# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 1 Introduction  
The global population is likely to exceed 10 billion by 2050, presenting great challenges for agriculture [1]. To fulfill the needs of the rapidly growing population, the current agricultural yield must be doubled by that time, which translates into an annual increase of 1.75% total factor productivity (TFP) [2]. Currently, the global TFP growth rate is approximately 1.5%, leaving a gap of 0.25% annually. Even worse, the TFP growth rate is only approximately 0.96% in developing countries, which is far behind the required growth rate. Cotton (_Gossypium_) is one of the most important textile fibers in the world, accounting for about 25% of total world textile fiber use [3]. Thus, improvement of cotton production is vital for fulfilling the fiber requirements of over ten billion people by 2050 [4]. As with all agricultural crops that have reproductive structures of economic importance, the yield of cotton can be expressed as a function of total seasonal light interception, radiation use efficiency, and harvest index [5]. Thus, yield improvement can be achieved by increasing any one of these three variables. The \"Green Revolution\" introduced dwarfing genes into the most important C3 cereal crops (e.g., rice and wheat), allowing an increased biomass allocation to grain with a reduction in the total aboveground biomass (thus, an increased harvest index) [6]. Breeding programs have continued to increase carbon allocation into grain [7], and in cotton specifically, genetic yield improvement historically has also been associated with an increase in biomass partitioning to reproductive units (bolls) [8; 9]. One study [10], however, suggested that future yield improvements in high potential environments would likely be achieved by (1) selecting cotton varieties that exhibit a more indeterminate growth habit (i.e., capitalize on the high insolation levels experienced in long growing season environments); and (2) increasing photosynthetic efficiency either through breeding or biotechnology efforts. Photosynthesis is a process that converts radiant energy into biochemical energy, and is the basis of plant growth. In contrast with efforts to breed for desirable plant growth habits or greater harvest index, photosynthetic improvement has not yet been achieved for breeding programs, and remains a promising avenue for increasing agricultural productivity in the future. In addition, variations in photosynthetic efficiency can be used as indicators of plant stress, which can be used for selecting genotypes with high levels of stress tolerance or for making management decisions at the field scale [11]. Furthermore, there is a tremendous amount of interest in using remote sensing to model gross primary productivity of natural ecosystems according to the original framework of [5; 12]. In order to achieve this, the photosynthetic efficiency of the canopy must be estimated. Chlorophyll fluorescence parameters are often used to evaluate photosynthetic performance and stress in plants [13]. These parameters include three measurable variables; i.e., minimal fluorescence (\(F_{0}\) or \(F_{0}^{\prime}\)) when photosystem II (PSII) centers are open, maximal fluorescence (\(F_{m}\) or \(F_{m}^{\prime}\)) when PSII centers are closed, and steady state fluorescence (\(F_{s}\) or \(F_{s}^{\prime}\)). There are several derived variables, such as variable fluorescence (\(F_{v}=F_{m}-F_{0}\) or \(F_{v}^{\prime}=F_{m}^{\prime}-F_{0}^{\prime}\)) and difference in fluorescence (\(F_{q}=F_{m}-F_{s}\) or \(F_{q}^{\prime}=F_{m}^{\prime}-F_{s}^{\prime}\)) between \(F_{m}\) (or \(F_{m}^{\prime}\)) and \(F_{s}\) (or \(F_{s}^{\prime}\)). Variables denoted by primes are for light-adapted states, and are otherwise for dark-adapted states.