Abstract
Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350–2500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR; the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.
Highlights
Atmosphere, plants, and soils control terrestrial carbon and water cycles [1,2,3,4,5]
This paper presents a highly equipped hyperspectral remote sensing system, the EcoSpec system, that integrates complementary environmental sensors to simultaneously collect measurements with hyperspectral reflectance of land surfaces
We developed a prototype methodology for minimizing the sun-angle effects on hyperspectral reflectance collected from land surface, known as a bidirectional reflectance distribution function (BRDF)
Summary
Atmosphere, plants, and soils control terrestrial carbon and water cycles [1,2,3,4,5]. Better understanding of ecosystem heterogeneity and dynamics at the biosphere–atmosphere interface is needed for accurately forecasting future climate and contributions and responses of the terrestrial biosphere [1,4]. While our ability to forecast ecosystem functions and climate at regional and global scales has significantly advanced, little is known about how local phenomena such as heterogeneity in water and carbon fluxes at a daily time scale relate to large-scale phenomena and vice versa [4,6]. Understanding ecosystem functions and climate change interactions is a critical knowledge gap, and such interactions need to be better represented in climate change models [4,6,7,8,9]. Near-surface remote sensing would play an important role in experimenting across spatial and temporal scales and filling the knowledge gap [11]
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