Crop Yield Estimation in the Canadian Prairies Using Terra/MODIS-Derived Crop Metrics

Citation

Liu, J., Huffman, T., Qian, B., Shang, J., Li, Q., Dong, T., Davidson, A., Jing, Q. (2020). Crop Yield Estimation in the Canadian Prairies Using Terra/MODIS-Derived Crop Metrics. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, [online] 13 2685-2697. http://dx.doi.org/10.1109/JSTARS.2020.2984158

Plain language summary

We evaluated the use of Terra/MODIS-derived crop metrics for yield estimation across the Canadian Prairies. This study was undertaken at the Census Agriculture Region (CAR) and the Rural Municipality (RM) of the province of Saskatchewan, in three prairie agro-climate zones. We compared MODIS-derived vegetation indices, the gross primary productivity (GPP) and the net primary productivity (NPP) to the known yields for barley, canola, and spring wheat. Results showed that vegetation indices at crop peak growing stages were better predictors of yield than GPP or NPP, and the enhanced vegetation index (EVI2) was better than the normalized vegetation index. Using seasonal maximum EVI2, CAR-level crop yields can be estimated with a relative root-mean-square-error (RRMSE) of 14-20% though the exact relationship varies by crop type and agro-climate zone. Cross validation showed the stability of the models across different years, although inter-annual fluctuations of estimation accuracy were observed. Assessments using RM-level yields showed slightly reduced accuracy. The best performing models were used to map annual crop yields at the Soil Landscapes of Canada (SLC) polygon level. The results indicated that the models could perform well at both spatial scales, and thus, could be used to disaggregate coarse resolution crop yields to finer spatial resolutions using MODIS data.

Abstract

We evaluated the utility of Terra/MODIS-derived crop metrics for yield estimation across the Canadian Prairies. This study was undertaken at the Census Agriculture Region (CAR) and the Rural Municipality (RM) of the province of Saskatchewan, in three prairie agro-climate zones. We compared MODIS-derived vegetation indices, gross primary productivity (GPP), and net primary productivity (NPP) to the known yields for barley, canola, and spring wheat. Multiple linear regressions were used to assess the relationships between the metrics and yield at the CAR and RM levels for the years 2000 to 2016. Models were evaluated using a leave-one-out cross validation (LOOCV) approach. Results showed that vegetation indices at crop peak growing stages were better predictors of yield than GPP or NPP, and EVI2 was better than NDVI. Using seasonal maximum EVI2, CAR-level crop yields can be estimated with a relative root-mean-square-error (RRMSE) of 14-20% and a Nash-Sutcliffe model efficiency coefficient (NSE) of 0.53-0.70, though the exact relationship varies by crop type and agro-climate zone. LOOCV showed the stability of the models across different years, although interannual fluctuations of estimation accuracy were observed. Assessments using RM-level yields showed slightly reduced accuracy, with NSE of 0.37-0.66, and RRMSE of 18-28%. The best performing models were used to map annual crop yields at the Soil Landscapes of Canada (SLC) polygon level. The results indicated that the models could perform well at both spatial scales, and thus, could be used to disaggregate coarse resolution crop yields to finer spatial resolutions using MODIS data.