Using remotely sensed and ancillary data to predict spatial variability of rainfed crop yield

Abstract

Rainfed agriculture is dominant in Sudan. The current methods for crop yield estimation are based on taking random cutting samples during harvesting time. This is ineffective in terms of cost of information and time. The general objective of this study is to highlight the potential role of remote-sensing techniques in upgrading methods of monitoring rainfed agricultural performance. The specific objective is to develop a relationship between satellite-derived crop data and yield of rainfed sorghum. The normalized difference vegetation index (NDVI), rainfall, air temperature (AT) and soil moisture (SM) are used as independent variables and yield as a dependent variable. To determine the uncertainty associated with the independent variables, a sensitivity analysis (SA) is conducted. Multiple models are developed using different combinations of data sets. The temporal images taken during sorghum’s mid-season growth stage give a better prediction than those taken during its development growth stage. Among predictor variables, SM is associated with the highest uncertainty.

Acknowledgements

This study is part of a PhD study funded by DAAD. The authors would like to acknowledge the ITT, Germany, for hosting the first author in Germany, with special thanks to Prof. Gease (the dean of the institute). They also appreciate the role of Prof. Adam H. Suliman, Prof. Seifeddin A. Hamad, Prof. Adam Ibrahim and Mr Ammar M. Ahmed for their valuable advice. The authors are thankful to the anonymous reviewers, whose comments improved the quality of this article.