It employs a farm field polygon-based ground reference dataset that is unprecedented in spatial and temporal coverage for the state, consisting of 2003 annual field site samples representing 415 unique field sites and five crop years (2005 2009).
The spatial allocation approach, which was used to match field polygons and the phenological metrics pixel grid, led to a total of 198 sample pixels in PESA and 207 in PETR to be used as input for the carbon models.
Following the method of Rull and Ritz (2003), we spatially refined these data through the overlay of matched land-use survey field polygons provided by the California Department of Water Resources (DWR 2005).
The software uses an empirical approach to allocate crops to fields (polygons in vector format) over a sequence of years, using a stochastic process (Markov chains) and rule-based constraints.
Topographic covariates (elevation and slope) were not ground truthed; however, vegetation attributes were ground-truthed based on field-established polygons (n = 1177) of known vegetation type.
Indirect approaches include WIV estimation from coastal or riverine bluffs [ Kanevskiy et al., 2011], or remote-sensing and field survey estimations of polygon sizes combined with WI width data.
For raster-based maps, the plots located within each pixel were area-weighted averaged on the basis of the NBS LC map (i.e. weighting the plots according to the fraction of the corresponding LC polygon area within each pixel) using only the pixels where the field plots represented, through LC polygons, at least 90% of the pixel area.
Distances between point locations, field boundaries, and shared Voronoi polygon edges were calculated using the ArcGIS 'Generate Near Table' tool.
‖S p ‖ is a polygon area.
Attribute information are collected (land use type, vegetation details) for these patches (training polygons) from the field.
