Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.

Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009

Vegetation greenness has been increasing globally since at least 1981, when satellite technology enabled large-scale vegetation monitoring. The greening phenomenon, together with warming, sea-level rise and sea-ice decline, represents highly credible evidence of anthropogenic climate change. In this Review, we examine the detection of the greening signal, its causes and its consequences. Greening is pronounced over intensively farmed or afforested areas, such as in China and India, reflecting human activities. However, strong greening also occurs in biomes with low human footprint, such as the Arctic, where global change drivers play a dominant role. Vegetation models suggest that CO2 fertilization is the main driver of greening on the global scale, with other factors being notable at the regional scale. Modelling indicates that greening could mitigate global warming by increasing the carbon sink on land and altering biogeophysical processes, mainly evaporative cooling. Coupling high temporal and fine spatial resolution remote-sensing observations with ground measurements, increasing sampling in the tropics and Arctic, and modelling Earth systems in more detail will further our insights into the greening of Earth. Vegetation on Earth is increasing, potentially leading to a larger terrestrial carbon sink. In this Review, we discuss the occurrence of this global greening phenomenon, its drivers and how it might impact carbon cycling and land-atmosphere heat and water fluxes.

/pdf/characteristics-drivers-and-feedbacks-of-global-greening-19nigyrijp.pdf

Characteristics, drivers and feedbacks of global greening

. This paper describes the latest version of the algorithm MAIAC
used for processing the MODIS Collection 6 data record. Since initial
publication in 2011–2012, MAIAC has changed considerably to adapt to global
processing and improve cloud/snow detection, aerosol retrievals and
atmospheric correction of MODIS data. The main changes include (1) transition
from a 25 to 1 km scale for retrieval of the spectral regression coefficient
(SRC) which helped to remove occasional blockiness at 25 km scale in the
aerosol optical depth (AOD) and in the surface reflectance, (2) continuous
improvements of cloud detection, (3) introduction of smoke and dust tests to
discriminate absorbing fine- and coarse-mode aerosols, (4) adding over-water
processing, (5) general optimization of the LUT-based radiative transfer for
the global processing, and others. MAIAC provides an interdisciplinary suite
of atmospheric and land products, including cloud mask (CM), column water
vapor (CWV), AOD at 0.47 and 0.55  µ m, aerosol type (background,
smoke or dust) and fine-mode fraction over water; spectral bidirectional
reflectance factors (BRF), parameters of Ross-thick Li-sparse (RTLS)
bidirectional reflectance distribution function (BRDF) model and
instantaneous albedo. For snow-covered surfaces, we provide subpixel snow
fraction and snow grain size. All products come in standard HDF4 format at
1 km resolution, except for BRF, which is also provided at 500 m resolution
on a sinusoidal grid adopted by the MODIS Land team. All products are
provided on per-observation basis in daily files except for the BRDF/Albedo
product, which is reported every 8 days. Because MAIAC uses a time
series approach, BRDF/Albedo is naturally gap-filled over land where missing
values are filled-in with results from the previous retrieval. While the BRDF
model is reported for MODIS Land bands 1–7 and ocean band 8, BRF is reported
for both land and ocean bands 1–12. This paper focuses on MAIAC cloud
detection, aerosol retrievals and atmospheric correction and describes MCD19
data products and quality assurance (QA) flags.

/pdf/modis-collection-6-maiac-algorithm-5bx8893p8y.pdf

MODIS Collection 6 MAIAC Algorithm

Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000-2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies.

/pdf/a-global-moderate-resolution-dataset-of-gross-primary-5bcyjad1em.pdf

A global moderate resolution dataset of gross primary production of vegetation for 2000–2016

Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?

. The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Angstrom exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and Terra–Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1% of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1–B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

https://amt.copernicus.org/articles/7/4353/2014/amt-7-4353-2014.pdf

A. Wu

Papers

Scientific Impact of MODIS C5 Calibration Degradation and C6+ Improvements