Management zones (MZs) are efficient for dealing
with spatial variability in agricultural fields and applying site-specific
management (SSM). This study proposes an approach for generating MZs using
time-series clustering (TSC) to enable SSM and time-specific management (TSM).
TSC was applied to time series of remote sensing images in a California
vineyard during four growing seasons (2015-2018) using three datasets:
evapotranspiration (ET), leaf area index (LAI), and the normalized difference
vegetation index (NDVI). MZs maps were generated for each dataset after
computing the optimal number of clusters. These maps and the corresponding
temporal dynamics characterizing each cluster (MZ) were quantitatively
compared. An aggregated MZs map was generated using multivariate clustering.
Additionally, time-series decomposition was used to decompose the ET time
series of each pixel into three components: long-term trend, seasonality, and
remainder. For each time-series component, TSC was applied and MZs maps were generated.
The different ET-components MZs maps were compared for spatial similarities.
Each ET-component MZs map was also analyzed against six environmental variables
(elevation, slope, northness, lithology, topographic wetness index, and soil
type) to check for the spatial association between the MZs maps and the local
characteristics in the vineyard. Finally, the ET-components maps were used as
independent variables against yield (ton ha-1) using analysis of variance
(ANOVA) to assess their relations to yield variability. The findings show that
the optimal number of clusters was two and that LAI TSC outperformed LAI and
NDVI. The MZs maps of NDVI and LAI were nearly identical, while ET showed
weaker similarities to NDVI and LAI. The aggregated MZs map was composed of ET
and NDVI, with spatial patterns visually similar to a 2016 yield map. For the
ET-components MZs maps, the trend and seasonality had a moderate spatial
association and were statistically different from the remainder. For the trend
and seasonality spatial association models, the most important predictor was
soil type, followed by elevation. The ET-remainder MZs map was strongly linked
with northness spatial variability. The yield levels corresponding to the two
clusters in all TSC were significantly different. These findings enabled
spatial quantification of ET time series at different temporal scales that may
benefit within-season decision-making regarding the amounts, timing, intervals,
and irrigation location. With the increasing availability of higher spatial and
temporal resolution satellite imagery, TSC may be further utilized for defining
within-field spatial variability and temporal dynamics of various
characteristics for SSM and TSM.