Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, R_S), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and R_S are irreconcilable. When we estimate GPP based on R_S measurements and some assumptions about R_S:GPP ratios, we found the resulted global GPP values (bootstrap mean [Formula: see text] Pg C yr^-1) are significantly higher than most GPP estimates reported in the literature ([Formula: see text] Pg C yr^-1). Similarly, historical GPP estimates imply a soil respiration flux (Rs_GPP, bootstrap mean of [Formula: see text] Pg C yr^-1) statistically inconsistent with most published R_S values ([Formula: see text] Pg C yr^-1), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R_S:GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.

Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models

Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.

Recent Progress in Quantitative Land Remote Sensing in China

During the past forty years, since the first book with a title mentioning quantitative and remote sensing was published [1], quantitative land remote sensing has advanced dramatically, and numerous books have been published since then [2–6] although some of them did not use quantitative land remote sensing in their titles. [...]