Impact of vertical and seasonal variation in leaf traits on simulating soybean canopy photosynthesis via 1D and 3D modeling
Year: 2025
Type: article
Abstract: • Vcmax 25 variation is more critical than LCC and LMA in GPP simulation. • Both measured and empirically parameterized Vcmax 25 profiles mitigated GPP overestimation. • The 1D and 3D models show similar response to the vertical and seasonal variation in leaf traits. • A scalable leaf traits parameterization is proposed for GPP modeling. • Effectiveness of the proposed parameterization is confirmed at the study site. Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax 25 ), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax 25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax 25 profile achieved comparable performance to the scheme with the measured Vcmax 25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R 2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R 2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDGs: Climate action, Zero hunger
Open Access status: hybrid
Estimated APC paid: $3,590
Awards: G2443523817, G2661944491, G3911128585, WITH RISING DEMANDS OF FOOD AND FIBER FROM A GROWING GLOBAL POPULATION AGRICULTURAL LANDSCAPE PLAYS AN INCREASINGLY IMPORTANT ROLE IN THE GLOBAL CARBON CYCLE. CROPLAND ALSO REPRESENTS ONE OF THE BIGGEST OPPORTUNITIES FOR CARBON SEQUESTRATION. ACCURATE QUANTIFICATION OF REGIONAL SCALE CROPLAND CARBON CYCLING IS CRITICAL FOR DESIGNING EFFECTIVE POLICIES AND MANAGEMENT PRACTICES THAT CAN CONTRIBUTE TO STABILIZING ATMOSPHERIC CO2 CONCENTRATIONS. A COMPREHENSIVE CARBON MONITORING SYSTEM SHOULD INCLUDE THE INTEGRATION OF BOTTOM-UP AND TOP-DOWN ESTIMATES OF CARBON FLUX. HOWEVER THE CURRENT CROPLAND-BASED CARBON MONITORING SYSTEMS FACE THE FOLLOWING CHALLENGES: (1) THEY PRIMARILY FOCUS ON BOTTOM-UP APPROACHES WITH LACK OF INTEGRATION AND CROSS-VERIFICATION BETWEEN BOTTOM-UP AND TO-DOWN APPROACHES; (2) THEY ARE LACK OF SPATIALLY EXPLICIT CHARACTERIZATION IN EITHER BOTTOM-UP PROCESS-BASED MODELS OR TOP-DOWN ATMOSPHERIC INVERSIONS. NOVEL SATELLITE DATA (INCLUDING SOLAR INDUCED CHLOROPHYLL FLUORESCENCE AND ATMOSPHERIC COLUMN-AVERAGE CO2 AND OTHER EXISTING NASA SATELLITE DATA PROVIDE UNIQUE OPPORTUNITIES IN ADDRESSING THESE CHALLENGES AND IMPROVING BOTH BOTTOM-UP AND TOP-DOWN APPROACHES. HERE WE PROPOSE ONE OF THE FIRST CARBON MONITORING SYSTEMS (CMS) THAT WILL INTEGRATE BOTH BOTTOM-UP AND TOP-DOWN APPROACHES TO JOINTLY QUANTIFY THE CARBON BUDGET FOR THE US CORN BELT. THE PROPOSAL PLANS TO ACHIEVE THREE MAJOR IMPROVEMENTS FOR BOTTOM-UP AND TOP-DOWN (TASK 1-3) WITH TASK 4 TO INTEGRATE AND SYNTHESIZE RESULTS FROM THE TWO APPROACHES TO GENERATE A CONSISTENT US CORN BELT CARBON FLUX PRODUCT INCLUDING A THOROUGH UNCERTAINTY ASSESSMENT COVERING THE PERIOD OF 2007 TO 2017. SPECIFICALLY THE FOUR TASKS ARE: TASK 1 (BOTTOM-UP APPROACH INVENTORY/SATELLITE): COMBINE USDA CROP STATISTICS-BASED AND SATELLITE-BASED SOLAR-INDUCED FLUORESCENCE (GOME-2 AND OCO-2) TO GENERATE AN IMPROVED 10 KM CARBON BUDGET INVENTORY (NPP GPP AND RA) FOR THE US CORN BELT. TASK 2 (BOTTOM-UP APPROACH MODELING/SATELLITE): ASSIMILATE MULTI-SOURCES OF SATELLITE DATA (MODIS LAI SMAP SOIL MOISTURE) AND NEWLY DERIVED CROP INVENTORY DATA (FROM TASK 1) INTO THE CLM-APSIM FRAMEWORK TO EXPLICITLY CONSTRAIN THE CROP PARAMETERS IN SPACE AND IMPROVE CARBON BUDGET SIMULATION. TASK 3 (TOP-DOWN APPROACH SATELLITE/IN-SITU): USE SATELLITE AND IN SITU DATA TOGETHER TO SOLVE FOR CO2 FLUXES AT HIGH-RESOLUTION IN A REGIONAL INVERSION OVER THE US CORN BELT. TASK 4 (BOTTOM-UP/TOP-DOWN INTEGRATION): INTEGRATE BOTTOM-UP AND TOP-DOWN APPROACHES TO JOINTLY CONSTRAIN THE CARBON BUDGET CROSS-VERIFY ESTIMATES AND PROVIDE ROBUST UNCERTAINTY CHARACTERIZATION. THIS CURRENT PROPOSAL TARGETS AT THE 2ND RESEARCH TOPIC THAT IS LISTED IN THE NASA CMS SOLICITATION I.E STUDIES THAT ADDRESS RESEARCH NEEDS TO ADVANCE REMOTE SENSING-BASED APPROACHES TO MONITORING REPORTING AND VERIFICATIONS. THE PROPOSED PROJECT DIRECTLY ADDRESSES NASA S STRATEGIC GOAL FOR THE EARTH SCIENCE TO STUDY PLANET EARTH FROM SPACE TO ADVANCE SCIENTIFIC UNDERSTANDING AND MEET SOCIETAL NEEDS . THE PROJECT WILL FULLY UTILIZE THE SIF AND XCO2 RETRIEVALS FROM THE NEW NASA SATELLITE OCO-2 AS WELL AS DATA FROM OTHER EXISTING NASA SATELLITE PRODUCTS (E.G. FROM SMAP MODIS CERES AND LANDSAT-BASED CROP DATA LAYER) TO DEVELOP IMPROVED CARBON FLUX ESTIMATIONS FROM BOTTOM-UP APPROACHES (INVENTORY-SATELLITE BASED AND PROCESS-MODEL BASED) AND TOP-DOWN APPROACHES (JOINTLY USING SATELLITE AND IN SITU DATA IN THE ATMOSPHERIC INVERSION). PUBLIC AND PRIVATE SECTORS CAN USE THIS PRODUCT TO INFORM AGRICULTURAL PRODUCTIVITY AND MANAGEMENTS WHICH WOULD FURTHER REALIZE THE VALUE OF NASA DATA. THIS EFFORT THUS CARRIES A GREAT PROMISE TO FURTHER CONSTRAIN THE REGIONAL AND GLOBAL CARBON CYCLE AND ALSO TO DIRECTLY ADDRESS ONE OF NASA S KEY SCIENTIFIC QUESTIONS FOR EARTH SCIENCE: HOW WILL CARBON CYCLE DYNAMICS AND ECOSYSTEM CHANGE IN THE FUTURE?, UIUC
CONTROL NUMBER 1953-1656, NEW AWARD
PROJECT TITLE ''MIDWEST BIOENERGY CROP LANDSCAPE LABORATORY (MBC-LAB): CAPTURING SPATIO-TEMPORAL
AND MANAGERIAL VARIATIONS TO PROVIDE A GOLD STANDARD DATA AND PLATFORM FOR VALIDATING
FIELD-LEVEL EMISSION FROM BIOENERGY CROPS'', G5540649282, G601984919, ----------
UNIVERSITY OF ILLINOIS-URBANA-CHAMPAIGN: NEW CONDITIONAL SMARTFARM AWARD.
CONTROL NUMBER: 2250-1516
TITLE: “THE ‘SYSTEM OF SYSTEMS’ SOLUTIONS FOR COMMERCIAL FIELD-LEVEL QUANTIFICATION OF SOIL ORGANIC CARBON AND NITROUS OXIDE EMISSION FOR SCALABLE APPLICATIONS (SYMFONI)”
----------, G6968631012, G83646676
OpenAlex ID: W4416531502