## 1 Introduction  
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]. Researchers have found strong temporal correlations between meteorological variables (e.g., temperature and photosynthetically active radiation) and ecosystem properties (e.g., photosynthesis and respiration) at landscape and regional scales or at low temporal frequency [7; 8; 9; 10]. Near-surface remote sensing would play an important role in experimenting across spatial and temporal scales and filling the knowledge gap [11]. Gaining insights for local-scale ecosystem functions using near-surface remote sensing is not a new concept. Spectroscopy, fluorescence sensing, and thermal imaging are three examples of the most promising near-surface remote sensing techniques for studying plant responses to environmental conditions at a fine scale [12]. Plant spectroscopy employs observations of spectral reflectance of plants across a contiguous spectral region that are often collected in the field or laboratory using a spectrometer. Such detailed measurements of plant spectral reflectance are known to provide information about plant traits that play an important role in plant photosynthesis [13] and other functions through establishing empirical relationships between their traits and spectral reflectance. Thus, plant spectroscopy provides a noninvasive, nondestructive alternative to traditional methods for collecting plant trait measurements. [PERSON] et al. [14] developed a tower-based hyperspectral system, WhiteRef, to automatically collect spectral reflectance measurements at a Mediterranean grassland. Spectral vegetation indices derived from the hyperspectral reflectance measurements collected by the system showed strong correlations with plant pigment and the fraction of photochemically active radiation (fPAR). Sun-induced chlorophyll fluorescence (SIF) is known to capture rapid change in photosynthesis, and its study is a fast-growing research area in remote sensing of vegetation [15]. To track SIF changes and their correlation with gross primary production (GPP), [PERSON], et al. [16] developed a tower-based fluorescence measurement system using a spectrometer having a very fine spectral resolution (approximately 0.13 nm) for the 680-775 nm spectral range. The study conducted in temperate deciduous forests showed strong diurnal and seasonal correlations between SIF and GPP estimated using the eddy covariance method (\(R^{2}\) = 0.82 and 0.73, respectively) as well as with fPAR (\(R^{2}\) = 0.90 and 0.80, respectively) [16]. [PERSON] et al. [17] further improved the system by optimizing tradeoff between spectral range and resolution. [PERSON] et al. [18] integrated a visible band (400-900 nm) into a SIF sensing system to allow for calculating vegetation indices (PhotoSpec). The initial testing based on broad leaves, grass, and dark light showed the stability and robustness of the system for continuously collecting SIF measurements throughout the day [18]. Increased canopy temperature caused by plant water stress is widely studied using thermal infrared (TIR) remote sensing [19; 20]. For proximal sensing, [PERSON] et al. [20] assembled a TIR imaging system using an off-the-shelf TIR camera to evaluate the effectiveness of high spatial resolution canopy temperature in relation to soil moisture to study plant moisture content.