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.     

Satellite passive microwave remote sensing for monitoring global land surface phenology

Vegetation phenology characterizes seasonal life-cycle events that influence the carbon cycle and land-atmosphere water and energy exchange. We analyzed global phenology cycles over a six year record (2003-2008) using satellite passive microwave remote sensing based Vegetation Optical Depth (VOD) retrievals derived from daily time series brightness temperature (T_b) measurements from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and other ancillary data inputs. The VOD parameter derives vegetation canopy attenuation at a given microwave frequency (18.7 GHz) and varies with canopy height, density, structure and water content. An error sensitivity analysis indicates that the retrieval algorithm can resolve the VOD seasonal cycle over a majority of global vegetated land areas. The VOD results corresponded favorably (p < 0.01) with vegetation indices (VIs) and leaf area index (LAI) information from satellite optical-infrared (MODIS) remote sensing, and phenology cycles determined from a simple bioclimatic growing season index (GSI) for over 82% of the global domain. Lower biomass land cover classes (e.g. savannas) show the highest correlations (R = 0.66), with reduced correspondence at higher biomass levels (0.03 < R < 0.51) and higher correlations for homogeneous land cover areas (0.41 < R < 0.83). The VOD results display a unique end-of-season signal relative to VI and LAI series, and may reflect microwave sensitivity to the timing of vegetation biomass depletion (e.g. leaf abscission) and associated changes in canopy water content (e.g. dormancy preparation). The VOD parameter is independent of and synergistic with optical-infrared remote sensing based vegetation metrics, and contributes to a more comprehensive view of land surface phenology.     

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.     

Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images

Leaf phenology of tropical evergreen forests affects carbon and water fluxes. In an earlier study of a seasonally moist evergreen tropical forest site in the Amazon basin, time series data of Enhanced Vegetation Index (EVI) from the VEGETATION and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors showed an unexpected seasonal pattern, with higher EVI in the late dry season than in the wet season. In this study we conducted a regional-scale analysis of tropical evergreen forests in South America, using time series data of EVI from MODIS in 2002. The results show a large dynamic range and spatial variations of annual maximum EVI for evergreen forest canopies in the region. In tropical evergreen forests, maximum EVI in 2002 typically occurs during the late dry season to early wet season. This suggests that leaf phenology in tropical evergreen forests is not determined by the seasonality of precipitation. Instead, leaf phenological process may be driven by availability of solar radiation and/or avoidance of herbivory.     

Effects of El Niño on spring phenology of the highest mountain in north-east Asia

Normalized difference vegetation index (NDVI) data on the highest mountain in north-east Asia were analysed to understand their temporal variability and response to large-scale El Niño–Southern Oscillation (ENSO) events. We demonstrated that El Niño events played an important role in determining the phenology conditions in the Mt Baekdu area in north-east Asia. The analysis confirmed that the onset of phenological spring was earlier during ENSO years. This was evident from a negative trend of about??16 days for each increase of 1 in the ENSO index in year-to-year variations in spring timing and those in ENSO magnitudes. Over two decades, the phenological phases were negatively correlated with air temperature variations under atmospheric warming at the mountain. However, such changes in NDVI are not likely to be affected by changes in local precipitation, as inferred from the analysis of forest types in this area. On the basis of NDVI changes during ENSO years, the results of this study emphasized the importance of the elevation effect and forest types on the ecological response. Moreover, we addressed a significant remote connection between local phenology at the highest mountain in north-east Asia and large-scale atmospheric and oceanic phenomena.     

Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest

A CO₂ eddy flux tower study has recently reported that an old-growth stand of seasonally moist tropical evergreen forest in Santarém, Brazil, maintained high gross primary production (GPP) during the dry seasons [Saleska, S. R., Miller, S. D., Matross, D. M., Goulden, M. L., Wofsy, S. C., da Rocha, H. R., de Camargo, P. B., Crill, P., Daube, B. C., de Freitas, H. C., Hutyra, L., Keller, M., Kirchhoff, V., Menton, M., Munger, J. W., Pyle, E. H., Rice, A. H., & Silva, H. (2003). Carbon in amazon forests: Unexpected seasonal fluxes and disturbance-induced losses. Science, 302, 1554-1557]. It was proposed that seasonally moist tropical evergreen forests have evolved two adaptive mechanisms in an environment with strong seasonal variations of light and water: deep roots system for access to water in deep soils and leaf phenology for access to light. Identifying tropical forests with these adaptive mechanisms could substantially improve our capacity of modeling the seasonal dynamics of carbon and water fluxes in the tropical zone. In this paper, we have analyzed multi-year satellite images from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite (4/1998-12/2002) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite (2000-2003). We reported temporal analyses of vegetation indices and simulations of the satellite-based vegetation photosynthesis model (VPM). The Enhanced Vegetation Index (EVI) identified subtle changes in the seasonal dynamics of leaf phenology (leaf emergence, leaf aging and leaf fall) in the forest, as suggested by the leaf litterfall data. The land surface water index (LSWI) indicated that the forest experienced no water stress in the dry seasons of 1998-2002. The VPM model, which uses EVI, LSWI and site-specific climate data (air temperature and photosynthetically active radiation, PAR) for 2001-2002, predicted high GPP in the late dry seasons, consistent with observed high evapotranspiration and estimated GPP from the CO₂ eddy flux tower.     

Evaluation of compositing period and AVHRR and MERIS combination for improvement of spring phenology detection in deciduous forests

Accurate and precise detection of phenology events is needed to assess trends in seasonal vegetation development indicative of climate or other environmental change processes. In this research, detection accuracy of start of season (SOS) phenology for deciduous forest across Eastern Canada was assessed using satellite time series and in situ PlantWatch observations. Several aspects were evaluated regarding performance of phenology information extraction: 1) effect of compositing period, 2) individual performance of the Advanced Very High Resolution Radiometer (AVHRR) and the Medium Resolution Imaging Spectrometer (MERIS) sensors, and 3) performance for these sensors combined. The AVHRR and MERIS sensors were used as they are overlapping operational missions with planned future continuity. Three approaches to utilizing the multi-sensor data were tested: 1) inter-calibrating NDVI data between sensors and using the multi-sensor data stream to detect SOS, 2) combining independently derived SOS estimates from AVHRR and MERIS based on a weighted average, and 3) combining approaches 1 and 2. Comparison with in situ observations of leaf out and first bloom showed that combining independent SOS estimates from AVHRR and MERIS was better than using the inter-calibrated multi-sensor data. Combining SOS estimates from both sensors reduced error by 1-2 days compared to the individual sensor results. Composite periods from 7 to 11 days produced the best results for leaf out with a mean absolute error (MAE) of 5 days. Results for first bloom were not as good as those for leaf out, producing a MAE of 6.5 days. For first bloom, compositing periods greater than 11 days did not increase error at the same rate as seen for leaf out. However, the larger MAE observed for first bloom may have masked this effect.     

Land surface phenology of North American mountain environments using moderate resolution imaging spectroradiometer data

Monitoring and understanding plant phenology is becoming an increasingly important way to identify and model global changes in vegetation life cycle events. High elevation biomes cover twenty percent of the Earth's land surface and provide essential natural resources. These areas experience limited resource availability for plant growth, development, and reproduction, and are one of the first ecosystems to reflect the harmful impact of climate change. Despite this, the phenology of mountain ecosystems has historically been understudied due to the rough and variable terrain and inaccessibility of the area. In addition, although numerous studies have used synoptically sensed data to study phenological patterns at the continental and global scales, relatively few have focused on characterizing the land surface phenology in mountainous areas. Here we use the MODIS/Terra + Aqua satellite 8-day 500 m Nadir BRDF Adjusted Reflectance product to quantify the land surface phenology. We relate independent data for elevation, slope, aspect, solar radiation, and temperature as well as longitude and latitude with the derived phenology estimates. We present that satellite derived SOS can be predicted based on topographic and weather variables with a significant R²adj between 0.56 and 0.62 for the entire western mountain range. Elevation and latitude exhibit the most significant influences on the timing of SOS throughout our study area. When examined at both the local and regional scales, as well as when accounting for aspect and temperature, SOS follows closely with Hopkins' Bioclimatic Law with respect to elevation and latitude.     

On the relationship of NDVI with leaf area index in a deciduous forest site

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI-LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998-2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year.The results suggest that the NDVI-LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI-LAI relationship was found to be poor (R² varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI.Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships (R²=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an “M-shaped” pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI-LAI relationships.     

Monitoring herbaceous biomass and water content with SPOT VEGETATION time-series to improve fire risk assessment in savanna ecosystems

This paper evaluated the capacity of SPOT VEGETATION time-series to monitor the vegetation biomass and water content in order to improve fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. First, the single date and integrated vegetation index approach, which quantify the amount of herbaceous biomass at the end of the rain season, were evaluated using in situ biomass data. It was shown that the integral of the Ratio Vegetation Index (iRVI) during the rain season was the most suitable index to estimate herbaceous biomass (R² = 0.69). Next, the performance of single, greenness, and accumulated remotely sensed fire risk indices, related to vegetation water content, were evaluated using fire activity data. The Accumulated Relative Normalised Difference Vegetation Index Decrement (ARND) performed the best when estimating fire risk (c-index = 0.76). Finally, results confirmed that the assessment of fire risk was improved by combination of both the vegetation biomass (iRVI) and vegetation water content (ARND) related indices (c-index = 0.80). The monitoring of vegetation biomass and water content with SPOT VEGETATION time-series provided a more suitable tool for fire management and suppression compared to satellite-based fire risk assessment methods, only related to vegetation water content.     

Analysis of leaf area index products from combination of MODIS Terra and Aqua data

A prototype product suite, containing the Terra 8-day, Aqua 8-day, Terra-Aqua combined 8- and 4-day products, was generated as part of testing for the next version (Collection 5) of the MODerate resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products. These products were analyzed for consistency between Terra and Aqua retrievals over the following data subsets in North America: single 8-day composite over the whole continent and annual time series over three selected MODIS tiles (1200 × 1200 km). The potential for combining retrievals from the two sensors to derive improved products by reducing the impact of environmental conditions and temporal compositing period was also explored. The results suggest no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectances products. The differences over smaller regions, however, can be large due to the random nature of residual atmospheric effects. High quality retrievals from the radiative transfer based algorithm can be expected in 90-95% of the pixels with mostly herbaceous cover and about 50-75% of the pixels with woody vegetation during the growing season. The quality of retrievals during the growing season is mostly restricted by aerosol contamination of the MODIS data. The Terra-Aqua combined 8-day product helps to minimize this effect and increases the number of high quality retrievals by 10-20% over woody vegetation. The combined 8-day product does not improve the number of high quality retrievals during the winter period because the extent of snow contamination of Terra and Aqua observations is similar. Likewise, cloud contamination in the single-sensor and combined products is also similar. The LAI magnitudes, seasonal profiles and retrieval quality in the combined 4-day product are comparable to those in the single-sensor 8-day products. Thus, the combined 4-day product doubles the temporal resolution of the seasonal cycle, which facilitates phenology monitoring in application studies during vegetation transition periods. Both Terra and Aqua LAI products show anomalous seasonality in boreal needle leaf forests, due to limitations of the radiative transfer algorithm to model seasonal variations of MODIS surface reflectance data with respect to solar zenith angle. Finally, this study suggests that further improvement of the MODIS LAI products is mainly restricted by the accuracy of the MODIS observations.     

Use of MODIS NDVI to evaluate changing latitudinal gradients of rangeland phenology in Sudano-Sahelian West Africa

This study is concerned with the implications of changing latitudinal gradients in vegetative phenology (green-up, senescence, and length of growing season) for the management of long-distance seasonal movements of livestock herds in Sudano-Sahelian West Africa. For a study area covering much of the southern half of Mali, phenological parameters were estimated using a double-logistic function fitted to seasonal NDVI trajectories for 1 km² MODIS data over the period 2000-2010. Green-up dates, senescence dates and length of growing season were all found to more strongly vary by latitude (+ 9 days/degree, − 5 days/degree and − 14 days/degree, respectively) than across years (+ 0.42 days/year, + 0.86 days/year and + 0.44 days/year respectively). Interannual and spatial variability of these parameters are highest at lower latitudes within the study area. The slopes of the relationship of phenological parameters with latitude change across the latitudinal range studied. Breakpoint analysis of annual green-up versus latitude curves identifies a mean inflection point of 13° north latitude above which the positive slope declines significantly. This previously-undescribed pattern is consistent with recent work on monsoonal dynamics showing rainfall onset being associated with an abrupt shift in the location of the ITCZ (monsoon onset) at latitudes north of 13° north latitude. The effects of the observed variation in latitudinal gradients of phenological variables on the direction and timing of regional livestock movements are discussed.     

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies was studied for the case of the large 2007 Peloponnese wildfires in Greece. Fire severity is defined as the degree of environmental change as measured immediately post-fire, whereas burn severity combines the direct fire impact and ecosystems responses. Geo Composite Burn Index (GeoCBI), two pre-/post-fire differenced Thematic Mapper (TM) dNBR assessments and a Moderate Resolution Imaging Spectroradiometer (MODIS) dNBR time series were used to analyze the temporal dimension. MODIS dNBR time series were calculated based on the difference between the NBR of the burned and control pixels, which were retrieved using time series similarity of a pre-fire year. The analysis incorporated the optimality statistic, which evaluates index performance based on displacements in the mid-infrared-near infrared bi-spectral space. Results showed a higher correlation between field and TM data early post-fire (R² = 0.72) than one-year post-fire (R² = 0.56). Additionally, mean dNBR (0.56 vs. 0.29), the dNBR standard deviation (0.29 vs. 0.19) and mean optimality (0.65 vs. 0.47) were clearly higher for the initial assessment than for the extended assessment. This is due to regenerative processes that obscured first-order fire effects impacting the suitability of the dNBR to assess burn severity in this case study. This demonstrates the importance of the lag timing, i.e. time since fire, of an assessment, especially in a quickly recovering Mediterranean ecosystem. The MODIS time series was used to study intra-annual changes in index performance. The seasonal timing of an assessment highly impacts what is actually measured. This seasonality affected both the greenness of herbaceous resprouters and the productivity of the control pixels, which is land cover specific. Appropriate seasonal timing of an assessment is therefore of paramount importance to anticipate false trends (e.g. caused by senescence). Although these findings are case study specific, it can be expected that similar temporal constraints affect assessments in other ecoregions. Therefore, within the limitations of available Landsat imagery, caution is recommended for the temporal dimension when assessing post-fire effects. This is crucial, especially for studies that aim to evaluate trends in fire/burn severity across space and time. Also, clarification in associated terminology is suggested.     

Data-model fusion for improving LAI mapping: a case study over China's land mass

A simple data-model fusion method is developed to improve leaf area index (LAI) mapping using satellite data. The objective is to overcome two issues with satellite-derived LAI maps: (1) optical remote sensing data are often seriously affected by the atmosphere due to clouds, and in some areas no reliable data are obtained in the whole growing season, and (2) seasonal variations in conifer LAI derived from satellite data are often distorted by the seasonal variations in leaf greenness (pigments), the background vegetation and snow cover, etc., and the derived LAI reflects the overall greenness rather than the actual forest leaf area present in a pixel. These shortcomings of satellite measurements can be greatly alleviated when an ecological model is used to simulate the LAI in the absence of reliable remote sensing data and to estimate the seasonal variation of LAI according to ecological principles. The usefulness of this fusion method is demonstrated through improving a China-wide LAI map series in 10-day intervals at 1 km resolution using Satellite Pour l'Observation de la Terre (SPOT) VEGETATION (VGT) data.     

Spectroscopic determination of leaf water content using continuous wavelet analysis

The gravimetric water content (GWC, %), a commonly used measure of leaf water content, describes the ratio of water to dry matter for each individual leaf. To date, the relationship between spectral reflectance and GWC in leaves is poorly understood due to the confounding effects of unpredictably varying water and dry matter ratios on spectral response. Few studies have attempted to estimate GWC from leaf reflectance spectra, particularly for a variety of species. This paper investigates the spectroscopic estimation of leaf GWC using continuous wavelet analysis applied to the reflectance spectra (350-2500 nm) of 265 leaf samples from 47 species observed in tropical forests of Panama. A continuous wavelet transform was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength and scale. Linear relationships were built between wavelet power and GWC expressed as a function of dry mass (LWC_D) and fresh mass (LWC_F) in order to identify wavelet features (coefficients) that are most sensitive to changes in GWC. The derived wavelet features were then compared to three established spectral indices used to estimate GWC across a wide range of species.Eight wavelet features observed between 1300 and 2500 nm provided strong correlations with LWC_D, though correlations between spectral indices and leaf GWC were poor. In particular, two features captured amplitude variations in the broad shape of the reflectance spectra and three features captured variations in the shape and depth of dry matter (e.g., protein, lignin, cellulose) absorptions centered near 1730 and 2100 nm. The eight wavelet features used to predict LWC_D and LWC_F were not significantly different; however, predictive models used to determine LWC_D and LWC_F differed. The most accurate estimates of LWC_D and LWC_F obtained from a single wavelet feature showed root mean square errors (RMSEs) of 28.34% (R² = 0.62) and 4.86% (R² = 0.69), respectively. Models using a combination of features resulted in a noticeable improvement predicting LWC_D and LWC_F with RMSEs of 26.04% (R² = 0.71) and 4.34% (R² = 0.75), respectively. These results provide new insights into the role of dry matter absorption features in the shortwave infrared (SWIR) spectral region for the accurate spectral estimation of LWC_D and LWC_F. This emerging spectral analytical approach can be applied to other complex datasets including a broad range of species, and may be adapted to estimate basic leaf biochemical elements such as nitrogen, chlorophyll, cellulose, and lignin.     

Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products

This paper evaluates the performances of a neural network approach to estimate LAI from CYCLOPES and MODIS nadir normalized reflectance and LAI products. A data base was generated from these products over the BELMANIP sites during the 2001-2003 period. Data were aggregated at 3 km × 3 km, resampled at 1/16 days temporal frequency and filtered to reject outliers. VEGETATION and MODIS reflectances show very consistent values in the red, near infrared and short wave infrared bands. Neural networks were trained over part of this data base for each of the 6 MODIS biome classes to retrieve both MODIS and CYCLOPES LAI products.Results show very good performances of neural networks to estimate the original LAI products with an overall root mean square error (RMSE) around 0.5 for MODIS LAI from both MODIS and CYCLOPES normalized reflectances and a RMSE ranging between 0.12 (CYCLOPES reflectances) and 0.29 (MODIS reflectances) for CYCLOPES LAI. A drop of 15% of performance was found by training MODIS biome dependant algorithm by a single network over all the classes at the same time. More detailed analyses show that CYCLOPES and MODIS LAI values are very consistent for grasses and crops. Conversely, other biomes including shrubs, savanna, needleleaf and broadleaf forests show significant discrepancies, mainly due to differences between LAI definitions used between CYCLOPES (closer to effective LAI) and MODIS (closer to true LAI). However, products derived from the original CYCLOPES LAI products show a better agreement with both effective and true LAI ground measurements values. MODIS LAI products show more instability, partly because of the slightly shorter temporal resolution as compared to CYCLOPES.These results confirm the interest and versatility of neural networks for operational algorithms. This approach could be extended to other products or sensors, and may constitute a step forward for the fusion of data from several sensors, hence contributing to develop ‘virtual constellations’.     

Characteristic patterns of QuikScat-based wind stress and turbulent heat flux in the tropical Indian Ocean

Using QuikScat-based vector wind data for 1999-2003, surface wind stress and turbulent heat (Q) have been mapped for the tropical Indian Ocean (IO) to understand their seasonal variability. During July wind stress is enhanced by ∼ 70% in the Arabian Sea compared to that during January. The Arabian Sea experiences a large Q loss (150-200 W/m²) during the summer and winter monsoons, which is nearly 1.3 times of that in the Bay of Bengal. The southeasterlies are strengthened during the southern hemisphere winter. Empirical Orthogonal Function analysis captures different phases of monsoon-induced variability in wind stress and Q, ranging from seasonal to high-frequency perturbations. Coherency between time coefficients of EOF-1 for wind stress and Q suggests that former leads the latter with a temporal lag of 20-40 days for period > 322 days. At high frequencies (< 21 days) Q leads wind stress with a temporal lag of 2 days. Possible explanation for wind stress leading Q over an annual time scale is offered based on the marine atmospheric boundary layer physics and pre-conditioned ocean surface, while on shorter time scales (21 days) ocean thermodynamics through mixed layer processes cause Q to lead wind stress.     

Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests

We evaluated whether satellite radar remote sensing of landscape seasonal freeze-thaw cycles provides an effective measure of active growing season timing and duration for boreal and subalpine evergreen forests. Landscape daily radar backscatter measurements from the SeaWinds scatterometer on-board the QuikSCAT satellite were evaluated across a regional network of North American coniferous forest sites for 2000 and 2001. Radar remote sensing measurements of the initiation and length of the growing season corresponded closely with both site measurements and ecosystem process model (BIOME-BGC) simulations of these parameters because of the sensitivity of the K_u-band scatterometer to snow cover freeze-thaw dynamics and associated linkages between growing season initiation and the timing of seasonal snowmelt. In contrast, remote sensing estimates of the timing of growing season termination were either weakly or not significantly associated with site measurements and model simulation results, due to the relative importance of light availability and other environmental controls on stand phenology in the fall. Regional patterns of estimated annual net primary production (NPP) and component photosynthetic and autotrophic respiration rates for the evergreen forest sites also corresponded favorably with remote sensing estimates of the seasonal timing of spring thaw and associated growing season length, indicating the importance of these parameters in determining spatial and temporal patterns of NPP and the potential utility of satellite radar remote sensing for regional monitoring of the terrestrial biosphere.