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TitleCrop yield estimation in the Canadian Prairies using Terra/MODIS-derived crop metrics
AuthorLiu, J; Huffman, T; Qian, B; Shang, J; Li, Q; Dong, T; Davidson, A; Jing, Q
SourceIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing vol. 13, 9103945, 2020 p. 2685-2697, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20200128
PublisherInstitute of Electrical and Electronics Engineers
Mediapaper; on-line; digital
File formatpdf; html
ProvinceAlberta; Manitoba; Saskatchewan
NTS52; 53; 54; 62; 63; 64; 82; 83; 84
Lat/Long WENS-120.0000 -93.1983 60.0000 49.0000
SubjectsAgriculture; geophysics; Science and Technology; remote sensing; satellite imagery; vegetation; statistical analyses; models; mapping techniques; Prairies; MODIS; Crops
Illustrationssketch maps; plots; bar graphs; tables; time series
Released2020 05 29
AbstractWe 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.

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