A spatiotemporal shape model fitting method for within-season crop phenology detection

Crop phenological information must be reliably acquired earlier in the growing season to benefit agricultural management. Although the popular shape model fitting (SMF) method and its various improved versions (e.g., SMF by the Separate phenological stage, SMF-S) have been successfully applied to after-season crop phenology detection, these existing methods cannot be applied to within-season crop phenology detection. This discrepancy arises due to the fact that, in the within-season scenario, phenological stages can beyond the defined cut-off time. Consequently, enhancing the alignment of the vegetation index (VI) curve segments prior to the cut-off time does not necessarily guarantee accurate within-season phenological detection. To resolve this issue, a new method named spatiotemporal shape model fitting (STSMF) was developed. STSMF does not seek to optimize the local curve matching between the target pixel and the shape model; instead, it determines similar local VI trajectories in the neighboring pixels of previous years. The within-season phenology of the target pixel was thus estimated from the corresponding phenological stage of the determined local VI trajectories. When compared with ground phenology observations, STSMF outperformed the existing SMF and SMF-S which were modified for the within-season scenario (SMFws and SMFSws) with the smallest mean absolute differences (MAE) between observed phenological stages and their corresponding model estimates. The MAE values averaged over all phenological stages for STSMF, SMFSws, and SMFws were 9.8, 12.4, and 27.1 days at winter wheat stations; 8.4, 14.9, and 55.3 days at corn stations; and 7.9, 12.4, and 64.6 days at soybean stations, respectively. Intercomparisons between after-season and within-season regional phenology maps also demonstrated the superior performance of STSMF (e.g., correlation coefficients for STSMF and SMFS(ws)are 0.89 and 0.80 at the maturity stage of winter wheat). Furthermore, the performance of STSMF was less affected by the detection time and the determination of shape models. In conclusion, the straightforward, effective, and stable nature of STSMF makes it suitable for within-season detection of agronomic phenological stages.