Continuous observation of vegetation canopy dynamics using an integrated low-cost, near-surface remote sensing system.

Graphical abstract Highlights • Developed a system that monitors VIs, fPAR, and LAI automatically. • Displayed linear responses to light intensities in the lab and rice field. • Agreed to a reference spectrometer and LAI-2200. • Tracked green LAI by integrating LED and micro-camera. Abstract Continuous monitoring of vegetation indices (VIs) the fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) through satellite remote sensing has advanced our understanding of biosphere–atmosphere interactions. Substantial efforts have been put into monitoring individual variables in the field, but options to concurrently monitor VIs, fPAR, and LAI in-situ have been lacking. In this paper, we present the Smart Surface Sensing System (4S), which automatically collects, transfers and processes VIs, fPAR and LAI data streams. The 4S consists of a microcomputer, controller and camera, a multi-spectral spectrometer built in with a light-emitting diode (LED) and an internet connection. Lab testing and field observations in a rice paddy site that experiences wet summer monsoon seasons confirmed the linear response of 4S to light intensities in the blue, green, red and near-infrared spectral channels, with wide ranging temperatures and humidity having only a minor impact on 4S throughout the growing season. Applied over an entire rice growing season (day of year [DOY] 120 - 248), VIs and fPAR from 4S were linearly related to corresponding VIs from a reference spectrometer (R2 = 0.98; NDVI, R2 = 0.96; EVI) and the LAI-2200 instrument (R2 = 0.76), respectively. Integration of gap fraction-based LAI from LED sensors and a green index from the micro-camera allowed tracking of the seasonality of green LAI. The continuous and diverse nature of 4S observations highlights its potential for evaluating satellite remote sensing products. We believe that 4S will be useful for the expansion of ecological sensing networks across multiple spatial and temporal scales. [ABSTRACT FROM AUTHOR]