Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage

Crop phenology provides important information for crop growth management and yield estimations. The popular shape model fitting (SMF) method detects crop phenology from vegetation index (VI) time-series data, but it has two limitations. First, SMF assumes the same “relative position” of phenological...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing of environment 2022-08, Vol.277, p.113060, Article 113060
Main Authors: Liu, Licong, Cao, Ruyin, Chen, Jin, Shen, Miaogen, Wang, Shuai, Zhou, Ji, He, Binbin
Format: Article
Language:English
Subjects:
Crop classification
Crop growth
Crop phenology
Crops
Curve fitting
Estimates
Iterative methods
Modelling
Phenology
Pixels
Shape model
Spatial variations
Temporal variations
Time series
Triticum aestivum
Vegetation
Vegetation index
Vegetation index time series
Wheat
Winter wheat
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Crop phenology provides important information for crop growth management and yield estimations. The popular shape model fitting (SMF) method detects crop phenology from vegetation index (VI) time-series data, but it has two limitations. First, SMF assumes the same “relative position” of phenological stages for the pixels of the same crop type. This assumption is valid only if all target pixels, relative to the shape model, display a synchronized increase (or decrease) in length between any two phenological stages, which is uncommon in practice. Second, the variance in the resulting phenology estimates for a particular phenological stage is related to the stage itself; this makes it challenging to simulate spatial and temporal variations in crop phenology using SMF. Here, we address both limitations by developing the shape model fitting by the Separate phenological stage method (“SMF-S"). SMF-S uses a modified fitting function and an iterative procedure to match the shape model with the VI time series for each phenological stage in an adaptive local window. Comparisons between SMF-S and SMF in simulation experiments show the superior performance of SMF-S in different scenarios, regardless of noise. Comparisons involving winter wheat field observations from the North China Plain showed that the RMSE values averaged over nine phenological stages were smaller for SMF-S (RMSE = 9.5 d) than for SMF (RMSE = 13.4 d) and one variant of SMF (the shape model with accumulated growing degree days (SM-AGDD); RMSE=33.6 d). Moreover, SMF-S better described the spatial variations (i.e., variance) in the results and captured the temporal shifts in multiple phenological stages. In the derived regional phenology maps of winter wheat on the North China Plain, SMF-S generated more reasonable spatial patterns, whereas SMF underestimated (overestimated) the variance in the early (late) phenological stages. We expect that the improved crop phenology estimates obtained with SMF-S could benefit various agricultural activities. •SMF-S is developed to detect crop phenology from vegetation index time-series data.•SMF-S fits each phenological stage by using modified function and adaptive windows.•SMF-S addresses two inherent limitations in SMF.•SMF-S performs better than SMF by testing on multisource phenology data.
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2022.113060