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Abstract Background Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF). Results We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions. Conclusions TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.
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This content will become publicly available on September 1, 2026
Sampling Regime Effects on Detecting Spatial Stability of Water Quality
Abstract Protecting surface water quality can be complicated by high spatiotemporal variability. Pollutant sources and transport pathways may be identified through sufficiently high‐density monitoring sites and high‐frequency sampling, but practical considerations necessitate tradeoffs between spatial and temporal resolution in water quality monitoring network design. We examined how tradeoffs in sampling density and frequency affect measures of spatiotemporal variability in water quality, emphasizing pattern stability over time. We quantified the spatial stability of stream water quality across >250 monitoring sites in the intensively monitored watershed draining to Lake Okeechobee, FL using Spearman's rank correlations between instantaneous observations and site long‐term means for each parameter. We found that water quality spatial patterns for geogenic, biogenic, and anthropogenic parameters were generally stable on decadal timescales for all solutes, and that sampling densely in space yields more information than sampling frequently in time. Variations in spatial stability decreased with increased sampling density but not with greater sampling frequency, attesting to the dominance of spatial variability over temporal variability. For nutrients, the spatial coefficient of variation (CV) was approximately double the temporal CV. Spatial stability of most solutes was similar across flow conditions, but high‐flow monitoring allows for more sites that effectively capture the long‐term spatial patterns of nutrient sources. Water quality monitoring regimes can be optimized for efficiency in capturing water quality patterns and should be adjusted to focus more on spatial variation. We discuss potential improvements for water quality monitoring, particularly in watersheds where scarce resources necessitate tradeoffs between sampling density and frequency.
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- Award ID(s):
- 2129926
- PAR ID:
- 10647858
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 61
- Issue:
- 9
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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