Satellite observations of terrestrial ecosystems and links to climate and carbon cycle

Satellite observations of terrestrial ecosystems and links to climate and carbon cycle Bases of remote sensing of vegetation canopies The Greening trend Land use, land use change Satellite and Models Estimations of GPP Assimilation Conclusions

Use remote sensing to measure: • Reflectance • related to chemical, physical properties of surface • Emittance • brightness temperature (IR part of spectrum) • Backscatter • From active sensor (RADAR or LiDAR – light detection and ranging) • Related to structure and physical properties of objects on surface

upper epidermis palisade layer spongy tissue lower epidermis image credit: Govaerts. reflectance spectrum of a green leaf Pigments in green leaves (notably chlorophyll) absorb strongly at red and blue wavelengths. Lack of such absorption at near-infrared wavelengths results in strong scatter from leaves.

Satellite view of a forest. Reflectance Ratio is VERY convenient... NDVI = (NIR-RED)/(NIR+RED) is related to ‘ greeness ’.

MODIS

A)links between satellite reflectance and vegetation parameter ‘greeness’, Leaf Area Index, chlorophyll content, N content uses Radiative transfer in the canopy B)Canopy structure, leaf properties allow classification of land cover

Questions : 1) Can we detect climate change impact on ecosystems ? 2) From space ? At large scale ? Answers : 1) yes. For example, birds, plants, insect phenology has changed. Spring is earlier by a few days. 2) we ’ll see

R. Myneni delayed fall earlier spring Jan Jul Aug Dec Jan Jul Aug Dec In the north, where vegetation growth is seasonal, the cumulative growing season greenness, which is the area under the NDVI curve, can change either due to a longer photosynthetically active growing season or due to increased greenness magnitude, or both. changes in growing season duration changes in greenness magnitude Increase Use NDVI threshold to assess changes in dates of spring green-up and autumn green- down (assess sensitivity to threshold value) Assess changes in peak seasonal greenness from July and August average NDVI

Analysis of GIMMS (v1) ndvi data for the period 1981 to 1999 NDVI averaged over boreal growing season months increased by about 10%, the timing of spring green-up advanced by about 6 days. A larger increase a longer active growing season are observed in Eurasia relative to North America 11.9 days/18 yrs (p<0.05) 8.4%/18 yrs (p<0.05) 12.4%/18 yrs (p<0.05) 17.5 days/18 yrs (p<0.05) From Zhou et al., (JGR, 106(D17):20069-20083, 2001)

Analyses of pixel-based persistence indices from GIMMS (v1) NDVI data for the period 1981 to 1999 About 61% of the total vegetated area between 40N-70N in Eurasia shows a persistent increase in growing season NDVI over a broad contiguous swath of land from Central Europe through Siberia to the Aldan plateau, where almost 58% (7.3 million km2) is forests and woodlands. North America, in comparison, shows a fragmented pattern of change, notable only in the forests of the southeast and grasslands of the upper Midwest. From Zhou et al., (JGR, 106(D17):20069-20083, 2001)

longer growing seasons from warming in the northern latitudes possibly explain some of the changes, with a role also for : increased incidences of fires and infestations fire suppression and forest re-growth changing harvests Changes in silviculture forest expansion and re-growth

Do we detect snow (only) ? Mognard TRENDS IN SNOW MELT from 1979-1997 SMMR AND SSM/I

The temporal changes and continental differences in NDVI are consistent with ground based measurements of temperature, an important determinant of biological activity in the north From Zhou et al., (JGR, 106(D17):20069-20083, 2001)

Classification and land use change Landsat image

1975, 1986, 1992 : deforestation, and some forest regrowth

Satellites bring constraints for the past 2 decades.

Modelling ecosystems function with satellite data Data as an input Estimation of photosynthesis from fPAR Data assimilation Assimilation of AVHRR data in a vegetation/SVAT model

Photosynthesis from remote sensing and weather data ERA40 GPP= LUE.*fPAR*PAR NDVI temporal and spatial variability APAR (mol.m-2d-1) LUE is estimated with experimental values

Trend in photosynthesis from 1982 to 1999 R. Nemanni et al. 2003

Data assimilation Satellite data are radiative only Biophysical parameters (fPAR) are derived from inverse techniques e.g. What Leaf Area Index gives such reflectances ? Assimilation extends such inverse technique to the whole vegetation model. - Look for the best agreement between model and data by correcting ‘ errors ’ (variable, parameters) - benefit from the model knowledge - requires good knowledge of errors !!!

Cayrol et al. (2000) Fluxes before and after assimilation of AVHRR data Model

Some simple concluding remarks We are in a data rich period for Earth Observation Archives are rare … but already show signs of climate change global impact. Some are free ! Use (with caution...) Networks allow calibration/validation of satellite products Lots to be done !

Don’t forget there’s a bonfire tonight. Hopefully, it will not be large enough to be detected ! Celebrate Hevelius famous polish astronomer … and famous « beer-maker » too !

Satellite observations of terrestrial ecosystems and links to climate and carbon cycle