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 show...准确量化农田总初级生产力（Gross Primary Production, GPP），对于监测农田状况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（Terrestrial Biosphere Models, TBMs）已被广泛应用于多尺度农田GPP的量化研究。然而，GPP的模型估算结果仍存在较大不确定性，针对农田的此类不确定性尤为突出。近年来，星载日光诱导叶绿素荧光（Solar-induced Chlorophyll Fluorescence, SIF）展现了从太空监测光合作用的能力，为实际光合监测提供了全新视角。本研究探究了SIF数据描述玉米物候的潜力，并在美国内布拉斯加州米德的玉米（Zea mays L.）试验点，对三种GPP建模方法展开评估：星载SIF反演结果、基于LUE的植被光合模型（Vegetation Photosynthesis Model, VPM），以及受SIF约束的基于过程的土壤-冠层光化学与能通量观测（Soil Canopy Observation of Photochemistry and Energy flux, SCOPE）模型。研究结果表明……