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1. Tidal Wetland Gross Primary Production Across the Continental United States, 2000–2019

   https://doi.org/10.1029/2019GB006349  

   Feagin, R. A.; Forbrich, I.; Huff, T. P.; Barr, J. G.; Ruiz‐Plancarte, J.; Fuentes, J. D.; Najjar, R. G.; Vargas, R.; Vázquez‐Lule, A.; Windham‐Myers, L.; et al (February 2020, Global Biogeochemical Cycles)

   Abstract We mapped tidal wetland gross primary production (GPP) with unprecedented detail for multiple wetland types across the continental United States (CONUS) at 16‐day intervals for the years 2000–2019. To accomplish this task, we developed the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field‐based eddy covariance tower data into a single Bayesian framework, and used a super computer network and remote sensing imagery (Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index). We found a strong fit between the BC model and eddy covariance data from 10 different towers (r²= 0.83,p< 0.001, root‐mean‐square error = 1.22 g C/m²/day, average error was 7% with a mean bias of nearly zero). When compared with NASA's MOD17 GPP product, which uses a generalized terrestrial algorithm, the BC model reduced error by approximately half (MOD17 hadr²= 0.45,p< 0.001, root‐mean‐square error of 3.38 g C/m²/day, average error of 15%). The BC model also included mixed pixels in areas not covered by MOD17, which comprised approximately 16.8% of CONUS tidal wetland GPP. Results showed that across CONUS between 2000 and 2019, the average daily GPP per m²was 4.32 ± 2.45 g C/m²/day. The total annual GPP for the CONUS was 39.65 ± 0.89 Tg C/year. GPP for the Gulf Coast was nearly double that of the Atlantic and Pacific Coasts combined. Louisiana alone accounted for 15.78 ± 0.75 Tg C/year, with its Atchafalaya/Vermillion Bay basin at 4.72 ± 0.14 Tg C/year. The BC model provides a robust platform for integrating data from disparate sources and exploring regional trends in GPP across tidal wetlands. 

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2. The effects of alternate wetting and drying irrigation on water use efficiency in Mid-South rice

   https://doi.org/10.1016/j.agrformet.2024.110069  

   Reavis, Colby W; Reba, Michele L; Runkle, Benjamin RK (June 2024, Agricultural and Forest Meteorology)

   Improved water management is a growing need in areas where rice production is intensive. In the state of Arkansas and other portions of the US, new irrigation practices are being implemented to conserve water during rice cultivation. The goal of this study was to evaluate canopy water use in two commercial rice fields using different irrigation practices across three growing seasons. Canopy water use was assessed across multiple metrics, including different representations of water use efficiency (WUE) as well as their contributing terms, gross primary production (GPP) and evapotranspiration (ET). Furthermore, we validated and employed a methodology for estimating transpiration from ET using the concept of underlying water use efficiency (uWUE) that includes a sensitivity to vapor pressure deficit (VPD). Periodic drying associated with the alternate wetting and drying irrigation practice did not result in decreased GPP, ET, or transpiration (T). Our findings indicated that approximately 43 to 56 % of ET is released as T during the growing season. The uWUE method improved the relationship between GPP and ET by accounting for the limitation of VPD on GPP during the afternoon periods. 

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3. Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

   https://doi.org/10.3389/ffgc.2021.695269  

   Yazbeck, Theresia; Bohrer, Gil; Gentine, Pierre; Ye, Luping; Arriga, Nicola; Bernhofer, Christian; Blanken, Peter D.; Desai, Ankur R.; Durden, David; Knohl, Alexander; et al (December 2021, Frontiers in Forests and Global Change)

   Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions. 

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4. A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images

   https://doi.org/10.3390/rs13061140  

   Kinane, Stephen M.; Montes, Cristian R.; Albaugh, Timothy J.; Mishra, Deepak R. (March 2021, Remote Sensing)

   null (Ed.)

   Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context. 

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5. Solar‐induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO‐2 and flux tower observations

   https://doi.org/10.1111/gcb.14297  

   Li, Xing; Xiao, Jingfeng; He, Binbin; Altaf_Arain, M.; Beringer, Jason; Desai, Ankur_R; Emmel, Carmen; Hollinger, David_Y; Krasnova, Alisa; Mammarella, Ivan; et al (June 2018, Global Change Biology)

   Abstract Solar‐induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite‐observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory‐2 (OCO‐2) provides the first opportunity to examine the SIF–GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO‐2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO‐2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R² = 0.72,p < 0.0001). Strong linear relationships between SIF and GPP were consistently found for all biomes (R² = 0.57–0.79,p < 0.0001) except evergreen broadleaf forests (R² = 0.16,p < 0.05) at the daily timescale. A higher slope was found for C₄grasslands and croplands than for C₃ecosystems. The generally consistent slope of the relationship among biomes suggests a nearly universal rather than biome‐specific SIF–GPP relationship, and this finding is an important distinction and simplification compared to previous results. SIF was mainly driven by absorbed photosynthetically active radiation and was also influenced by environmental stresses (temperature and water stresses) that determine photosynthetic light use efficiency. OCO‐2 SIF generally had a better performance for predicting GPP than satellite‐derived vegetation indices and a light use efficiency model. The universal SIF–GPP relationship can potentially lead to more accurate GPP estimates regionally or globally. Our findings revealed the remarkable ability of finer resolution SIF observations from OCO‐2 and other new or future missions (e.g., TROPOMI, FLEX) for estimating terrestrial photosynthesis across a wide variety of biomes and identified their potential and limitations for ecosystem functioning and carbon cycle studies. 

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