Engineering topics

Wu, Genghong

Publications and source records attributed to Wu, Genghong.

14 records

DOE OSTI2025

Data for Attributing Differences of Solar-Induced Chlorophyll Fluorescence (SIF)-Gross Primary Production (GPP) Relationships between Two C4 Crops, Corn and Miscanthus

Information to characterize the solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationship in C4 cropping systems remains limited. The annual C4 crop corn and perennial C4 crop miscanthus differ in phenology, canopy structure and leaf physiology. Investigating the SIF-GPP relationships in these species could deepen our understanding of SIF-GPP relationships within C4 crops. Using in situ canopy SIF and GPP measurements for both species along with leaf-level measurements, we found considerable differences in the SIF-GPP relationships between corn and miscanthus, with a stronger SIF-GPP relationship and higher slope of SIF-GPP observed in corn compared to miscanthus. These differences were mainly caused by leaf physiology. For miscanthus, high non-photochemical quenching (NPQ) under high light, temperature and water vapor deficit (VPD) conditions caused a large decline of fluorescence yield (ΦF), which further led to a SIF midday depression and weakened the SIF-GPP relationship. The larger slope in corn than miscanthus was mainly due to its higher GPP in mid-summer, largely attributed to the higher leaf photosynthesis and less NPQ. Our results demonstrated variation of the SIF-GPP relationship within C4 crops and highlighted the importance of leaf physiology in determining canopy SIF behaviors and SIF-GPP relationships.

Feedstock Production↗

DOE OSTI2024

Evaluation of average leaf inclination angle quantified by indirect optical instruments in crop fields

Average leaf inclination angle ($\overline{θ}$ L ) is an important canopy structure variable that influences light regime, photosynthesis, and evapotranspiration of plants. $\overline{θ}$ L can be measured through direct methods (e.g., protractor), which are labor-intensive and time-consuming, or through indirect optical instruments, which are more efficient than the direct methods. However, uncertainties of different indirect optical instruments for quantifying $\overline{θ}$ L remain largely unquantified. In this study, we evaluated and compared the performances of three major indirect optical instruments: (1) LAI-2200, (2) 30°-tilted camera, and (3) digital hemispherical photography (DHP), in different crop fields over a growing season, benchmarked with direct measurements. LAI-2200 and 30°-tilted camera showed higher agreement with direct $\overline{θ}$ measurements (R 2 = 0.54, RMSE = 7.37°; R 2 = 0.58, RMSE = 8.08°) than DHP (R 2 = 0.14, RMSE = 13.96°). Different performances of indirect optical instruments could be attributed to the accuracy of gap fraction measurement and the performance of the $\overline{θ}$ L quantification algorithms. When using the LAI-2200 algorithm, larger gap fraction gradients over view zenith angles led to larger $\overline{θ}$ L values, and smaller gap fraction gradients led to smaller $\overline{θ}$ L values. Such error propagation was larger in sparse canopy than in dense canopy. The Wilson G function of the LAI-2200 algorithm performed better in estimating $\overline{θ}$ L than the G function based on the ellipsoidal LAD function used by the CAN_EYE algorithm. We also proposed a modification of the LAI-2200 algorithm, which further improved the performance of LAI-2200 and 30°-tilted cameras in estimating $\overline{θ}$ L . We envision that the low-cost 30°-tilted cameras provide a promising sensor solution to continuously monitor canopy structure for various ecosystems.

30°-tilted camera↗

DOE OSTI2023

SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (Φ PSIImax ), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R 2 = 0.86, RMSE=1.99 μmol m -2 s -1 ) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. Further, the seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the Φ PSIImax . These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in Φ PSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R 2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m –2 s –1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Furthermore, our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

SIF and Vegetation Indices in the US Midwestern Agroecosystems, 2016-2021, Carbon Monitoring System (CMS)

This dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart). The sites were either miscanthus, corn-soybean rotation or corn-corn-soybean rotation. The spectral data for SIF retrieval and hyperspectral reflectance for vegetation index calculation were collected by the FluoSpec2 system, installed near planting, and uninstalled after harvest to collect whole growing-season data. Raw nadir SIF at 760 nm from different algorithms (sFLD, 3FLD, iFLD, SFM) are included. SFM_nonlinear and SFM_linear represent the Spectral fitting method (SFM) with the assumption that fluorescence and reflectance change with wavelength non-linearly and linearly, respectively. Additional data include two SIF correction factors including calibration coefficient adjustment factor (f_cal_corr_QEPRO) and upscaling nadir SIF to eddy covariance footprint factor (ratio_EC footprint, SIF pixel), and measured FPAR from quantum sensors and Rededge NDVI calculated FPAR. The data are provided in comma-separated values (CSV) format.

Ameriflux↗

DOE OSTI2022

Attributing differences of solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationships between two C4 crops: corn and miscanthus

There remains limited information to characterize the solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationship in C4 cropping systems. The annual C4 crop corn and perennial C4 crop miscanthus differ in phenology, canopy structure and leaf physiology. Investigating the SIF-GPP relationships in these species could deepen our understanding of SIF-GPP relationships within C4 crops. Using in situ canopy SIF and GPP measurements for both species along with leaf-level measurements, we found considerable differences in the SIF-GPP relationships between corn and miscanthus, with a stronger SIF-GPP relationship and higher slope of SIF-GPP observed in corn compared to miscanthus. These differences were mainly caused by leaf physiology. For miscanthus, high non-photochemical quenching (NPQ) under high light, temperature and water vapor deficit (VPD) conditions caused a large decline of fluorescence yield (Φ F ), which further led to a SIF midday depression and weakened the SIF-GPP relationship. The larger slope in corn than miscanthus was mainly due to its higher GPP in mid-summer, largely attributed to the higher leaf photosynthesis and less NPQ. Furthermore, our results demonstrated variation of the SIF-GPP relationship within C4 crops and highlighted the importance of leaf physiology in determining canopy SIF behaviors and SIF-GPP relationships.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A physiological signal derived from sun-induced chlorophyll fluorescence quantifies crop physiological response to environmental stresses in the U.S. Corn Belt

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e. experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e. canopy structure and radiation, 60% ~ 82%) and physiological information (i.e. physiological SIF yield, Φ F , 17% ~ 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether Φ F indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that Φ F responded to the proxy for physiological stress (partial correlation coefficient, r p = 0.40, p < 0.001) while non-physiological signals of SIF did not respond ( p > 0.1). The field-scale Φ F data showed water deficit stress from the comparison between irrigated and rainfed fields, and Φ F was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r = 0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed Φ F was more strongly correlated spatially with air temperature and VPD ( r = 0.23 and 0.39) than SIF ( r = 0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that Φ F reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of Φ F is spatially scalable. Utilizing Φ F for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Interannual variability of ecosystem iso/anisohydry is regulated by environmental dryness

Plants are characterized by the iso/anisohydry continuum depending on how they regulate leaf water potential (?L). However, how iso/anisohydry changes over time in response to year-to-year variations in environmental dryness and how such responses vary across different regions remains poorly characterized. We investigated how dryness, represented by aridity index, affects the interannual variability of ecosystem iso/anisohydry at the regional scale, estimated using satellite microwave vegetation optical depth (VOD) observations. This ecosystem-level analysis was further complemented with published field observations of ?L at the species level. We found different behaviors in the directionality and sensitivity of isohydricity (s) with respect to the interannual variation of dryness in different ecosystems. These behaviors can largely be differentiated by the average dryness of the ecosystem itself: in mesic ecosystems, s decreases in drier years, albeit at a higher rate; in xeric ecosystems, s increases in drier years, albeit at a lower rate. These results were supported by the species-level synthesis. Our study suggests that how plants adjust their water use across years – as revealed by their interannual variability in isohydricity – depends on the dryness of plants’ living environment. This finding advances our understanding of plant responses to drought at regional scales.

Wu, Genghong↗

DOE OSTI2021

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

The photosynthetic capacity or the CO 2 -saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C 4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V max can be well predicted from spectra (r=0.81). Here, the integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V max prediction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Data from Unique Contributions of Chlorophyll and Nitrogen to Predict Crop Photosynthetic Capacity from Leaf Spectroscopy

The photosynthetic capacity or the CO2-saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that Vmax can be well predicted from spectra (r=0.81). The integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of Vmax (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to Vmax prediction.

Biomass Analytics↗

DOE OSTI2020

Varying Contributions of Drivers to the Relationship Between Canopy Photosynthesis and Far‐Red Sun‐Induced Fluorescence for Two Maize Sites at Different Temporal Scales

Abstract Sun‐induced fluorescence (SIF) has been found to be strongly correlated with gross primary production (GPP) in a quasi‐linear pattern at the scales beyond leaves. However, the causes of the GPP:SIF relationship deviating from a linear pattern remain unclear. In the current study conducted at two maize sites in Nebraska in 2017 summer growing season, we investigated the relationship between GPP and SIF at 760 nm (F 760 ) at two temporal scales and quantified the contributions of incoming photosynthetically active radiation (PAR in ), fraction of absorbed PAR (fPAR), light use efficiency (LUE), and F 760 yield (F 760,y , defined as F 760 /(PAR in ×fPAR)) to GPP and F 760 variabilities to further understand the linearity and deviations in the GPP:F 760 relationship. We found the following: (1) For individual growth stages when canopy structure and chlorophyll content were stable, GPP and F 760 were strongly controlled by PAR in , while LUE and F 760,y had much lower contributions to the GPP:F 760 relationship; during this period, LUE and F 760,y had either a slightly negative or no clear relationship, which explained some deviations in the GPP:SIF relationship. (2) At the seasonal scale, the contribution of LUE to GPP variability as well as the contribution of F 760,y to F 760 variability increased and was comparable to the contribution of PAR in ; the LUE:F 760,y relationship showed a strong linear relationship, which strengthened the linear GPP:F 760 relationship. Both maize sites showed similar patterns. A framework was applied to estimate LUE at individual stages and as a result, significantly improved the GPP estimation, thus enhancing the SIF potential for inferring photosynthesis.

Miao, Guofang↗

DOE OSTI2020

Radiance-based NIR v as a proxy for GPP of corn and soybean

Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIR v,Rad ), defined as the product of observed NIR radiance and normalized difference vegetation index (NDVI), can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIR v,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIR v,Ref ), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF760). The strong linear relationship between NIR v,Rad and absorbed photosynthetically active radiation by green leaves (APAR green ), and that between APAR green and GPP, explain the good NIR v,Rad -GPP relationship. The NIR v,Rad -GPP relationship is robust and consistent across sites. The scalability and simplicity of NIR v,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.

54 ENVIRONMENTAL SCIENCES↗