Yield prediction and waterlogging assessment for tea plantation land using satellite image‐based techniques

Abstract

Motivated by the operational use of remote sensing in various agricultural crop studies, this study evaluates the application and utility of remote sensing‐based techniques in yield prediction and waterlogging assessment of tea plantation land in the Assam State of India. The potential of widely used vegetation indices like NDVI and SR (simple ratio) and the recently proposed TVI has been evaluated for the prediction of green leaf tea yield and made tea yield based on image‐derived leaf area index (LAI), along with weather parameters. It was observed that the yield model based on the TVI showed the highest correlation (R² = 0.83) with green leaf tea yield. The NDVI‐ and SR‐based models suffered non‐responsiveness when the yield approached maximum. The NDVI and SR showed saturation when the LAI exceeded a magnitude of 4. However, the TVI responded well, even when the LAI exceeded 5, and thus has potential use in the estimation of the LAI of dense vegetation such as some crops and forest where it generally exceeds the threshold value of 4.

An attempt was made for the innovative application of TCT and NDWI in the mapping of waterlogging in tea plantation land. The NDWI in conjunction with TCT offered fairly good accuracy (87%) in the delineation of tea areas prone to waterlogging. This observation indicates the potential of NDWI and TCT in mapping waterlogged areas where the soil has considerable vegetation cover.

Acknowledgements

The authors wish to acknowledge the Rossell Tea Company Limited, India for providing yield data. Thanks are also due to the anonymous reviewers of this manuscript for their suggestions and useful comments.