land cover types (ubRMSE ≤ 0.04 m³ m⁻³). This was corroborated by similarities in season-adjusted anomalies between observed and forecasted soil moisture for nearly all agroecological zones and land cover types, with a correlation coefficient of r > 0.5 for most locations). The broad-scale interpretation of soil moisture forecasting can inform moisture availability and variability by regions; however, more research is encouraged to improve forecasting at spatially and temporally detailed levels to assist small-scale farming practices in the continent.

KEYWORDS:

• Soil moisture forecasting
• remote sensing
• ESA CCI
• sub-saharan Africa
• agroecological zones
• land cover types

Acknowledgments

The University of Johannesburg (South Africa) provided the necessary financial and material support to undertake the study. We thank Adrian Snyman, Linux Systems Administrator, from Technology Architecture and Planning at the University of Johannesburg, on guiding as with cluster computation used to analyze our data. We would also like to thank the two anonymous reviewers whose comments resulted in the current improved version of the paper.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author contributions

Solomon G. Tesfamichael: Conceptualization and writing original draft; Yegnanew A. Shiferaw: Data analysis; Tsehaie Woldai: Supervision and editing.