Characterizing temperate forest structural and spectral diversity with Hyperion EO-1 data

Forest structural diversity can serve as an important indicator of biodiversity. The relationship between spaceborne hyperspectral remotely sensed data and several measures of forest structure was explored over a 625 km² coastal temperate forest landscape on Vancouver Island, British Columbia, Canada. Thirteen Hyperion bands were selected for analysis based on the documented and hypothesized importance of various spectral wavelengths to forest characterization. To aid in understanding spectral trends, measures of forest stand structural diversity (projected age, projected height, and stand species composition complexity) were derived from forest inventory data. The spectral distance between the stand mean and standard deviation of reflectance and related expectations from global equivalents for each of the 13 bands were used to relate measures of spectral diversity (N = 801 forest inventory stands).Canonical correlation analysis was then used to determine the independent and shared relationships between these selected measures of forest structural diversity (dependent variables) and spectral diversity (independent variables). The dependent variables that were most strongly correlated with the first canonical variate were projected age and projected height, with canonical loadings of 0.973 and 0.979, respectively. In contrast, stand species composition complexity had a weak, negative correlation with spectral diversity (canonical loading = − 0.025). The wavelengths contributing the most to the canonical function included: 681-740 nm, 551-680 nm, and 1401-2400 nm. There have been few studies that attempt to directly link spectral and species diversity in temperate forest environments. From this initial investigation, we posit that the complex spectral response of coastal temperate forests may confound efforts to directly link spectral and species diversity across a range of site conditions.Our results, which are constrained by the spectral and spatial resolution of the data used, our target environment, and the metrics selected for measuring forest structure, suggest that attributes that characterize forest structural conditions may have a more meaningful relationship with spectral diversity than measures of species diversity alone, and that future studies in coastal temperate forests that seek to link spectral diversity with biodiversity should include measures of forest structural diversity, in addition to measures of species diversity.     

Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data

Hyperspectral data acquired by the Hyperion instrument, on board the Earth Observing-1 (EO-1) satellite, were evaluated for the discrimination of five important Brazilian sugarcane varieties (RB72-454, SP80-1816, SP80-1842, SP81-3250, and SP87-365). The radiance values were converted into surface reflectance images by a MODTRAN4-based technique. To discriminate varieties with similar reflectance values, multiple discriminant analysis (MDA) was applied over the data. To obtain an adequate discriminant function, a stepwise method was used to select the best variables among surface reflectance values, ratios of reflectance, and several spectral indices potentially sensitive to changes in chlorophyll content, leaf water, and lignin-cellulose. Results showed that the five Brazilian sugarcane varieties were discriminated using EO-1 Hyperion data. Differences in canopy architecture affected sunlight penetration and reflectance, resulting in a higher reflectance for planophile (e.g., SP81-3250) than erectophile (e.g., SP80-1842) sugarcane plants. The variety SP80-1842 presented lower reflectance values, deeper lignin-cellulose absorption bands at 2103 nm and 2304 nm, shallower leaf liquid water absorption bands at 983 nm and 1205 nm, and lower leaf liquid water content than the other sugarcane varieties. To discriminate this cultivar, a single near-infrared (NIR) band threshold was used. To discriminate the other four sugarcane varieties with similar reflectance values, MDA was used producing a classification accuracy of 87.5% for a hold-out set of pixels. The comparison between the ground truth data and the MDA-derived classification image confirmed the model' capacity to differentiate the varieties accurately. The best results were obtained for the cultivar SP87-365 that presented the lowest spectral variability in the discriminant space. Some misclassified areas were associated with the cultivars SP80-1816 and SP81-3250 that showed the highest spectral variability.     

Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

Quantitative estimation of fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV) and bare soil (f_BS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving f_PV, f_NPV and f_BS across the ~ 2 million km² Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for f_PV, f_NPV and f_BS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000-2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of f_NPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of f_PV, f_NPV and f_BS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world.     

An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation

Estimation of chlorophyll content and the leaf area index (LAI) using remote sensing technology is of particular use in precision agriculture. Wavelengths at the red edge of the vegetation spectrum (705 and 750 nm) were selected to test vegetation indices (VIs) using spaceborne hyperspectral Hyperion data for the estimation of chlorophyll content and LAI in different canopy structures. Thirty sites were selected for the ground data collection. The results show that chlorophyll content and LAI can be successfully estimated by VIs derived from Hyperion data with a root mean square error (RMSE) of 7.20–10.49 μg cm^?2 for chlorophyll content and 0.55–0.77 m² m^?2 for LAI. The special index derived from three bands provided the best estimation of the chlorophyll content (RMSE of 7.19 μg cm^?2 for the Modified Chlorophyll Absorption Ratio Index/Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI₇₀₅)) and LAI (RMSE of 0.55 m² m^?2 for a second form of the MCARI (MCARI2₇₀₅)). These results demonstrate the possibilities for analysing the variation in chlorophyll content and LAI using hyperspectral Hyperion data with bands from the red edge of the vegetation spectrum.     

An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data

Long term observations of global vegetation from multiple satellites require much effort to ensure continuity and compatibility due to differences in sensor characteristics and product generation algorithms. In this study, we focused on the bandpass filter differences and empirically investigated cross-sensor relationships of the normalized difference vegetation index (NDVI) and reflectance. The specific objectives were: 1) to understand the systematic trends in cross-sensor relationships of the NDVI and reflectance as a function of spectral bandpasses, 2) to examine/identify the relative importance of the spectral features (i.e., the green peak, red edge, and leaf liquid water absorption regions) in and the mechanism(s) of causing the observed systematic trends, and 3) to evaluate the performance of several empirical cross-calibration methods in modeling the observed systematic trends. A Level 1A Hyperion hyperspectral image acquired over a tropical forest—savanna transitional region in Brazil was processed to simulate atmospherically corrected reflectances and NDVI for various bandpasses, including Terra Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA-14 Advanced Very High Resolution Radiometer (AVHRR), and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). Data were extracted from various land cover types typically found in tropical forest and savanna biomes and used for analyses. Both NDVI and reflectance relationships among the sensors were neither linear nor unique and were found to exhibit complex patterns and bandpass dependencies. The reflectance relationships showed strong land cover dependencies. The NDVI relationships, in contrast, did not show land cover dependencies, but resulted in nonlinear forms. From sensitivity analyses, the green peak (∼550 nm) and red-NIR transitional (680-780 nm) features were identified as the key factors in producing the observed land cover dependencies and nonlinearity in cross-sensor relationships. In particular, differences in the extents to which the red and/or NIR bandpasses included these features significantly influenced the forms and degrees of nonlinearity in the relationships. Translation of MODIS NDVI to “AVHRR-like” NDVI using a weighted average of MODIS green and red bands performed very poorly, resulting in no reduction of overall discrepancy between MODIS and AVHRR NDVI. Cross-calibration of NDVI and reflectance using NDVI-based quadratic functions performed well, reducing their differences to ± .025 units for the NDVI and ± .01 units for the reflectances; however, many of the translation results suffered from bias errors. The present results suggest that distinct translation equations and coefficients need to be developed for every sensor pairs and that land cover-dependency need to be explicitly accounted for to reduce bias errors.     

Combining EO-1 Hyperion and Envisat ASAR data for mangrove species classification in Mai Po Ramsar Site,Hong Kong

Mangrove habitat is one of the most highly productive ecosystems. The distribution of mangrove species acts as an inventory to formulate conservation management plans. This study explored the potential of combining hyperspectral (Earth-observing (EO)-1 Hyperion) and multi-temporal synthetic aperture radar (SAR) (Environmental Satellite (Envisat) ASAR) data, supported by in situ field surveys, to map mangrove species. Hyperspectral imaging captures a number of narrow contiguous spectral bands providing richer spectral details than those obtained from traditional broadband sensors. All-weather radar sensing allows continuous data acquisition and its signal penetrability can reveal canopy structural characteristics, which offer an additional data dimension that is not available in optical sensing. Through combining the two data types, this study achieved three objectives. First, facing the issue of dimensionality and limited field samples, feature selection techniques from computer science were adopted to select spectral and radar features that are crucial for mangrove species discrimination. Second, classification accuracy using various combinations of spectral and radar features was evaluated. Third, classification algorithms including maximum likelihood (ML), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM) were used to estimate species distribution, and classification accuracy was compared. Results suggested that feature selection techniques are capable of identifying salient features in spectral and radar space that can effectively discriminate between mangrove species. Combining optical and radar data can improve classification accuracy. Among the classifiers, ANN produces more accurate and robust estimation.     

Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data

Nitrogen (N) is one of the most important limiting nutrients for sugarcane production. Conventionally, sugarcane N concentration is examined using direct methods such as collecting leaf samples from the field followed by analytical assays in the laboratory. These methods do not offer real-time, quick, and non-destructive strategies for estimating sugarcane N concentration. Methods that take advantage of remote sensing, particularly hyperspectral data, can present reliable techniques for predicting sugarcane leaf N concentration. Hyperspectral data are extremely large and of high dimensionality. Many hyperspectral features are redundant due to the strong correlation between wavebands that are adjacent. Hence, the analysis of hyperspectral data is complex and needs to be simplified by selecting the most relevant spectral features. The aim of this study was to explore the potential of a random forest (RF) regression algorithm for selecting spectral features in hyperspectral data necessary for predicting sugarcane leaf N concentration. To achieve this, two Hyperion images were captured from fields of 6–7 month-old sugarcane, variety N19. The machine-learning RF algorithm was used as a feature-selection and regression method to analyse the spectral data. Stepwise multiple linear (SML) regression was also examined to predict the concentration of sugarcane leaf N after the reduction of the redundancy in hyperspectral data. The results showed that sugarcane leaf N concentration can be predicted using both non-linear RF regression (coefficient of determination, R ²?=?0.67; root mean square error of validation (RMSEV)?=?0.15%; 8.44% of the mean) and SML regression models (R ²?=?0.71; RMSEV?=?0.19%; 10.39% of the mean) derived from the first-order derivative of reflectance. It was concluded that the RF regression algorithm has potential for predicting sugarcane leaf N concentration using hyperspectral data.     

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000-2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.     

Seasonal characterization and discrimination of savannah physiognomies in Brazil using hyperspectral metrics from Hyperion/EO-1

The seasonal characterization and discrimination of savannahs in Brazil are still challenging due to the high spatial variability of the vegetation cover and the spectral similarity between some physiognomies. As a preparatory study for future hyperspectral missions that will operate with large swath width and better signal-to-noise ratio than the current orbital sensors, we evaluated six Hyperion images acquired over the Estação Ecológica de Águas Emendadas, a protected area in central Brazil. We studied the seasonal variations in spectral response of the savannah physiognomies and tested their discrimination in the rainy and dry seasons using distinct sets of hyperspectral metrics. Floristic and structural attributes were inventoried in the field. We considered three sets of metrics in the data analysis: the reflectance of 146 Hyperion bands, 22 narrowband vegetation indices (VIs), and 24 absorption band parameters. The VIs were selected to represent vegetation structure, biochemistry, and physiology. The depth, area, width, and asymmetry of the major absorption bands centred at 680 nm (chlorophyll), 980, and 1200 nm (leaf water) and 1700, 2100, and 2300 nm (lignin-cellulose) were calculated on a per-pixel basis using the continuum removal method. Using feature selection and multiple discriminant analysis (MDA), we tested the discriminatory capability of these metrics and of their combined use for vegetation discrimination in the rainy and dry seasons. The results showed that the spectral modifications with seasonality were stronger with the savannah woodland-grassland gradient represented by decreasing tree height, basal area, tree density and biomass and by increasing canopy openness. We observed a reflectance increase in the red, red edge, and shortwave (SWIR) intervals towards the dry season. In the near-infrared, the reflectance differences between the physiognomies were smaller in the dry season than in the rainy season. From the 22 VIs, the visible atmospherically resistant index (VARI), visible green index (VIg), and normalized difference infrared index (NDII) were the most sensitive indices to water stress and vegetation cover, presenting the largest rates of changes between the rainy (March) and dry (August) seasons in shrub and grassland areas. Absorption band parameters associated with the lignin-cellulose spectral features in the SWIR increased towards the dry season with great amounts of non-photosynthetic vegetation (NPV) in the herbaceous stratum. The opposite was observed for the 680 nm chlorophyll absorption band and the 980 and 1200 nm leaf water features. In general, the number of selected metrics necessary for vegetation discrimination was lower in the dry season than in the rainy season. The best MDA-classification accuracy was obtained in the dry season using nine VIs (79.5%). The combination of different hyperspectral metrics increased the classification accuracy to 81.4% in the rainy season and to 84.2% in the dry season. This combination added a gain higher than 10% for the classification of shrub savannah, open woodland savannah and wooded savannah.     

Spectral pathways for effective delineation of high-grade bauxites: a case study from the Savitri River Basin,Maharashtra, India,using EO-1 Hyperion data

Bauxite, the only source of aluminium, is an aggregate of minerals, most of which are oxides and hydroxides of aluminium and iron such as gibbsite, bohemite, goethite and haematite. Bauxite is used in the chemical and refractory industries and its quality is controlled by the presence of impurities such as iron and silica. Bauxite commonly occurs together with iron-rich laterites as alteration products of parental igneous and metamorphic rocks. Aluminium-rich bauxites grade towards highly ferruginous laterites with a transitional Al-rich laterites or ferruginous bauxite, herein described as Al-laterites. In the Savitri River Basin, bauxite contains 58–75% gibbsite, 6–11% goethite and 19–26% haematite, whereas the mineralogy of Al-laterites and Fe-laterites are dominated by haematite (29–68%) and goethite (6–25%) with subordinate amounts of gibbsite. Conventional techniques to demarcate the high-grade pockets of bauxites rich in gibbsite are tedious, time consuming and involve detailed field sampling and geochemical analyses. Our work illustrates how spectral properties of these three litho-units can be effectively utilized in mapping of high-grade bauxites occurring over wide areas using hyperspectral remote sensing (HRS). The methodology adopted herein involves generation of noise-free field spectral database of target materials, linear unmixing of field spectra for constituent minerals, classification of preprocessed Hyperion images using field spectra and finally accuracy assessment for ore grade estimation. It is observed that bauxite mapping using Hyperion data and noise-free field spectra yielded results that correlate well with the chemistry and mineralogy of representative samples. By adopting the above procedure, we achieved classification accuracies of 100%, 71% and 89% for bauxite, Al-laterite and Fe-laterite classes, respectively.     

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.     

Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

In previous studies of the universal pattern decomposition method (UPDM), spectral shifts, which are very common in hyperspectral imaging spectrometers, were not taken into account when calculating standard spectral pattern vectors. This study evaluated the effect of spectral shifts on the sensor dependence of the vegetation index based on the UPDM (VIUPD) and 11 other vegetation indices (VIs). Spectral shifts were calculated using Gao's spectrum-matching method. The influences of smoothing techniques (moving average and Savitzky–Golay filters) on the consistency of these VIs were also evaluated and compared. Data from the typical narrowband imaging spectrometers, Hyperion and the Compact High Resolution Imaging Spectrometer (CHRIS), were chosen for the study. For all VIs, both smoothing and spectral calibration changed the consistency between Hyperion and CHRIS. Spectral calibration had a positive effect on the majority of VIs, whereas smoothing improved the performance of some VIs but decreased the consistency of others. When compared with spectral calibration and Savitzky–Golay smoothing, moving average generated greater variations within the results. Among the smoothing techniques employed, moving average smoothing exhibited a larger distortion of VI sensor dependency than that of Savitzky–Golay smoothing of the same order. VIUPD based on narrowband hyperspectral data was sensitive to spectral operations (spectral calibration and smoothing). For VIUPD, spectral calibration increased its sensor independence, whereas smoothing had a negative effect. After spectral calibration, VIUPD was more sensor independent than any other VI examined in this study.     

Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data

The eddy covariance technique provides measurements of net ecosystem exchange (NEE) of CO₂ between the atmosphere and terrestrial ecosystems, which is widely used to estimate ecosystem respiration and gross primary production (GPP) at a number of CO₂ eddy flux tower sites. In this paper, canopy-level maximum light use efficiency, a key parameter in the satellite-based Vegetation Photosynthesis Model (VPM), was estimated by using the observed CO₂ flux data and photosynthetically active radiation (PAR) data from eddy flux tower sites in an alpine swamp ecosystem, an alpine shrub ecosystem and an alpine meadow ecosystem in Qinghai-Tibetan Plateau, China. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)) derived from the Moderate Resolution Imaging Spectral radiometer (MODIS) data and climate data at the flux tower sites, and estimated the seasonal dynamics of GPP of the three alpine grassland ecosystems in Qinghai-Tibetan Plateau. The seasonal dynamics of GPP predicted by the VPM model agreed well with estimated GPP from eddy flux towers. These results demonstrated the potential of the satellite-driven VPM model for scaling-up GPP of alpine grassland ecosystems, a key component for the study of the carbon cycle at regional and global scales.     

Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems

Northern Arizona ecosystems are particularly sensitive to plant-available moisture and have experienced a severe drought with considerable impacts on ecosystems from desert shrub and grasslands to pinyon-juniper and conifer forests. Long-term time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are used to monitor recent regional vegetation activity and temporal patterns across various ecosystems. Surface air temperature, solar radiation and precipitation are used to represent meteorological anomalies and to investigate associated impacts on vegetation greenness. Vegetation index anomalies in the northern Arizona ecosystem have a decreasing trend with increasing surface air temperature and decreasing precipitation. MODIS NDVI and EVI anomalies are likely sensitive to the amount of rainfall for northern Arizona ecosystem conditions, whereas inter-annual variability of surface air temperature accounts for MODIS NDVI anomaly variation. The higher elevation area shows the slow vegetation recovery through trend analysis from MODIS vegetation indices for 2000–2011 within the study domain and along elevation.     

Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data

In this study, we used the remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS), meteorological and eddy flux data and an artificial neural networks (ANNs) technique to develop a daily evapotranspiration (ET) product for the period of 2004-2005 for the conterminous U.S. We then estimated and analyzed the regional water-use efficiency (WUE) based on the developed ET and MODIS gross primary production (GPP) for the region. We first trained the ANNs to predict evapotranspiration fraction (EF) based on the data at 28 AmeriFlux sites between 2003 and 2005. Five remotely-sensed variables including land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), leaf area index (LAI) and photosynthetically active radiation (PAR) and ground-measured air temperature and wind velocity were used. The daily ET was calculated by multiplying net radiation flux derived from remote sensing products with EF. We then evaluated the model performance by comparing modeled ET with the data at 24 AmeriFlux sites in 2006. We found that the ANNs predicted daily ET well (R² = 0.52-0.86). The ANNs were applied to predict the spatial and temporal distributions of daily ET for the conterminous U.S. in 2004 and 2005. The ecosystem WUE for the conterminous U.S. from 2004 to 2005 was calculated using MODIS GPP products (MOD17) and the estimated ET. We found that all ecosystems' WUE-drought relationships showed a two-stage pattern. Specifically, WUE increased when the intensity of drought was moderate; WUE tended to decrease under severe drought. These findings are consistent with the observations that WUE does not monotonously increase in response to water stress. Our study suggests a new water-use efficiency mechanism should be considered in ecosystem modeling. In addition, this study provides a high spatial and temporal resolution ET dataset, an important product for climate change and hydrological cycling studies for the MODIS era.     

Rice heading date retrieval based on multi-temporal MODIS data and polynomial fitting

The objective of the study is to identify the rice heading date and analyse its spatial characteristics on a regional scale using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) normalized differential vegetation index (NDVI) data and a new approach: quadratic polynomial fitting. The cloud-contaminated NDVI value was identified by reliability data and linearly interpolated with values before and after the cloudy one. The discrete Fourier transformation (DFT) and quadratic polynomial fitting were implemented to generate new time series curves. Rice heading date was retrieved by calculating the day for maximum NDVI. Comparing with DFT, the proposed quadratic polynomial fitting significantly improves the computation efficiency, while providing approximate precision of estimation. In regional analysis, the rice heading date retrieved from polynomial fitting is more consistent than that from DFT. The study also suggests that multi-temporal MODIS NDVI data combined with different methods can retrieve crop phenology information on a large scale.     

Development of a global evapotranspiration algorithm based on MODIS and global meteorology data

The objective of this research is to develop a global remote sensing evapotranspiration (ET) algorithm based on Cleugh et al.'s [Cleugh, H.A., R. Leuning, Q. Mu, S.W. Running (2007) Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment 106, page 285-304- 2007 (doi: 10.1016/j.rse.2006.07.007).] Penman-Monteith based ET (RS-PM). Our algorithm considers both the surface energy partitioning process and environmental constraints on ET. We use ground-based meteorological observations and remote sensing data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to estimate global ET by (1) adding vapor pressure deficit and minimum air temperature constraints on stomatal conductance; (2) using leaf area index as a scalar for estimating canopy conductance; (3) replacing the Normalized Difference Vegetation Index with the Enhanced Vegetation Index thereby also changing the equation for calculation of the vegetation cover fraction (F_C); and (4) adding a calculation of soil evaporation to the previously proposed RS-PM method.We evaluate our algorithm using ET observations at 19 AmeriFlux eddy covariance flux towers. We calculated ET with both our Revised RS-PM algorithm and the RS-PM algorithm using Global Modeling and Assimilation Office (GMAO v. 4.0.0) meteorological data and compared the resulting ET estimates with observations. Results indicate that our Revised RS-PM algorithm substantially reduces the root mean square error (RMSE) of the 8-day latent heat flux (LE) averaged over the 19 towers from 64.6 W/m² (RS-PM algorithm) to 27.3 W/m² (Revised RS-PM) with tower meteorological data, and from 71.9 W/m² to 29.5 W/m² with GMAO meteorological data. The average LE bias of the tower-driven LE estimates to the LE observations changed from 39.9 W/m² to − 5.8 W/m² and from 48.2 W/m² to − 1.3 W/m² driven by GMAO data. The correlation coefficients increased slightly from 0.70 to 0.76 with the use of tower meteorological data. We then apply our Revised RS-PM algorithm to the globe using 0.05° MODIS remote sensing data and reanalysis meteorological data to obtain the annual global ET (MODIS ET) for 2001. As expected, the spatial pattern of the MODIS ET agrees well with that of the MODIS global terrestrial gross and net primary production (MOD17 GPP/NPP), with the highest ET over tropical forests and the lowest ET values in dry areas with short growing seasons. This MODIS ET product provides critical information on the regional and global water cycle and resulting environment changes.     

Retrieval of aerosol optical thickness from HJ-1 CCD data based on MODIS-derived surface reflectance

The successfully launched Huanjing-1 (HJ-1) satellite by China in 2008 provides a new source of data for monitoring the environment. In this article, we develop a new algorithm for retrieving the aerosol optical thickness (AOT) using HJ-1 charge-coupled device (CCD) data with the assistance of the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and the bidirectional reflectance distribution function (BRDF) data products. This algorithm is then used to retrieve AOT in a delta region of the Yangtze River. The retrieved results are assessed for their accuracy by comparison with ground-measured data using sun photometers. Comparison of such derived AOT with in situ AOT measured using sun photometers indicates a root mean squared error (RMSE) of 0.123, and their regression relation has a correlation coefficient of 0.896 that is statistically significant at the 0.01 level. Such a relatively high level of retrieval accuracy suggests that HJ-1 CCD data can be used competently and effectively to retrieve AOT with the assistance of MODIS products that are used to construct the surface reflectance model. This study successfully demonstrates the feasibility of synergistically retrieving AOT from data acquired by different sensors. The lower dependence on data from a sole source means that the retrieval is less restrictive by data availability.     

Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites

Numerous models of evapotranspiration have been published that range in data-driven complexity, but global estimates require a model that does not depend on intensive field measurements. The Priestley-Taylor model is relatively simple, and has proven to be remarkably accurate and theoretically robust for estimates of potential evapotranspiration. Building on recent advances in ecophysiological theory that allow detection of multiple stresses on plant function using biophysical remote sensing metrics, we developed a bio-meteorological approach for translating Priestley-Taylor estimates of potential evapotranspiration into rates of actual evapotranspiration. Five model inputs are required: net radiation (R_n), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), maximum air temperature (T_max), and water vapor pressure (ea). Our model requires no calibration, tuning or spin-ups. The model is tested and validated against eddy covariance measurements (FLUXNET) from a wide range of climates and plant functional types—grassland, crop, and deciduous broadleaf, evergreen broadleaf, and evergreen needleleaf forests. The model-to-measurement r² was 0.90 (RMS = 16 mm/month or 28%) for all 16 FLUXNET sites across 2 years (most recent data release). Global estimates of evapotranspiration at a temporal resolution of monthly and a spatial resolution of 1° during the years 1986-1993 were determined using globally consistent datasets from the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP-II) and the Advanced Very High Resolution Spectroradiometer (AVHRR). Our model resulted in improved prediction of evapotranspiration across water-limited sites, and showed spatial and temporal differences in evapotranspiration globally, regionally and latitudinally.     

Detection of algal blooms in European waters based on satellite chlorophyll data from MERIS and MODIS

A technique for algal-bloom detection in European waters is described, based on standard chlorophyll a concentration (Chl) data from two ocean-colour sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS). Comparison of the two data sources shows good agreement in case 1 waters, whereas the difference is significant in coastal waters including turbid areas. A relationship between the water-leaving reflectance at 667 nm and Chl for case 1 waters was used to eliminate pixels where Chl retrieval is contaminated by backscatter from inorganic suspended matter. Daily Chl data are compared to a predefined threshold map to determine whether an algal bloom has occurred. In this study, a threshold map was defined as the 90th percentile of previous years' data to take account of regional differences in typical Chl levels, with separate maps for each sensor to take account of sensor-specific bias. The algal-bloom detection processing chain is described, and example results are presented.