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Wu, Jin

Publications and source records attributed to Wu, Jin.

45 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

G-LiHT Campaign Leaf Sample details & photos, March 2017: Puerto Rico

This data package includes details of leaves sampled for leaf spectra and chemistry from 5 sites in Puerto Rico, in March of 2017. Sunlit canopy and shaded leaves of 66 species were collected. Data for each sample includes species, leaf age, type of analysis (spectroscopy, gas exchange, chemistry), sample number and sample photographs. The data package includes a spreadsheet with sample information and a zip file of photographs (1.6 GB). This data was collected as part of the 2017 BNL–G-LiHT leaf spectra campaign. See related datasets for leaf spectral reflectance and transmittance, leaf mass area (LMA), and leaf chemistry. Note that leaf sample details are also included in related datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

G-LiHT Campaign Leaf Carbon and Nitrogen Content, Mar2017: Puerto Rico

Measurements of leaf carbon and nitrogen content collected from 68 tropical tree species. Data includes leaves collected from fully sunlit and shaded canopy strata as well as leaves for young, mature, old and senescent leaf ages. Data for each sample includes the relative age estimate, leaf canopy position and sample number. This data was collected as part of the 2017 NGEE-Tropics / NASA G-LiHT airborne campaign. This data package includes processed data for leaf carbon and nitrogen content (*.csv). Metadata files include data description (_dd.csv) for tabular data, site information (*.csv), sampling protocol (*.pdf) and the NGEE-Tropics FRAMES e-field log and file submission metadata (*.xlsx). See related datasets for sample details including photographs, leaf-level reflectance and transmittance spectra, leaf mass per area (LMA) and water content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest

In tropical forests, leaf phenology-particularly the pronounced dry-season green-up-strongly regulates biogeochemical cycles of carbon and water fluxes. However, uncertainties remain in the understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a few years. Time-series satellite observations might fill this gap, but the commonly used satellites (e.g. MODIS, Landsat and Sentinel-2) have resolutions too coarse to characterize single crowns. To resolve this observational challenge, we used the PlanetScope constellation with a 3m resolution and near daily nadir-view coverage. We first developed a rigorous method to cross-calibrate PlanetScope surface reflectance using daily BRDF-adjusted MODIS as the reference. We then used linear spectral unmixing of calibrated PlanetScope to obtain dry-season change in the fractional cover of green vegetation (GV) and non-photosynthetic vegetation (NPV) at the PlanetScope pixel level. We used the Central Amazon Tapajos National Forest k67 site, as all necessary data (from field to phenocam and satellite observations) was available. For this proof of concept, we chose a set of 22 dates of PlanetScope measurements in 2018 and 16 in 2019, all from the six drier months of the year to provide the highest possible cloud-free temporal resolution. Our results show that MODIS-calibrated dry-season PlanetScope data (1) accurately assessed seasonal changes in ecosystem-scale and crown-scale spectral reflectance; (2) detected an increase in ecosystem-scale GV fraction (and a decrease in NPV fraction) from June to November of both years, consistent with local phenocam observations with R 2 around 0.8; and (3) monitored large seasonal trend variability in crown-scale NPV fraction. Finally, our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The response of stomatal conductance to seasonal drought in tropical forests

Stomata regulate CO2 uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (gs) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and water potential (?leaf), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (Vc,max25), leaf water content, the degree of isohydry, and predawn ?leaf. We first investigated how the choice of four commonly used leaf-level gs models with and without the inclusion of ?leaf as an additional predictor variable influence the ability to predict gs, and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of ?leaf did not improve model performance and that the models that represent the response of gs to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each gs model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R2=0.66, n=15), highlighting a potential path towards improved model parameterization. This study advances understanding of gs dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

carbon and water exchange, stomatal conductance mo↗
DOE OSTI2020

Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging

Abstract Photosynthesis is currently measured using time-laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (~2 m×2 m) in <1 min. Using field-grown Nicotiana tabacum with genetically altered photosynthetic pathways over two growing seasons (2017 and 2018), we built predictive models for eight photosynthetic parameters and pigment traits. Using partial least squares regression (PLSR) analysis of plot-level sunlit vegetative reflectance pixels from a single visible near infra-red (VNIR) (400–900 nm) hyperspectral camera, we predict maximum carboxylation rate of Rubisco (Vc,max, R2=0.79) maximum electron transport rate in given conditions (J1800, R2=0.59), maximal light-saturated photosynthesis (Pmax, R2=0.54), chlorophyll content (R2=0.87), the Chl a/b ratio (R2=0.63), carbon content (R2=0.47), and nitrogen content (R2=0.49). Model predictions did not improve when using two cameras spanning 400–1800 nm, suggesting a robust, widely applicable and more ‘cost-effective’ pipeline requiring only a single VNIR camera. The analysis pipeline and methods can be used in any cropping system with modified species-specific PLSR analysis to offer a high-throughput field phenotyping screening for germplasm with improved photosynthetic performance in field trials.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2017

Using High Spatial Resolution Satellite Imagery to Map Forest Burn Severity Across Spatial Scales in a Pine Barrens Ecosystem

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (less than or equal to 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal - pre- and post-fire event - WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the less than 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.

Meng, Ran↗
NASA NTRS1995

Sea surface winds-A critical input to oceanic models, but are they accurately measured?

Wind, driving oceans, and the links between them to the atmosphere compose a critical parameter for the world circulation model as well as for the evaluation of climate changes. Traditionally, wind velocities have been reported by ships of oppurtunity and recorded on a network of buoys; they have also recently been generated by numerical weather prediction models and mapped with spaceborne remote sensors. Wind speeds from buoy measurements, ship observations, and model computations are compared, using the globally available altimeter returns that they have in common. Large, systematic deviations are found among the results obtained with these techniques, cautioning against use of these wind speeds.

Wu, Jin↗
NASA NTRS1992

Near-nadir microwave specular returns from the sea surface - Altimeter algorithms for wind and wind stress

Two approaches have been adopted to construct altimeter wind algorithms: one is based on the mean-square sea surface slope, and the other is based on the Seasat scatterometer wind. Both types of algorithms are critically reviewed with respect to the mechanism governing near-nadir sea returns and the comparison between altimeter and buoy winds. A new algorithm is proposed; it is deduced on the basis of microwave specular reflection and is finely tuned with buoy-measured winds. On the basis of this algorithm and the formula of the wind-stress coefficient, a simple wind-stress algorithm is also proposed.

Wu, Jin↗