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Empirical combination of Landsat 7 and 8 imagery to detect the phenological changes in rainfed cropland vegetation
Conference paper

Empirical combination of Landsat 7 and 8 imagery to detect the phenological changes in rainfed cropland vegetation

J. Shen and F.H. Evans
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (Waikoloa, HI, USA, 26/09/2020–02/10/2020)
2020
DOI: https://doi.org/10.1109/IGARSS39084.2020.9323444
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Abstract

Seasonal rainfall is the dominant driver of grain yield in Australia where grain crops are grown in rainfed, broadacre farming systems. Effective monitoring of within-farm scale management and crop response is of great importance to grain growers in Australia. Landsat data have the potential to bridge the gap between broadacre cropping and precision agriculture. However, the images acquired by different Landsat sensors are subject to the conditions of the sensors and their onboard satellite platforms. This study empirically combined the time series of images from both Landsat 7 ETM+ and 8 OLI to detect the changes in winter wheat phenology at a typical farm paddock in Western Australia. Results showed that: 1) the combined method for incorporating Landsat 7 and 8 imagery improves the data frequency, in terms of higher temporal resolution and less missing values; 2) phenological detections based on the combined dataset are more realistic than those based on either of the two series of data separately. This study provides a better understanding of the usefulness of Landsat imagery for identifying and mapping crop phenology at within-farm scale.

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