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1. Drone-Based Measurements of Soil Water Content in Potential RSL Analogs: Hyperspectral Moisture Mapping of Hydrothermal Spring Discharge in the Alvord Desert, Oregon

   Johnson, J. (January 2020, 51st Lunar and Planetary Science Conference)

   While previous field analyses of seasonal, linear soil moisture features have focused largely on analysis of image data or ground-based spectroscopic measurements, here we report on new efforts to quantify soil moisture in a hydrothermal spring discharge plume in the Alvord Desert of eastern Oregon, using drone-based hyperspectral measurements in the vicinity of 1.4 µm, combined with ground-based measurements of soil composition and physical properties (clay content and grain size distribution, salinity, and soil moisture). 

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2. Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland

   https://doi.org/10.1139/dsa-2023-0003  

   Slade, Glenn; Fawcett, Dominic; Cunliffe, Andrew M.; Brazier, Richard E.; Nyaupane, Kamal; Mauritz, Marguerite; Vargas, Sergio; Anderson, Karen (January 2023, Drone Systems and Applications)

   Drone-based multispectral sensing is a valuable tool for dryland spatial ecology, yet there has been limited investigation of the reproducibility of measurements from drone-mounted multispectral camera array systems or the intercomparison between drone-derived measurements, field spectroscopy, and satellite data. Using radiometrically calibrated data from two multispectral drone sensors (MicaSense RedEdge (MRE) and Parrot Sequoia (PS)) co-located with a transect of hyperspectral measurements (tramway) in the Chihuahuan desert (New Mexico, USA), we found a high degree of correspondence within individual drone data sets, but that reflectance measurements and vegetation indices varied between field, drone, and satellite sensors. In comparison to field spectra, MRE had a negative bias, while PS had a positive bias. In comparison to Sentinel-2, PS showed the best agreement, while MRE had a negative bias for all bands. A variogram analysis of NDVI showed that ecological pattern information was lost at grains coarser than 1.8 m, indicating that drone-based multispectral sensors provide information at an appropriate spatial grain to capture the heterogeneity and spectral variability of this dryland ecosystem in a dry season state. Investigators using similar workflows should understand the need to account for biases between sensors. Modelling spatial and spectral upscaling between drone and satellite data remains an important research priority. 

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3. Predicting Urban Trees’ Functional Trait Responses to Heat Using Reflectance Spectroscopy

   https://doi.org/10.3390/rs16132291  

   Kyaw, Thu Ya; Alonzo, Michael; Baker, Matthew E; Eisenman, Sasha W; Caplan, Joshua S (July 2024, Remote Sensing)

   Plant traits are often measured in the field or laboratory to characterize stress responses. However, direct measurements are not always cost effective for broader sampling efforts, whereas indirect approaches such as reflectance spectroscopy could offer efficient and scalable alternatives. Here, we used field spectroscopy to assess whether (1) existing vegetation indices could predict leaf trait responses to heat stress, or if (2) partial least squares regression (PLSR) spectral models could quantify these trait responses. On several warm, sunny days, we measured leaf trait responses indicative of photosynthetic mechanisms, plant water status, and morphology, including electron transport rate (ETR), photochemical quenching (qP), leaf water potential (Ψleaf), and specific leaf area (SLA) in 51 urban trees from nine species. Concurrent measures of hyperspectral leaf reflectance from the same individuals were used to calculate vegetation indices for correlation with trait responses. We found that vegetation indices predicted only SLA robustly (R2 = 0.55), while PLSR predicted all leaf trait responses of interest with modest success (R2 = 0.36 to 0.58). Using spectral band subsets corresponding to commercially available drone-mounted hyperspectral cameras, as well as those selected for use in common multispectral satellite missions, we were able to estimate ETR, qP, and SLA with reasonable accuracy, highlighting the potential for large-scale prediction of these parameters. Overall, reflectance spectroscopy and PLSR can identify wavelengths and wavelength ranges that are important for remote sensing-based modeling of important functional trait responses of trees to heat stress over broad ranges. 

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4. Comparison of Soil Moisture Content Retrieval Models Utilizing Hyperspectral Goniometer Data and Hyperspectral Imagery From an Unmanned Aerial System

   https://doi.org/10.1029/2023JG007381  

   Nur, Nayma Binte; Bachmann, Charles M. (June 2023, Journal of Geophysical Research: Biogeosciences)

   Abstract To understand surface biogeophysical processes, accurately evaluating the geographical and temporal fluctuations of soil moisture is crucial. It is well known that the surface soil moisture content (SMC) affects soil reflectance at all solar spectrum wavelengths. Therefore, future satellite missions, such as the NASA Surface Biology and Geology mission, will be essential for mapping and monitoring global soil moisture changes. Our study compares two widely used moisture retrieval models: the multilayer radiative transfer model of soil reflectance (MARMIT) and the soil water parametric (SWAP)‐Hapke model. We evaluated the SMC retrieval accuracy of these models using unmanned aerial systems (UAS) hyperspectral imagery and goniometer hyperspectral data. Laboratory analysis employed hyperspectral goniometer data of sediment samples from four locations reflecting diverse environments, while field validation used hyperspectral UAS imaging and coordinated ground truth collected in 2018 and 2019 from a barrier island beach at the Virginia Coast Reserve Long‐Term Ecological Research site. The (SWAP)‐Hapke model achieves comparable accuracy to MARMIT using laboratory hyperspectral data but is less accurate when applied to UAS hyperspectral imagery than the MARMIT model. We proposed a modified version of the (SWAP)‐Hapke model, which achieves better results than MARMIT when applied to laboratory spectral measurements; however, MARMIT's performance is still more accurate when applied to UAS imagery. These results are likely due to differences in the models' descriptions of multiply‐scattered light and MARMIT's more detailed description of air‐water interactions. 

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5. Landscape Position Impacts on the Water Balance in the Chihuahuan Desert: Insights From Cosmic-Ray Neutron Sensing at Upland Watershed and Downstream Playa Sites

   Hurtado, Ruby Yaritza (June 2024, ProQuest)

   In the semiarid, water-limited deserts of the Southwest United States, soil moisture is a crucial factor influencing atmospheric, hydrologic, and ecological processes. These dynamics are driven by infrequent yet significant precipitation events that redistribute moisture and establish hydrologic connectivity across the landscape. The Chihuahuan Desert, particularly within its endorheic basins, exemplifies these large-scale interactions where a complex balance of hydrological fluxes is maintained within a closed system. These basins receive most of their precipitation in upland regions, from which surface runoff can lead to downstream connectivity. This connectivity is influenced by the local water balance, including interactions among precipitation, leakage, and evapotranspiration, which are essential for understanding soil moisture variability. Additionally, soil moisture is affected by soil profile characteristics, vegetation, and atmospheric conditions. Field-scale methods like Cosmic-Ray Neutron Sensing (CRNS) are more appropriate than point-scale in situ sensors for quantifying hydrologic connectivity between upland and downstream regions, as CRNS reliably captures soil moisture temporal dynamics over several hectares. This study examines these dynamics within the endorheic Jornada Basin of the Chihuahuan Desert, focusing on two contrasting sites: an Upland Watershed (UW) on a piedmont slope and a Downstream Playa (DP) in a valley bottom. Using CRNS and complementary water balance instrumentation, I compared soil moisture dynamics at these two sites from July 2022 to February 2024. My analysis centered on a significant precipitation event early in the study period that generated surface runoff and playa inundation, followed by an extended dry period. Although temporal variations in leakage and evapotranspiration are similar at both sites, their rates differ significantly. The UW experienced a higher drying rate, necessitating greater plant water uptake from the subsurface. This led to an increased upward leakage to sustain vegetation, resulting in a leakage value of -205 mm, indicating vertical plant water uptake. Conversely, at the DP, the inundation event was formed by 228 mm of surface runoff, supplementing water inputs from precipitation. This additional water reduced the need for upward soil water movement to sustain plant water uptake, resulting in a leakage value of -97 mm. These findings enhance our understanding of hydrologic fluxes within endorheic basins and improve the applicability of hydrological models and the downscaling of remotely sensed soil moisture products. 

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