Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity

Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH's flawed window-based spatial analysis and underutilized temporal component. In particular, In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patte..., We calculated the spectral diversity and its spatial, temporal and spatio-temporal components from multi-temporal Sentinel-2 data in three grasslands utilized for agricultural production, two in Europe and one in the United States, using the function divcom from the stdiversity v1.1.0 package (Rossi et al., 2021). For the same parcels, we collected in situ species inventories and used species richness to express the plant diversity of each parcel. A detailetd description of data collection and processing, as well as the method used, can be found in Rossi et al. (2024).
 , , # Spectral and species diversity on a parcel level

## Description of the file structure

species_spectral_diversity.csv

includes the spectral richness per parcel and the calculated spectral diversity components including the temporal, spatial and spatio-temporal components for three different grassland sites in Switzerland, Netherlands and Oklahoma (United States).

* *ID*: The ID of the parcel
* *Species_richness*: The species richness per parcel corresponded to the total count of observed plant species within all the given parcel plots. For each study area, a different sampling design was used.
* *Parcel_size:* The size in m2 of the parcel after negative buffering of 50 m for Oklahoma and 10 m in the other locations.
* *Study_area:* The state where the field and remote sensing data were collected.
* *Lat_centroid: Latitude of the parcel centroid (EPSG:4326)*
* *Lon_centroid: Longitude of the parcel centroid (EPSG:4326)*
* *Sampling_year*: The year of in-situ and remote sensing data ...