Abstract
Sun-induced chlorophyll fluorescence (SIF) provides a new method for monitoring vegetation photosynthesis from space and has been widely used to estimate gross primary productivity (GPP). However, the ability of SIF obtained from the Orbital Carbon Observatory 2 (OCO-2 SIF) and Global Ozone Monitoring Experiment-2 (GOME-2) to estimate GPP in the cold and arid region of Heihe River Basin remains unclear because previous comparisons were insufficient. Here, we choose maize and alpine meadow to evaluate the performance of SIF obtained by OCO-2 and GOME-2 in GPP estimations. The results of this study show that daily SIF757 had stronger correlations with daily tower GPP than daily SIF771, and the correlation between daily SIF757 and daily tower GPP was stronger than the correlation between 16-d averaged SIF740 and 16-d averaged tower GPP. The 16-d averaged absorbed photosynthetically active radiation (APAR) and reconstructed sun-induced fluorescence (RSIF) had the strongest linear correlations with 16-d averaged tower GPP. GPP_VPM and GPP_RSIF exhibited the best performance in GPP estimation, closely followed by GPP_SIF757, then GPP_SIF771 and GPP_ SIF740. We also found that the robustness of the correlation coefficients of OCO-2 SIF with GOME-2 SIF was highly dependent on the size of their spatial footprint overlaps, indicating that the spatial differences between OCO-2 and GOME-2 footprints contribute to the differences in GPP estimates between OCO-2 and GOME-2. In addition, the differences of viewing zenith angle (VZA), cloud contamination, scale effects, and environmental scalars (Tscalar × Wscalar) can result in differences between OCO-2 SIF and GOME-2 SIF.
Highlights
Plant photosynthesis, termed gross primary productivity (GPP), is the main source of energy for all life on Earth [1], and it drives ecosystem functions and carbon cycling [2]
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We evaluated the performance of SIF obtained by OCO-2 and GOME-2 in the estimation of GPP for maize and alpine meadow areas at the Daman and A’rou sites
Summary
Plant photosynthesis, termed gross primary productivity (GPP), is the main source of energy for all life on Earth [1], and it drives ecosystem functions and carbon cycling [2]. In traditional remote sensing GPP estimation methods, GPP is calculated using light use efficiency (LUE) models based on vegetation indices (such as the normalized difference vegetation index [NDVI] and enhanced vegetation index [EVI]) and auxiliary meteorological data (such as temperature and solar radiation) as inputs. Vegetation indices based on reflectance can track the greenness well, they are less sensitive to actual variations in photosynthesis [9,10,11]. Vegetation indices such as NDVI cannot reflect diurnal physiological changes caused by heat and water stress [12]. With the rapid development of remote sensing techniques, machine learning methods have become a very useful tool to process GPP-related data. The basal areas of spruce and fir were mapped by using machine learning techniques in conjunction with remote sensing and measurement data in Central Siberia [15]
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