We test the relationship between canopy photosynthesis and reflected near‐infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near‐infrared and red radiation from vegetation, with light‐emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near‐infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near‐infrared radiation were highly correlated with measurements of canopy photosynthesis on half‐hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near‐infrared radiation were greatest for the fertilized and irrigated C₄ corn crop, intermediate for the C₃ tules on nutrient‐rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient‐poor, mineral soils. Reflected near‐infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near‐infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications.

The leaf chlorophyll content (Chl_leaf) is a crucial vegetation parameter in carbon cycle modelling and agricultural monitoring at local, regional and global scales. The red‐edge spectral region is sensitive to variations in Chl_leaf. An increasing number of sensors are capable of sampling red‐edge bands, providing opportunities to estimate Chl_leaf. However, the contributions of canopy/foliar/soil factors are always combined in the reflectance signal, which limits the generalizability of vegetation index (VI)‐based Chl_leaf inversions. This study aims to propose a new red‐edge chlorophyll index to decouple the effects of the canopy and soil background from the Chl_leaf estimation. The chlorophyll sensitive index (CSI) was proposed, and the regression equations between the CSI and Chl_leaf were acquired using PROSAIL (PROSPECT + SAIL) and the 4‐Scale‐PROSPECT model. Sensitivity analyses showed that the CSI is resistant to variations in the canopy structure and soil background. Validation results obtained using 308 ground‐measured samples over nine sites world‐wide revealed that CSI improves the Chl_leaf retrieval accuracy (root mean square error (RMSE = 9.39 μg cm⁻²) compared with the existing Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI; RMSE = 13.00 μg cm⁻²). Moreover, the CSI method steadily achieves a highly accurate inversion under different LAI and Chl_leaf conditions. Based on the CSI regression method, a Chl_leaf product with a 30‐m/10‐day resolution across China was generated. The CSI is sensitive to Chl_leaf but resistant to canopy structure and soil moisture parameters, and it has the potential to explicitly retrieve leaf‐scale biochemistry in ecosystem modelling and ecological applications.

The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) algorithm has been successfully implemented for Visible Infrared Imager Radiometer Suite (VIIRS) observations by optimizing a small set of configurable parameters in Look-Up-Tables (LUTs). Our preliminary evaluation showed reasonable agreement between VIIRS and MODIS LAI/FPAR retrievals. However, there is a need for a more comprehensive investigation to assure continuity of multi-sensor global LAI/FPAR time series, as the preliminary evaluation was spatiotemporally limited. In this study, we use a multi-year (2012–2016) global LAI/FPAR product generated from VIIRS and MODIS to evaluate for spatiotemporal consistency. We also quantify uncertainty of the product by utilizing available ground measurements. For both consistency and uncertainty evaluation, we account for variations in biome type and temporal resolution. Our results indicate that the LAI/FPAR retrievals from VIIRS and MODIS are consistent at different spatial (i.e., global and site) and temporal (i.e., 8-day, seasonal and annual) scales. The estimate of mean discrepancy (−0.006 ± 0.013 for LAI and −0.002 ± 0.002 for FPAR) meets the stability requirement for long-term LAI/FPAR Earth System Data Records (ESDRs) from multi-sensors as suggested by the Global Climate Observing System (GCOS). It is noteworthy that the rate of retrievals from the radiative transfer-based main algorithm is also comparable between two sensors. However, a relatively larger discrepancy over tropical forests was observed due to reflectance saturation and an unexpected interannual variation of main algorithm success was noticed due to instability in input surface reflectances. The uncertainties/relative uncertainties of VIIRS and MODIS LAI (FPAR) products assessed through comparisons to ground measurements are estimated to be 0.60/42.2% (0.10/24.4%) and 0.55/39.3% (0.11/26%), respectively. Note that the validated LAI were only distributed in low domains (~2.5), resulting in large relative uncertainty. Therefore, more ground measurements are needed to achieve a more comprehensive evaluation result of product uncertainty. The results presented here generally imbue confidence in the consistency between VIIRS and MODIS LAI/FPAR products and the feasibility of generating long-term multi-sensor LAI/FPAR ESDRs time series.