Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize

Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based Vegetation Photosynthesis Model (VPM), and a process-based Soil Canopy Observation of Photochemistry and Energy flux (SCOPE) model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.

准确量化农田总初级生产力（cropland gross primary production, GPP）对于监测农田状态与碳收支具有重要意义。近数十年来，基于卫星的光能利用率（light-use efficiency, LUE）模型与基于过程的陆地生物圈模型（process-based terrestrial biosphere models, TBMs）已被广泛应用于不同尺度的农田GPP量化研究。然而，GPP的模型估算仍存在较大不确定性，针对农田生态系统的情况尤为突出。近年来，星载日光诱导叶绿素荧光（space-borne solar-induced chlorophyll fluorescence, SIF）展现出从太空监测光合作用的能力，为实际光合作用监测提供了新视角。
本研究以美国内布拉斯加州米德（Mead）地区的玉米（Zea mays L.）样地为研究对象，探究了SIF数据描述玉米物候的潜力，并评估了三种GPP建模方法：星载SIF反演结果、基于光能利用率的植被光合作用模型（Vegetation Photosynthesis Model, VPM），以及受SIF约束的基于过程的光化学与能量通量土壤冠层观测模型（Soil Canopy Observation of Photochemistry and Energy flux, SCOPE）。
研究结果表明，相较于卫星植被指数（增强型植被指数EVI与地表水分指数LSWI），SIF能够更精准地捕捉涡度相关塔观测的总初级生产力（GPP_EC）的季节动态，尤其在生长季初期与末期表现更佳。因此，SIF与GPP_EC的相关性显著强于EVI和LSWI。在生长季内以GPP_EC为参照对GPP估算结果进行评估，三种建模方法均能合理估算玉米GPP，其中SCOPE模型、VPM模型与SIF模型的皮尔逊相关系数（Pearson's correlation coefficients, r）分别为0.97、0.94与0.93。当通过优化最大羧化速率（maximum carboxylation capacity, Vcmax）这一关键参数引入SIF观测数据后，SCOPE模型对玉米GPP的模拟效果最优。
本研究结果证实，SIF数据可为探究光合活动的季节动态提供新途径，可用于约束基于过程的模型以提升GPP估算精度，同时无需依赖气候输入数据与卫星植被指数，即可通过整合SIF与GPP_EC数据实现合理的GPP估算。