The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests

Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites of satellite data, ranging from 8 days to 16 days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with in situ leaf unfolding (LU) of trembling aspen (Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date. In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to in situ LU dates ranged between 13 and 40 days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (~ 13 to 20 days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to − 5 days) than the SOS estimates from NDVI composites that use the midpoint (− 2 to − 27 days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both, SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28 days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing in situ phenology networks to improve the validation of satellite derived green-up dates.     

Estimation of winter wheat phenology under the influence of cumulative temperature and soil salinity in the Yellow River Delta,China, using MODIS time-series data

Understanding the impact of environmental factors on crop phenology is significant in predicting crop growth stages, agricultural decision-making, and yield estimation. Here, using Moderate Resolution Imaging Spectroradiometer time-series data, we present phenological detection mechanisms and an explanation for the phenological variability linked to environmental drivers, such as cumulative temperature and soil salinity, for winter wheat (Triticum aestivum L.) in the Yellow River Delta in 2013. The 8-day normalized difference vegetation index was fitted to a double Gaussian function. Phenological phases, such as the green-up and heading phases, were extracted using maximum curvature approaches. The spatial characteristics of the phenological patterns were investigated. The relationships between the phenological phases and cumulative temperature were explored. Then, the relationships between the phenological phases and soil salinity were evaluated by selecting sites with similar soil fertility and temperature forcing. This study concluded that the regional average green-up date occurred on 5 March, and the regional average heading date occurred on 9 May. The spatial distributions of the green-up and heading phases showed a gradual delay from the southwest to the northeast and from the south to the north. The green-up phase lagged 4–5 days for every 10 degree days that the cumulative temperature decreased. The heading phase lagged 1–2 days for every 10 degree days that the cumulative temperature decreased. The green-up phase in a non-salinization region might be approximately 5–9 days earlier than that in a severe or moderate salinization region. The heading phase in a severe region might occur approximately 1–8 days earlier than that in a non-salinization or moderate salinization region. The method proposed in this article may be useful for understanding the impact of temperature and soil salinity on phenology and could be used to better manage winter wheat in coastal salinization areas.     

Green leaf phenology at Landsat resolution: Scaling from the field to the satellite

Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations (r² = 0.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10-14 days over short distances (< 500 m). These dramatic gradients occur in of low-relief (< 40 m) upland regions. The patterns suggest that microclimates resulting from springtime cold-air drainage may be influential in governing the start of leaf growth; every 4.16 m loss in elevation delayed spring leaf onset by 1 day. These microclimates may be of crucial importance in interpreting in situ records and interpolating phenology from satellite data. Regional patterns from the Landsat analyses suggest topographic, coastal, and land-use controls on phenology. Our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5-7 days earlier than comparable rural areas. The platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows an effective scaling from plot to satellite phenological observations.     

The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India

India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The phenology of these natural vegetation types is often controlled by climatic condition. Estimating phenological variables will help in understanding the response of tropical and subtropical vegetation to climate change. The study investigated the annual and inter-annual variation in vegetation phenology in India using satellite remote sensing. The study used time-series data of the only available satellite measured index of terrestrial chlorophyll content (MERIS Terrestrial Chlorophyll Index) from 2003 to 2007 at 4.6 km spatial resolution. A strong coincidence was observed with expected phenological pattern, in particular, in inter-annual and latitudinal variability of key phenological variables. For major natural vegetation type the onset of greenness had greater latitudinal variation compared to the end of senescence and there was a small or no leafless period. In the 2003-04 growing season a late start for the onset of greenness was detected at low-to-mid latitudes and it was attributed to the extreme cold weather during the early part of 2003. The length of growing season varied from east to west for the major cropping areas in the Indo-Gangetic plain, for both the first and second crops. For the first time, this study attempted to establish a broad regional phenological pattern for India using remotely sensed estimation of canopy chlorophyll content using five years of data. The overall patterns of phenological variables detected from this study broadly coincide with the pattern of natural vegetation phenology revealed in earlier community level studies. The results of this study suggest the need for an organised network combining ground and space observation which is at presently missing in India.     

Extracting grassland vegetation phenology in North China based on cumulative SPOT-VEGETATION NDVI data

Plant phenology is one of the main indicators of climate or other environmental processes. This paper assesses the detection accuracy of start of season (SOS) and end of season (EOS) for grassland vegetation in north China from 2001 to 2010 using SPOT-VEGETATION normalized difference vegetation index (NDVI) data sets and in situ observations. The cumulative NDVI is calculated and fitted using a logistic model to identify phenological transition dates. The curvature of the fitted logistic models predicts phenological transition dates that correspond to the times at which the curvature in the yearly integrated NDVI exhibits local minimums or maximums. Validating with in situ observations, phenological dates are extracted from satellite time series data and are accurate to within 10 days. The spatial trends of SOS and EOS are very similar for 2001–2010. SOS mainly occurs from the day of year (DOY) 110 to DOY 170, and EOS occurs from DOY 240 to DOY 300. SOS displays a marked delay from south to north, while EOS gradually advances, indicating regional differences in climate and terrain. However, the effect of latitude and longitude on the average EOS of alpine grasslands is not significantly different, while SOS at low latitude and high longitude is 10 days earlier than at high-latitude and high-longitude regions. We detected an overall advance in SOS of 3.1 days over 10 years, and a 1.3-day delay in EOS. However, the amplitude is low (about 5 days) and the changes in most regions are not significant (close to zero). The results in this paper are concordant with many reported studies that explored the phenology of grasslands in North China, which is an important component of global grasslands science.     

Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data

Vegetation phenology is the chronology of periodic phases of development. It constitutes an efficient bio-indicator of impacts of climate changes and a key parameter for understanding and modelling vegetation-climate interactions and their implications on carbon cycling. Numerous studies were devoted to the remote sensing of vegetation phenology. Most of these were carried out using data acquired by AVHRR instrument onboard NOAA meteorological satellites. Since 1999, multispectral images were acquired over the whole earth surface every one to two days by MODIS instrument onboard Terra and Aqua platforms. In comparison with AVHRR, MODIS constitutes a significant technical improvement in terms of spatial resolution, spectral resolution, geolocation accuracy, atmospheric corrections scheme and cloud screening and sensor calibration. In this study, 250 m daily MODIS data were used to derive precise vegetation phenological dates over deciduous forest stands. Phenological markers derived from MODIS time-series and provided by MODIS Global Land Cover Dynamics product (MOD12Q2) were compared to field measurements carried out over the main deciduous forest stands across France and over five years. We show that the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands. At plot level, the prediction uncertainty is 8.5 days and the bias is 3.5 days. MODIS Global Land Cover Dynamics MOD12Q2 provides estimates of onset of green-up dates which deviate substantially from in situ observations and do not perform better than the null model. RMSE values are 20.5 days (bias -17 days) using the onset of greenness increase and 36.5 days (bias 34.5 days) using the onset of greenness maximum. An improvement of prediction quality is obtained if we consider the average of MOD12Q2 onset of greenness increase and maximum as marker of onset of green-up date. RMSE decreases to 16.5 days and bias to 7.5 days.     

Temporal and spatial variabilities of total ozone column over Portugal

This paper focuses on the spatial-temporal structure of total ozone column (TOC) over Portugal. This relevant region of southwestern Europe has not been evaluated yet in detail due to the lack of continuous and well-covered ground-based TOC measurements. The data used in this study are derived from the NASA's Total Ozone Mapping Spectrometer (TOMS) for the period 1978-2005. The TOC spatial behavior shows no significant longitudinal variability (smaller than 3%). In contrast, the variation in latitude changes between 3.5% and 6% depending on the calendar month. The TOC in the northern Portugal is, on average, higher than that recorded in the South. The temporal variability was analyzed for three scales: long-term, seasonal and short-term. The long-term TOC changes are analyzed between 1978 and 1999 by means of linear least squares fits. The results show an annual TOC trend of (−2.65 ± 0.70)%/decade which is statistically significant at the 95% confidence level. This TOC decrease is smaller than the trends obtained in other midlatitudes regions which could be partially explained by the compensation due to the observed increase in the tropospheric ozone over the Iberian Peninsula. A trend analysis performed for each individual month shows a statistically significant TOC decline between March and October, with a maximum linear trend value of (−7.30 ± 1.45)%/decade in May. The amplitude of the seasonal TOC cycle over Portugal shows a slight dependence in latitude, varying from 28.6 DU (37.5° N) to 33.6 DU (41.5° N). Finally, the short-term variability showed a notable seasonal behavior, with maximum day-to-day TOC changes in winter (~ 6%) and minimum in summer (~ 3%). In addition, the persistence (characterized by the autocorrelation coefficients) strongly decreases after a few days (except in summer months).     

Fire-induced changes in green-up and leaf maturity of the Canadian boreal forest

Recent studies of vegetation phenology of northern forests using satellite data suggest that the observed earlier spring increase and peak amplitude of the normalized difference vegetation index (NDVI) are a result of climate warming. In addition to undergoing an increase in temperature, the northern forests of Canada have also seen a dramatic increase in area burned by wildfire over the same time period. Using the Canadian Large Fire Database, we analyzed the impact fire had on the phenological dates derived from fitting a logistical model to yearly data from 2004 for several different subsets of both AVHRR-NDVI and MODIS LAI in wildfire dominated terrestrial ecozones. Fire had a significant but complex effect on estimated phenology dates. The most recently burned areas (1994–2003) had later green-up dates in two ecozones for AVHRR data and all ecozones for MODIS. However, older forested (not burned during 1980–2003) had estimated green-up dates 1 to 9 days earlier than the entire forested area in the MODIS LAI data. These data corroborate studies in Canada and demonstrate that fire history is influencing boreal forest phenology and growing season LAI.     

Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT/VEGETATION time-series

In mountain forest ecosystems where elevation gradients are prominent, temperature gradient-based phenological variability can be high. However, there are few studies that assess the capability of remote sensing observations to monitor ecosystem phenology along elevation gradients, despite their relevance under climate change. We investigated the potential of medium resolution remotely sensed data to monitor the elevation variations in the seasonal dynamics of a temperate deciduous broadleaf forested ecosystem. Further, we explored the impact of elevation on the onset of spring leafing. This study was based on the analysis of multi-annual time-series of VEGETATION data acquired over the French Pyrenees Mountain Region (FPMR), in conjunction with simultaneous ground-based observations of leaf phenology made for two dominant tree species in the region (oak and beech). The seasonal variations in the perpendicular vegetation index (PVI) were analyzed during a five-year period (2002 to 2006). The five years of data were averaged into a one sole year in order to fill the numerous large spatio-temporal gaps due to cloud and snow presence - frequent in mountains - without altering the temporal resolution. Since a VEGETATION pixel (1 km²) includes several types of land cover, the broadleaf forest-specific seasonal dynamics of PVI was reconstructed pixel-by-pixel using a temporal unmixing method based on a non-parametric statistical approach. The spatial pattern of the seasonal response of PVI was clearly consistent with the relief. Nevertheless the elevational or geographic range of tree species, which differ in their phenology sensitivity to temperature, also has a significant impact on this pattern. The reduction in the growing season length with elevation was clearly observable from the delay in the increase of PVI in spring and from the advance of its decrease in the fall. The elevation variations in leaf flushing timing were estimated from the temporal change in PVI in spring over the study area. They were found to be consistent with those measured in situ (R2 > 0.95). It was deduced that, over FPMR, the mean delay of leaf flushing timing for every 100 m increase in elevation was estimated be approximately 2.3 days. The expected estimation error of satellite-based leaf unfolding date for a given elevation was approximately 2 days. This accuracy can be considered as satisfactory since it would allow us to detect changes in leafing timing of deciduous broadleaf forests with a magnitude equivalent to that due to an elevation variation of 100 m (2.3 days on average), or in other words, to that caused by a variation in the mean annual air temperature of 0.5 °C. Although averaging the VEGETATION data over five years led to a loss of interannual information, it was found to be a robust approach to characterise the elevation variations in spring leafing and its long-term trends.     

Interannual vegetation phenology estimates from global AVHRR measurements: Comparison with in situ data and applications

In a previous paper, we described a procedure to correct the directional effects in AVHRR reflectance time series. The corrected measurements show much less high frequency variability than their original counterparts, which makes them suitable to study vegetation dynamics. The time series are used here to estimate the start and ending dates of the growing season for 18 years from 1982 to 1999. We focus on the interannual variations of these phenological parameters.A database of in situ phenology observations is used to quantify the accuracy of the satellite-based estimates. Although based on a limited sampling of the Northern mid and high latitudes, the comparison indicates that i) the satellite phenological product contains meaningful information on interannual onset anomalies; ii) there is a higher degree of consistency over regions covered by Broadleaf Forests, Grasses and cereal Crops than over those covered by Needleleaf Forests or Savannas; and iii) the satellite phenological product is of lower quality in regions with mountainous terrain. In favorable conditions, interannual variations of the onset are captured with an accuracy of a few days.As this satellite-derived product captures the interannual onset variability at ground-truth sites, we confidently use it to larger scales studies. Mapped at a continental scale, the onset anomalies show coherent patterns at the regional (≈ 1000 km) scale for the mid and high latitudes of the Northern hemisphere, which is consistent with a meteorological forcing. In the tropics, there is larger spatial heterogeneity, which suggests more complex controls of the phenology. The relation between vegetation phenology and climate is further investigated over Europe by comparing the variability of the satellite-derived vegetation onset and that of the winter North Atlantic Oscillation index, at a fine spatial scale. The strong correlations observed confirm that this climate forcing parameter explains most of the onset variability over a large fraction of Northern Europe (earlier onsets for positive winter NAO), with lower impact towards the south and opposite effects around the Mediterranean basin. The NAO has a predictive character as the January-February NAO index is strongly correlated with the vegetation onset that occurs around April in Northern Europe.     

The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa

In this study, we test whether we can significantly (p < 0.05) distinguish cotton (Gossypium hirsutum L.) fields from maize (Zea mays L.) and sorghum (Sorghum bicolor) fields in smallholder agricultural landscapes of the Mid-Zambezi Valley, Zimbabwe, using a temporal series of 16-day Moderate Resolution Imaging Spectroradiometer – normalized difference vegetation index (MODIS NDVI) data. We test this hypothesis at different phenological stages over the growing season, that is, early green-up onset, late green-up onset, green-peak, early senescence and late senescence. We also statistically compare the rate of change in the greenness of the three crops at the three phenological stages. Results show that we can significantly (p < 0.05) distinguish cotton fields from maize and sorghum fields using 16-day MODIS NDVI data during the late green-up onset as well as during the green-peak stage of the three crops. Our results indicate that cotton can successfully be distinguished from maize and sorghum in spatially heterogeneous smallholder agricultural landscapes using temporal MODIS NDVI.     

Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model

Landsat imagery with a 30 m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 16 days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 250 m, 500 m, and 1000 m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 500 m, and Landsat (at 30 m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x? = 0.086, σ = 0.088, broadleaf x? = 0.019, σ = 0.079, coniferous x? = 0.039, σ = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r² = 0.76 for the NIR band; r² = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.     

基于多源数据的禹城农田生态系统冬小麦生育期识别方法比较研究

多源数据与其技术方法逐渐被应用于植被物候的研究当中,但基于多源数据物候识别方法间的差异性比较及定量化评估工作还有待加强。以山东禹城农田生态系统为例,探讨了基于多源数据,NDVI、EVI、数字相机图片、碳通量数据(NEE)以及人工实测数据获取的冬小麦主要生育日期的结果进行差异比较及定量化评估。结果表明:①通过碳通量数据获取的主要生育日期的计算结果与人工实测结果最接近,各阶段差异均<3 d;通过数字相机图片获取的结果仅次于通过碳通量数据获取的结果,而通过遥感数据NDVI、EVI获取的结果与人工实测结果差距最大;②通过NDVI、EVI两种数据获取的冬小麦主要生育期结果具有极显著的相关性,最高达到R²=0.857(P<0.001);③基于多源数据获取的冬小麦主要生育期的计算结果,均显示出禹城站冬小麦返青期提前,蜡熟期推迟,生长季长度变长的年际变化特征。     

The effects of phenology on indirect measures of aboveground biomass in annual grasses

Remote sensing is increasingly being used to quantify vegetation biomass across large areas, often with algorithms based on calibrated relationships between biomass and indices such as the normalized difference vegetation index (NDVI). To improve capacity to evaluate grassland dynamics over time, we examined the influence of phenological changes on NDVI–biomass relationships in annual grasses. Our findings support the use of NDVI throughout early growth and the beginning stages of canopy maturation, but suggest caution for later stages. In contrast, measurements of fractional photosynthetically active radiation (fAPAR) absorbed by the canopy and leaf area index (LAI) served as good season‐long surrogates for canopy biomass. Canopies reached maximum biomass approximately 40 days after maximum greenness, with biomass increasing by approximately 20% during senescence. For multi‐year studies of management impact (i) avoid using seasonal comparisons from dates much after the point of maximum greenness or (ii) consider non‐NDVI‐based approaches.     

Satellite-driven estimation of terrestrial carbon flux over Far East Asia with 1-km grid resolution

The terrestrial carbon cycle is strongly affected by natural phenomena, terrain heterogeneity, and human-induced activities that alter carbon exchange via vegetation and soil activities. In order to accurately understand terrestrial carbon cycle mechanisms, it is necessary to estimate spatial and temporal variations in carbon flux and storage using process-based models with the highest possible resolution. We estimated terrestrial carbon fluxes using a biosphere model integrating eco-physiological and mechanistic approaches based on satellite data (BEAMS) and observations with 1-km grid resolution. The study area is the central Far East Asia region, which lies between 30° and 50° north latitude and 125° and 150° east longitude. Aiming to simulate terrestrial carbon exchanges under realistic land surface conditions, we used as many satellite-observation datasets as possible, such as the standard MODIS, TRMM, and SRTM high-level land products. Validated using gross primary productivity (GPP), net ecosystem production (NEP), net radiation and latent heat with ground measurements at six flux sites, the model estimations showed reasonable seasonal and annual patterns. In extensive analysis, the total GPP and NPP were determined to be 2.1 and 0.9 PgC/year, respectively. The total NEP estimation was + 5.6 TgC/year, meaning that the land area played a role as a carbon sink from 2001 to 2006. In analyses of areas with complicated topography, the 1-km grid estimation could prove to be effective in evaluating the effect of landscape on the terrestrial carbon cycle. The method presented here is an appropriate approach for gaining a better understanding of terrestrial carbon exchange, both spatially and temporally.     

Copper oxide nanoparticle sensors for hydrogen cyanide detection: Unprecedented selectivity and sensitivity

In the current work, CuO nanoparticles were synthesized in a facile way, and characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and thermogravimetry. Using these CuO nanoparticles, CuO functionalized QCM resonators were fabricated and explored for HCN sensing. The sensing properties were studied by a sensor characterization system coupled with a mass spectrometer. The sensor response to HCN (+347 Hz) was found to be in an opposite direction as compared with other common volatile substances (ether: −1230 Hz; water: −1815 Hz; n-hexane: −2100 Hz; benzene: −3410 Hz; acetic acid: −4840 Hz; ethanol: −6270 Hz), offering excellent selectivity for HCN detection. In addition, the sensitivity (15.1 Hz/μg) was very high, and the response (30 s) and recovery (750 s) were very fast. A sensing mechanism was proposed based on experimental results, in which a surface redox reaction occurs between CuO and Cu₂O on the nanoparticle reversibly upon contact with HCN and air, respectively. The current results would provide an exciting alternative to fast, sensitive and selective detection of trace HCN, which would be of particular benefit in the area of public security and environmental applications.     

Landsat-based inventory of glaciers in western Canada, 1985-2005

We report on a glacier inventory for the Canadian Cordillera south of 60°N, across the two western provinces of British Columbia and Alberta, containing ~ 30,000 km² of glacierized terrain. Our semi-automated method extracted glacier extents from Landsat Thematic Mapper (TM) scenes for 2005 and 2000 using a band ratio (TM3/TM5). We compared these extents with glacier cover for the mid-1980s from high-altitude, aerial photography for British Columbia and from Landsat TM imagery for Alberta. A 25 m digital elevation model (DEM) helped to identify debris-covered ice and to split the glaciers into their respective drainage basins. The estimated mapping errors are 3-4% and arise primarily from seasonal snow cover. Glaciers in British Columbia and Alberta respectively lost − 10.8 ± 3.8% and − 25.4% ± 4.1% of their area over the period 1985-2005. The region-wide annual shrinkage rate of − 0.55% a^− 1 is comparable to rates reported for other mountain ranges in the late twentieth century. Least glacierized mountain ranges with smaller glaciers lost the largest fraction of ice cover: the highest relative ice loss in British Columbia (− 24.0 ± 4.6%) occurred in the northern Interior Ranges, while glaciers in the northern Coast Mountains declined least (− 7.7 ± 3.4%).     

Sampling biases in MODIS and SeaWiFS ocean chlorophyll data

Although modern ocean color sensors, such as MODIS and SeaWiFS, are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a “truth field”, which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25° longitude by 0.67° latitude.The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and > 5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (< 3%) in the mid-latitudes (between − 40° and 40°). Low interannual variability in the global annual mean sampling biases suggested that global scale trend analyses were valid.High latitude biases were much higher than the global annual means, up to 20% as a basin annual mean, and over 80% in some months. This was the result of the high solar zenith angle exclusion in the processing algorithms. Only data where the solar angle is < 75° are permitted, in contrast to the assimilation which samples regularly over the entire area and month. High solar zenith angles do not facilitate phytoplankton photosynthesis and low chlorophyll concentrations occurring here are missed by the data sets. Ocean color sensors selectively sample in locations and times of favorable phytoplankton growth, producing overestimates of chlorophyll.The biases derived from lack of sampling in the high latitudes varied monthly, leading to artifacts in the apparent seasonal cycle from ocean color sensors. A false secondary peak in chlorophyll occurred in May-August, which resulted from lack of sampling in the Antarctic.Persistent clouds, characteristic in the North Pacific, also produced overestimates, again by selectively sampling only the high growth periods. In contrast, areas characterized by thick aerosols showed chlorophyll underestimates to nearly − 30% in basin monthly means. This was the result of selective sampling in lower aerosol thickness periods, which corresponded with lower phytoplankton growth periods.A combination of MODIS and SeaWiFS sampling was most effective at reducing mid-latitude biases due to inter-orbit gaps, sun glint, and sensor tilt changes. But these biases were low using a single sensor, suggesting multiple sensors had little effect in reducing global and regional monthly and annual mean biases.Ocean color data are an invaluable source of information about global biological processes. However, these results suggest that sampling errors need to be considered in applications involving global and regional mean chlorophyll biomasses as well as seasonal variability and regional trend analysis.     

Response of seasonal vegetation development to climatic variations in eastern central Asia

Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and December 1991. Monthly precipitation and mean temperature data (1982-1990) were acquired from 19 meteorological stations throughout the grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50°N) experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert (40°N-50°N). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil moisture availability of specific sites. Plant stress caused by drought was the most significant contributor to later vegetation green-up as observed from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming.     

Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999

We used land surface temperature (LST) algorithms and NDVI values to estimate changes in vegetation in the European continent between 1982 and 1999 from the Pathfinder AVHRR Land (PAL) dataset. These two parameters are monitored through HANTS (Harmonic ANalysis of Time Series) software, which allows the simultaneous observation of mean value, first harmonic amplitude and phase behaviors in the same image. These results for each complete year of data show the effect of volcanic aerosols and orbital drift on PAL data. Comparison of time series of HANTS cloud-free time series with the original time series for various land cover proves that this software is useful for LST analysis, although primarily designed for NDVI applications. Comparison of yearly averages of HANTS LST over the whole Europe with air temperature confirms the validity of the results. Maps of the evolution for both parameters between periods 1982/1986 and 1995/1999 have been elaborated: NDVI data show the well confirmed trend of increase over Europe (up to 0.1 in NDVI), Southern Europe seeing a decrease in NDVI (− 0.02). LST averages stay stable or slightly decrease (up to − 1.5 K) over the whole continent, except for southern areas for which the increase is up to 2.5 K. These results evidence that arid and semi-arid areas of Southern Europe have become more arid, the rest of Europe seeing an increase in its wood land proportion, while seasonal amplitude in Northern Europe has decreased.