The approach that we took used a ten-year time series of optical images from around the flowering stage of the wheat crop from sensors on the Landsat series of satellites. The Green Normalised Difference Vegetation Index (GNDVI) was calculated for each pixel location and year. The GNDVI is closely related to the photo synthetically absorbed radiation and has shown a linear correlation with the Leaf Area Index (LAI) and biomass in other studies. The spatial and temporal variability in the GNDVI were quantified across the village.
Soil brightness imagery, which is an indicator of soil texture, organic matter and soil moisture, was also obtained from a commercial source for testing as an additional variable for defining management zones. Sampling data for soil inorganic nitrogen (N) and organic carbon (OC) were also available for validation purposes.
A clustering algorithm, known as partitioning around medoids, was used to group fields into three or four clusters, or management zones. The clustering algorithm aims to minimise the amount of variation (in the clustering variables) within clusters and maximise the amount of variation between clusters. The accuracy of this approach for delineating management zones was evaluated by calculating the relative variance of the measured soil N and OC. Soil brightness alone was a fairly poor predictor of measured N and OC in this study, explaining only up to 9% of the variability. The GNDVI variability metrics were a reasonable predictor (up to 39%) of variability in measured N and OC. The three-zone solution in the combined model (soil brightness + GNDVI) was the best predictor of N and OC variability at up to 45%.
The results of this study can be considered as a preliminary method based on the integration of different remotely sensed data to delineate management zones at the village scale. More studies are needed to further refine them for guiding site-specific management in small scale farming systems. In addition, incorporating measurements of field level yields would aid in validating this approach in the future.
This project was funded by the Agri Tech in China Newton Network+ (ATCNN) and Norwegian Ministry of Foreign Affairs (SINOGRAIN II, CHN-17/0019).
Find out more
If you would like to find out more about our remote sensing offer and our recent research in this area, please contact Ben.Hockridge@adas.co.uk. You can also download some further reading here.