Automatic monitoring of ecosystem structure and functions using integrated low-cost near surface sensors

Near surface sensors are able to acquire more reliable and detailed information with higher temporal resolution than satellite observations. Conventional near surface sensors usually work individually, and thus they require considerable manpower from data collection through information extraction and sharing. Recent advances of Internet of Things (IoT) provides unprecedented opportunities to integrate various low-cost sensors as an intelligent near surface observation system for monitoring ecosystem structure and functions. In this study, we developed a Smart Surface Sensing System (4S), which can automatically collect, transfer, process and analyze data, and then publish time series results on public-available website. The system is composed of micro-computer Raspberry pi, micro-controller Arduino, multi-spectral spectrometers made from Light Emitting Diode (LED), visible and near infrared cameras, and Internet module. All components are connected with each other and Raspberry pi intelligently controls the automatic data production chain. We did intensive tests and calibrations in-lab. Then, we conducted in-situ observations at a rice paddy field and a deciduous broadleaf forest. During the whole growth season, 4S obtained landscape images, spectral reflectance in red, green, blue, and near infrared, normalized difference vegetation index (NDVI), fraction of photosynthetically active radiation (fPAR), and leaf area index (LAI) continuously. Also We compared 4S data with other independent measurements. NDVI obtained from 4S agreed well with Jaz hyperspectrometer at both diurnal and seasonal scales (R2 = 0.92, RMSE = 0.059), and 4S derived fPAR and LAI were comparable to LAI-2200 and destructive measurements in both magnitude and seasonal trajectory. We believe that the integrated low-cost near surface sensor could help research community monitoring ecosystem structure and functions closer and easier through a network system.