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1. Proximal remote sensing and gross primary productivity in a temperate salt marsh

   https://doi.org/10.1016/j.agrformet.2023.109639  

   Vázquez-Lule, Alma; Vargas, Rodrigo (October 2023, Agricultural and Forest Meteorology)

   Salt marshes are highly productive ecosystems relevant for Blue Carbon assessments, but information for estimating gross primary productivity (GPP) from proximal remote sensing (PRS) is limited. Temperate salt marshes have seasonal canopy structure and metabolism changes, defining different canopy phenological phases, GPP rates, and spectral reflectance. We combined multi-annual PRS data (i.e., PhenoCam, discrete hyperspectral measurements, and automated spectral reflectance sensors) with GPP derived from eddy covariance. We tested the performance of empirical models to predict GPP from 12 common vegetation indices (VIs; e.g., NDVI, EVI, PSRI, GCC), Sun-Induced Fluorescence (SIF), and reflectance from different areas of the electromagnetic spectrum (i.e., VIS-IR, RedEdge, IR, and SIF) across the annual cycle and canopy phenological phases (i.e., Greenup, Maturity, Senescence, and Dormancy). Plant Senescence Reflectance Index (PSRI) from hyperspectral data and the Greenness Index (GCC) from PhenoCam, showed the strongest relationship with daily GPP across the annual cycle and within phenological phases (r2=0.30–0.92). Information from the visible-infrared electromagnetic region (VIS-IR) coupled with a partial least square approach (PLSR) showed the highest data-model agreement with GPP, mainly because of its relevance to respond to physiological and structural changes in the canopy, compared with indices (e.g., GCC) that particularly react to changes in the greenness of the canopy. The most relevant electromagnetic regions to model GPP were ∼550 nm and ∼710 nm. Canopy phenological phases impose challenges for modeling GPP with VIs and the PLSR approach, particularly during Maturity, Senescence, and Dormancy. As more eddy covariance sites are established in salt marshes, the application of PRS can be widely tested. Our results highlight the potential to use canopy reflectance from the visible spectrum region for modeling annual GPP in salt marshes as an example of advances within the AmeriFlux network. 

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2. Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

   https://doi.org/10.3390/drones4030056  

   Atkins, Jeff W.; Stovall, Atticus E.; Yang, Xi (September 2020, Drones)

   null (Ed.)

   Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity. 

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3. Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data

   https://doi.org/10.3390/RS12183077  

   Bórnez, Kevin; Richardson, Andrew D.; Verger, Aleixandre; Descals, Adrià; Peñuelas, Josep (September 2020, Remote Sensing)

   null (Ed.)

   High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile). 

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4. UAS‐based multispectral imaging for detecting iron chlorosis in grain sorghum

   https://doi.org/10.1002/agg2.20540  

   Garcia‐Williams, Isabel_A; Starek, Michael_J; Brewer, Michael_J; Berryhill, Jacob (July 2024, Agrosystems, Geosciences & Environment)

   Abstract This study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolorL.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs. 

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5. Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

   https://doi.org/10.5194/bg-18-1971-2021  

   Wheeler, Kathryn I.; Dietze, Michael C. (January 2021, Biogeosciences)

   Abstract. Monitoring leaf phenology tracks the progression ofclimate change and seasonal variations in a variety of organismal andecosystem processes. Networks of finite-scale remote sensing, such as thePhenoCam network, provide valuable information on phenological state at hightemporal resolution, but they have limited coverage. Satellite-based data withlower temporal resolution have primarily been used to more broadly measurephenology (e.g., 16 d MODIS normalizeddifference vegetation index (NDVI) product). Recent versions of the GeostationaryOperational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI attemporal scales comparable to that of PhenoCam throughout most of thewestern hemisphere. Here we begin to examine the current capacity of thesenew data to measure the phenology of deciduous broadleaf forests for thefirst 2 full calendar years of data (2018 and 2019) by fittingdouble-logistic Bayesian models and comparing the transition dates of the start, middle, and end of theseason to those obtained from PhenoCam and MODIS 16 dNDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was morecorrelated with PhenoCam at the start and middle of spring but had a largerbias (3.35 ± 0.03 d later than PhenoCam) at the end of spring.Satellite-based autumn transition dates were mostly uncorrelated with thoseof PhenoCam. PhenoCam data produced significantly more certain (allp values ≤0.013) estimates of all transition dates than any of thesatellite sources did. GOES transition date uncertainties were significantlysmaller than those of MODIS EVI for all transition dates (all p values ≤0.026), but they were only smaller (based on p value <0.05) than thosefrom MODIS NDVI for the estimates of the beginning and middle of spring. GOES willimprove the monitoring of phenology at large spatial coverages and providesreal-time indicators of phenological change even when the entire springtransition period occurs within the 16 d resolution of these MODISproducts. 

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