Retrieval of high-resolution sea surface temperature data for Sendai Bay, Japan, using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

We studied sea surface temperature (SST) retrieval algorithms for Sendai Bay, using output from the thermal-infrared channels of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board Terra. While the highest resolutions of other satellite SST products are about 1 km, the ASTER thermal-infrared channels provide 90-m spatial resolution. To develop the ASTER algorithm, we employed statistical methods in which SSTs retrieved from the thermal-infrared measurements were tuned against the Moderate Resolution Imaging Spectroradiometer (MODIS) SST product with a 1-km spatial resolution. Terra also carries a MODIS sensor, which observed the same area as the ASTER sensor at the same time. The MODIS SST was validated around Sendai Bay, revealing a bias of −0.15 °C and root mean-square difference (RMSD) of 0.67 °C against in situ SSTs. Taking into account the spatial-resolution difference between ASTER and MODIS, match-up was generated only if the variability of ASTER brightness temperatures (T₁₃) was small in a pixel of MODIS SST (MP). The T₁₃ within one MP was about 121 pixels. The standard deviation (σ₁₃) of T₁₃ was calculated for each cloud-free MP, and the threshold of σ₁₃ for choosing match-up MPs was decided by analyzing the σ₁₃ histogram of one ASTER image. The 15 synchronous pairs of ASTER/MODIS images are separated into two groups of 8 pairs called set (A) and 7 pairs called set (B). Using the common procedure, the match-ups are generated for set (A) and set (B). The former is used for developing the ASTER Multi-Channel SST (MCSST) algorithm, and the latter for validation of the developed ASTER SST. Analysis of the whole 15 pairs indicated that ASTER SST does not depend on the satellite zenith angle. We concluded that, using Akaike's information criterion with set (A) match-ups, the multiple regression formula with all five thermal-infrared channels was adequate for the ASTER SST retrieval. Validation of ASTER SST using match-up set (B) indicated a bias of 0.101 °C and RMSD of 0.455 °C.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Empirical models for estimating daily maximum,minimum and mean air temperatures with MODIS land surface temperatures

Daily air temperature is a measurement that is required by many biogeochemical models. This study compared daily maximum (T _max), minimum (T _min) and mean (T _mean) air temperature observations collected at 678 standard meteorological stations of China in 2003 with estimates derived from daytime and night-time land surface temperature (LST) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board TERRA and AQUA satellites. Correlation analysis showed that the determination coefficients (R ² > 0.81) between models using night-time LSTs and the observed air temperatures were higher than those using daytime LSTs (R ² > 0.57), but with significant seasonal variation. Though estimates derived from coupled daytime and night-time LSTs were more accurate than using night-time or daytime LSTs alone, the available pixels were substantially reduced. Four empirical models were established for T _max, T _min and T _mean with MODIS night-time LSTs alone, or with coupled daytime and night-time LSTs, respectively. Solar declination was incorporated into the models to simulate seasonal variation of the correlations. Model validation showed that percentage of residuals within –3°C to 3°C ranged approximately from 60.2% to 74.3%, 64.4% to 69.9% and 76.8% to 85.7% for T _max, T _min and T _mean, respectively. It was concluded that night-time LST was the optimum predictor for estimating daily T _min, T _mean and even T _max when considering both the performance of the models and the availability of the LST data. Moreover, there was no significant difference between LSTs of TERRA and AQUA for estimating daily air temperatures.     

The influence of igneous intrusions on the peak temperatures of host rocks: Finite-time emplacement, evaporation, dehydration, and decarbonation

Using a 13-m-thick basic sill and its limestone host rocks of the Permian Irati Formation from the Parana Basin, South America, as an example, this paper presents a numerical investigation based on heat conduction models on the effect of the emplacement mechanism of igneous intrusions, pore-water evaporation, and dehydration and decarbonation of host rocks on the peak temperature (T_peak) of host rocks. Our results demonstrate that: (1) the finite-time intrusion mechanism of magma can lower the predicted T_peak of host rocks by up to 100 °C relative to the instantaneous intrusion mechanism, and although pore-water evaporation together with dehydration and decarbonation reactions can also depress the thermal effect of the sill on its host rocks, the maximum effect of these mechanisms on T_peak only reaches approximately 50 °C. (2) The effect of pore-water evaporation on T_peak is obviously greater than that of the dehydration and decarbonation reactions: the former can cause a maximum deviation of 40 °C in the predicted T_peak, whereas the deviation due to the latter is less than 20 °C. Further, the effect of the dehydration and decarbonation reactions on T_peak is less than 10 °C if pore-water evaporation is allowed simultaneously in the models and can hence be ignored in thermal modeling. (3) The finite-time intrusion mechanism of magma probably represents the natural condition of the sill. Pore-water evaporation and dehydration and decarbonation of host rocks are also likely to play important roles in lowering the thermal effect of the sill.     

Empirical models for estimating daily surface water vapour pressure,air temperature,and humidity using MODIS and spatiotemporal variables. Applications to peninsular Spain

In this article, we present empirical models for estimating daily surface water vapour pressure (e₀), air temperature (T_a), and relative humidity (RH) over cloud-free land areas in peninsular Spain using Moderate Resolution Imaging Spectroradiometer (MODIS) and spatiotemporal variables. The models were obtained and validated using daily mean, maximum, and minimum e₀, T_a, and RH data (year 2010) from 331 ground-level meteorological stations and the diurnal Terra-MODIS data in peninsular Spain, but the methodology can easily be extrapolated and used to obtain algorithms for other regions around the world. The best e₀ models are based on total precipitable water (W) estimations obtained by MOD05 or IMAPP WVNIR products and the spatiotemporal variables of longitude (λ), distance to the coast (d_coast), and Julian day (JD). Other models based on Sobrino’s W algorithm or on Recondo’s e₀ algorithm for Asturias (in northern Spain) were also tested. The best T_a models are based on land surface temperature (LST) obtained by the MOD11 LST or IMAPP LST products and on other remote-sensing variables, such as W and the normalized difference vegetation index (NDVI), and the spatiotemporal variables λ, JD, and height (h). Models based on Sobrino’s LST algorithms were also tested. RH can be derived directly from e₀ and T_a or from models similar to those used to obtain e₀ and T_a. Models based on the NASA standard products MOD05 and MOD11 LST are slightly better than those based on IMAPP products, but the advantage of IMAPP products for our purposes is that they can be generated in almost real time from the data obtained by the MODIS antenna at the University of Oviedo. IMAPP models obtain the following: R² = 0.83-0.79-0.70 and RSE = 1.62-1.59-1.76 hPa for e_0mean, e_0max, and e_0min; and R² = 0.91-0.91-0.80 and RSE = 1.96-2.25-3.00 K for T_mean, T_max, and T_min. Worse results are obtained for RH: R² = 0.49-0.39 and RSE = 7.21-9.75% for RH_min and RH_mean, with no correlation found for RH_max. Model validations yield R² and RSE values similar to those obtained in the models, with an RMSD = 1.86-1.99-2.21 hPa for e_0mean, e_0max, and e_0min; an RMSD = 2.05-2.40-2.95 K for T_mean, T_max, and T_min; and RMSD = 8-11% for RH_min and RH_mean. The bias is small in all cases: <0.2 hPa for e₀, ≤0.1 K for T_a, and ≤ |1|% for RH. From the results of this article, we propose substituting the traditionally used RH variable with the e₀ variable to be used as meteorological variable in environmental risk models such as, for example, fire risk models.     

An interferometric biosensor composed of a prism-chamber assembly and a composite waveguide with a Ta2O5 nanometric layer

A highly sensitive integrated polarimetric interferometer biosensor with improved long-time stability and simple operation was prepared by using a novel prism-chamber assembly and an inexpensive waveguide made by sputtering a tapered nanometric layer of Ta₂O₅ on a single-mode glass waveguide. By comparing the measured refractive-index (RI) sensitivities with those simulated based on a four-layer homogeneous waveguide, both the equivalent thicknesses (T_eq) for the tapered Ta₂O₅ layers and a severe dependence of RI sensitivity on T_eq were obtained. Addition of 1 g of water in 100 g of a Chinese liquor (alcohol concentration = 46% (v/v)) was easily detected by the sensor. Monitoring of anti-human IgG adsorption with a waveguide of T_eq = 31.99 nm indicates that the antibody coverage required for inducing a phase-different change of Δ? = π is less than 0.012 monolayer. The same waveguide presents a quasi-linear dependence of Δ? on water temperature with the slope of d(Δ?)/dT = −28.50°/°C to which the contribution by the thermo-optical effect of the waveguide is 4.24°/°C, equivalent to a liquid RI change of Δn_c = 1.41 × 10⁻⁵. The interferometer exhibits the promising potential for chemical and biological analyses because of its outstanding characteristics.     

Remote sensing assessment of sediment re-suspension during Hurricane Frances in Apalachicola Bay, USA

Assessments of hurricane-induced environmental impacts are important to coastal management and risk analysis of ecosystems. In this study, a previously-developed remote sensing model for non-hurricane conditions by Wang et al. [Wang, H. Q., Hladik, C. M., Milla, K., Huang, W. R., Edmiston, L., Harwell, M. A., & Schalles, J. F. (in press). Detecting and mapping water quality indicators in Apalachicola Bay, Florida using MODIS Terra 250-m imagery. International Journal of Remote Sensing] has been substantially enhanced to investigate the impact of Hurricane Frances on total suspended solid (TSS) concentrations in Apalachicola Bay, Florida, USA. The remote sensing model uses 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) to map TSS concentrations in the Bay. Eleven additional satellite imageries of MODIS were used in the model improvement and calibration. TSS concentration computation in the present model has been substantially improved by using a two-step process: firstly producing atmospheric correction intercept by an approach of in-water reflectance regression, and then building the regression model (R² = 0.8534, n = 25) between 250-m MODIS reflectance and observed TSS concentrations, which includes an extreme high TSS concentration data of 208 mg/L for severe storm or hurricane condition. Also, we carried out the validation of model (RMSE = 5.5 mg/L, n = 21). MODIS-derived TSS maps show substantial increases of TSS concentrations in the Bay during the passage of Hurricane Frances (the average TSS and maximum concentration about 54.3 mg/L and 165 mg/L in the Bay respectively) compared to under no-storm or -hurricane condition ( the average TSS and maximum concentration were approximately 24-27 mg/L and 58-64 mg/L). In comparison to those before and 5-days after the passage of the hurricane, the average TSS concentration in the Bay was twice higher while the maximum TSS concentration increased almost three times during the hurricane. This indicates that strong winds during the hurricane have caused strong sediment re-suspension. The spatial variations of TSS concentrations were analyzed by applying the hydrodynamic characteristics of wind-induced flow and tidal currents as described by Huang [Huang, W., Jones, K., & Wu, T. (2002). Modeling surface wind effects on subtidal salinity in Apalachicola Bay. Estuarine, Coastal and Shelf Science, 55(1), 33−46; Huang, W., Sun, H., Nnaji, S., & Jones, K. (2002). Tidal hydrodynamics in a multiple inlet estuary: Apalachicola Bay. International Journal of Coastal Research, 18(4), 674−684], which show westward currents in the Bay under westward wind condition. Therefore, the southwestward wind (about 50° from the north) during the hurricane induced southwestward currents and transport that resulted in the high TSS concentrations near West Pass in the Bay and the Gulf. Within the Bay, TSS concentrations were generally higher in the southern portion of the Bay, which was due mainly to transport by the combination of southwestward wind and southward residual flow from the Apalachicola River.     

Influence of doping BiYO3 and Nb2O5 on PTCR characteristics of BaTiO3 thermistor ceramics

Nb₂O₅-doped (1 − x)Ba_0.96Ca_0.04TiO₃-xBiYO₃ (where x = 0.01, 0.02, 0.03 and 0.04) lead-free PTC thermistor ceramics were prepared by a conventional solid state reaction method. X-ray diffraction, scanning electron microscope, Agilent E4980A and resistivity-temperature measurement instrument, were used to characteristic the lattice distortion, microstructure, temperature dependence of permittivity and resitivity-temperature dependence. It was revealed that the tetragonality c/a of the perovskite lattice, the microstructure and the Curie temperature changed with the BiYO₃ content. In order to decrease the room temperature resistivity, the effect of Nb₂O₅ on the room temperature resistivity was also studied, and its optimal doping content was finally chosen as 0.2 mol%. The 0.97Ba_0.96Ca_0.04TiO₃-0.03BiYO₃-0.002Nb₂O₅ thermistor ceramic exhibited a low ρ_RT of 3.98 × 10³ Ω cm, a typical PTCR effect of ρ_max/ρ_min > 10³ and a T_c of 153 °C.     

Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand

Quantification of the magnitude of net terrestrial carbon (C) uptake, and how it varies inter-annually, is an important question with future potential sequestration influenced by both increased atmospheric CO₂ and changing climate. However the assessment of differences in measured and modeled C accumulation is a challenging task due to the significant fine scale variation occurring in terrestrial productivity due to soil, climate and vegetation characteristics as well as difficulties in measuring carbon accumulation over large spatial areas. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a means of monitoring gross primary production (GPP), both spatially and temporally, routinely from space. However it is critical to compare and contrast the temporal dynamics of the C and water fluxes with those measured from ground-based networks, or estimated using physiological models. In this paper, using a number of approaches, our objective is to determine if any systematic biases exists in either the MODIS, or the modeled estimates of fluxes, relative to the measurements made over an evergreen, needleleaf temperate rainforest on Vancouver Island, Canada. Results indicate that 8-day GPP as predicted with a simple physiological model (3PGS), forced using local meteorology and canopy characteristics, matched measured fluxes very well (r² = 0.86, p < 0.001) with no significant difference between eddy covariance (EC) and modeled GPP (p < 0.001). In addition, modeled water supply closely matched measured relative available soil water content at the site. Using canopy characteristics from the MODIS fraction of photosynthetically active radiation (fPAR) algorithm, slightly reduced the correspondence of the predictions due to a large number of unsuccessful retrievals (83%) due to sun angle, snow and cloud. Predictions of GPP based on the MODIS GPP algorithm, forced using local meteorology and canopy characteristics, were also highly correlated with EC measurements (r² = 0.89, p < 0.001) however these estimates were biased under predicting GPP. Estimates of GPP based on the most recent MODIS reprocessing (collection 4.5) remained highly correlated (r² = 0.88, p < 0.001) yet were also the most biased with the estimates being 30% less than the EC-measured GPP. Most of the variance in GPP at the site was explained by the absorbed photosynthetically active radiation. We also compared the nighttime respiration as measured over 2 years at the site with the minimum 8-day MODIS land surface temperature and found a significant relationship (r² = 0.57), similar to other studies.     

PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments

The PROSPECT leaf optical model has, to date, combined the effects of photosynthetic pigments, but a finer discrimination among the key pigments is important for physiological and ecological applications of remote sensing. Here we present a new calibration and validation of PROSPECT that separates plant pigment contributions to the visible spectrum using several comprehensive datasets containing hundreds of leaves collected in a wide range of ecosystem types. These data include leaf biochemical (chlorophyll a, chlorophyll b, carotenoids, water, and dry matter) and optical properties (directional-hemispherical reflectance and transmittance measured from 400 nm to 2450 nm). We first provide distinct in vivo specific absorption coefficients for each biochemical constituent and determine an average refractive index of the leaf interior. Then we invert the model on independent datasets to check the prediction of the biochemical content of intact leaves. The main result of this study is that the new chlorophyll and carotenoid specific absorption coefficients agree well with available in vitro absorption spectra, and that the new refractive index displays interesting spectral features in the visible, in accordance with physical principles. Moreover, we improve the chlorophyll estimation (RMSE = 9 µg/cm²) and obtain very encouraging results with carotenoids (RMSE = 3 µg/cm²). Reconstruction of reflectance and transmittance in the 400-2450 nm wavelength domain using PROSPECT is also excellent, with small errors and low to negligible biases. Improvements are particularly noticeable for leaves with low pigment content.     

Multiple shadow fractions in spectral mixture analysis of a cotton canopy

Shadows are being used more frequently to estimate plant canopy biophysical characteristics. Typically, a zero value is assumed or a threshold value is derived from histogram analysis of imagery to determine the shadow endmember (EM). Here, two distinct shadow EMs were measured in situ for use in spectral mixture analysis of a cotton canopy on five dates in 2003. The four EMs used in the analysis were: sunlit green leaf, sunlit dry soil, self-shadowed leaf, shadowed dry soil. This 4-EM model was compared to a 3-EM model where a zero-value shade EM was used for unmixing with the two sunlit EMs. Multiple endmember spectral mixture analysis (MESMA) was used to allow EM composition to vary across each scene. The analysis and EMs were applied to fine-scale hyperspectral image data collected in the wavelength range, 440 to 810 nm. Ground data collected included percent cover, height, SPAD (a measure of leaf greenness), and chlorophyll a concentration. The normalized difference vegetation index (NDVI) was also compared to the unmixing results. Regression analysis showed that NDVI was equal to the 4-EM model for estimation of percent cover (r² = 0.95, RMSE = 6.6) although the NDVI y-intercept was closer to zero. The 4-EM model was best for estimating height (r² = 0.79, RMSE = 0.07 m) and chlorophyll a concentration (r² = 0.46, RMSE = 7.0 μg/cm²). The 3-EM model and NDVI performed poorly when estimating chlorophyll a concentration. Inclusion of two distinct shadow EMs in the model improved relationships to crop biophysical parameters and was better than assuming one, zero-value shade EM. Since MESMA operates at the pixel level and allows variable EM assignment to each pixel, mapping the spatial variability of shadows and other variables of interest is possible, providing a powerful input to canopy and ecosystem models as well as precision farming.     

Electrical conductivity dependence of Ni doped Sm0.95Ce0.05FeO3−δ on surface morphology and composition

Sm_0.95Ce_0.05Fe_1−xNi_xO_3−δ materials are considered as candidates for sensing reducing gases. The total electrical conductivity of Ni doped Sm_0.95Ce_0.05FeO_3−δ perovskite materials is discussed in terms of Ni concentration, surface morphology and relative surface atomic ratios. Powders of formula Sm_0.95Ce_0.05Fe_1−xNi_xO_3−δ (x = 0-0.10) were prepared from citrate precursors by using a sol gel method and were then pressed uniaxially and sintered at 1350 °C for 4 h to form pellets. In fresh pellets the relative surface atomic ratios of Sm and Ni increased while that of Fe and Ce decreased as a function of nickel concentration, showing the segregation of samarium species. In contrast, the chemically reduced pellets show Fe rich surfaces. The electrical conductivity of fresh, partially reduced (700 °C under 5% (v/v) H₂/N₂ for 1 h) and fully reduced (1000 °C under 5% (v/v) H₂/N₂ for 1 h) pellets was measured by the four probe DC method.Under air, x = 0.07 and x = 0.10 showed the highest electrical conductivity in the series. Interestingly the x = 0.01-0.05 materials were n-type conductors while x = 0.07-0.10 exhibited p-type behaviour. The reduction treatment at 1000 °C enhanced electrical conductivities up to ∼5000 fold due to changes associated with surface morphology and surface elemental composition. While phase separations are usually detrimental, in this case the reduced sensors are more sensitive without sacrificing reproducibility.     

Near-surface thermal structure and surface diurnal warming in the Adriatic Sea using satellite and drifter data

An extensive set of in situ temperature data collected by surface drifters is combined with satellite-derived sea surface temperature images to study the difference between the pseudo-bulk and bulk temperatures (ΔT_pb-b) in the Adriatic Sea for the period 21 September 2002-31 December 2003. The variations of this temperature difference are described as a function of local wind speed and incoming solar radiation provided by a local area atmospheric model. The daily sea surface temperature variability is also assessed by computing the temperature difference between the daily maximal and minimal values (ΔT_day-night). The data show that the smaller the wind speed and the larger the solar radiation, the larger ΔT_pb-b. The temperature difference reached the highest value (∼5 °C) on a hot day (more than 600 W/m²) of May 2003 in weak wind condition (around 3 m/s). For strong winds (speed > 6 m/s) the dependence on both the wind and solar radiations vanishes as the temperature difference approaches zero because the near-surface water becomes thermally homogenous due to the wind-induced vertical mixing. Strong diurnal warming of the sea surface, as derived by the pseudo-bulk estimates, and a strong near-surface stratification were found during the spring/summer season. Monthly mean statistics show that the diurnal cycle of the pseudo-bulk and bulk temperature starts to become significant already in February and March. Subsequently (from April to August) both the diurnal warming and the stratification are maximal (monthly means of ΔT_day-night ∼1-2 °C and of ΔT_pb-b ∼0.5 °C ), while in fall and early winter the ΔT_pb-b values are quite small (monthly means near 0 °C) and the ΔT_day-night monthly means are bounded by 0.5-1.5 °C. Maximal amplitudes of the diurnal cycle can exceed 4 °C (mostly in spring-summer) for both the pseudo-bulk and bulk temperatures. However, the monthly means of ΔT_day-night is generally twice as large for the pseudo-bulk estimates (∼2 °C) with respect to the bulk layer (∼1 °C). The diurnal warming of the sea surface, as derived by the pseudo-bulk temperature, occurs at about 14:30 local time, that is more than 2 h after the maximal sun elevation and an hour earlier than the bulk temperature maximum at 20-40 cm depth.     

Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters

Near real-time data from the MODIS satellite sensor was used to detect and trace a harmful algal bloom (HAB), or red tide, in SW Florida coastal waters from October to December 2004. MODIS fluorescence line height (FLH in W m^− 2 μm^− 1 sr^− 1) data showed the highest correlation with near-concurrent in situ chlorophyll-a concentration (Chl in mg m^− 3). For Chl ranging between 0.4 to 4 mg m^− 3 the ratio between MODIS FLH and in situ Chl is about 0.1 W m^− 2 μm^− 1 sr^− 1 per mg m^− 3 chlorophyll (Chl = 1.255 (FLH × 10)^0.86, r = 0.92, n = 77). In contrast, the band-ratio chlorophyll product of either MODIS or SeaWiFS in this complex coastal environment provided false information. Errors in the satellite Chl data can be both negative and positive (3-15 times higher than in situ Chl) and these data are often inconsistent either spatially or temporally, due to interferences of other water constituents. The red tide that formed from November to December 2004 off SW Florida was revealed by MODIS FLH imagery, and was confirmed by field sampling to contain medium (10⁴ to 10⁵ cells L^− 1) to high (> 10⁵ cells L^− 1) concentrations of the toxic dinoflagellate Karenia brevis. The FLH imagery also showed that the bloom started in mid-October south of Charlotte Harbor, and that it developed and moved to the south and southwest in the subsequent weeks. Despite some artifacts in the data and uncertainty caused by factors such as unknown fluorescence efficiency, our results show that the MODIS FLH data provide an unprecedented tool for research and managers to study and monitor algal blooms in coastal environments.     

Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa

The estimation of near surface air temperature (Ta) is useful for a wide range of applications such as agriculture, climate related diseases and climate change studies. Air temperature is commonly obtained from synoptic measurements in weather stations. In Africa, the spatial distribution of weather stations is often limited and the dissemination of temperature data is variable, therefore limiting their use for real-time applications. Compensation for this paucity of information may be obtained by using satellite-based methods. However, the derivation of near surface air temperature (Ta), from the land surface temperature (Ts) derived from satellite is far from straight forward. Some studies have tried to derive maximum Ta from satellites through regression analysis but the accuracy obtained is quite variable according to the study. The main objective of this study was to explore the possibility of retrieving high-resolution Ta data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Ts products over different ecosystems in Africa. First, comparisons between night MODIS Ts data with minimum Ta showed that MODIS nighttime products provide a good estimation of minimum Ta over different ecosystems (with (ΔTs − Ta) centered at 0 °C, a mean absolute error (MAE) = 1.73 °C and a standard deviation = 2.4 °C). Secondly, comparisons between day MODIS Ts data with maximum Ta showed that (ΔTs − Ta) strongly varies according to the seasonality, the ecosystems, the solar radiation, and cloud-cover. Two factors proposed in the literature to retrieve maximum Ta from Ts, i.e. the Normalized Difference Vegetation Index (NDVI) and the Solar Zenith Angle (SZA), were analyzed. No strong relationship between (ΔTs − Ta) and (i) NDVI and (ii) SZA was observed, therefore requiring further research on robust methods to retrieve maximum Ta.     

Detection of Asia dust storms using multisensor satellite measurements

Observations from visible, infrared and microwave satellite instruments are integrated to detect dust storm over northwestern China. Microwave measurements are used to detect the dust storm underneath ice clouds, while visible and infrared measurements are utilized for delineating the cloud-free dust systems. Detection is based on microwave polarized brightness temperature differences (ΔT_b = T_bv − T_bh) among two channels of 89 GHz and 23.8 GHz and infrared brightness temperature difference (BTD) between channels at 11 and 12 μm. It is shown that the integrated approach is better than the method solely based on infrared BTD in storm detection, especially for those dust systems covered by ice clouds. This approach is applied for the Asia dust storms cases using the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer (AMSR-E) onboard Aqua satellite.     

MODIS tree cover validation for the circumpolar taiga-tundra transition zone

A validation of the 2005 500 m MODIS vegetation continuous fields (VCF) tree cover product in the circumpolar taiga-tundra ecotone was performed using high resolution Quickbird imagery. Assessing the VCF's performance near the northern limits of the boreal forest can help quantify the accuracy of the product within this vegetation transition area. The circumpolar region was divided into 7 longitudinal zones and validation sites were selected in areas of varying tree cover where Quickbird imagery is available in Google Earth. Each site was linked to the corresponding VCF pixel and overlaid with a regular dot grid within the VCF pixel's boundary to estimate percent tree crown cover in the area. Percent tree crown cover was estimated using Quickbird imagery for 396 sites throughout the circumpolar region and related to the VCF's estimates of canopy cover for 2000-2005. Regression results of VCF inter-annual comparisons (2000-2005) and VCF-Quickbird image-interpreted estimates indicate that: (1) Pixel-level, inter-annual comparisons of VCF estimates of percent canopy cover were linearly related (mean R² = 0.77) and exhibited an average root mean square error (RMSE) of 10.1% and an average root mean square difference (RMSD) of 7.3%. (2) A comparison of image-interpreted percent tree crown cover estimates based on dot counts on Quickbird color images by two different interpreters were more variable (R² = 0.73, RMSE = 14.8%, RMSD = 18.7%) than VCF inter-annual comparisons. (3) Across the circumpolar boreal region, 2005 VCF-Quickbird comparisons were linearly related, with an R² = 0.57, a RMSE = 13.4% and a RMSD = 21.3%, with a tendency to over-estimate areas of low percent tree cover and anomalous VCF results in Scandinavia. The relationship of the VCF estimates and ground reference indicate to potential users that the VCF's tree cover values for individual pixels, particularly those below 20% tree cover, may not be precise enough to monitor 500 m pixel-level tree cover in the taiga-tundra transition zone.     

Evaluation of MODIS land surface temperature products for daily air surface temperature estimation in northwest Vietnam

Recently, the MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) is considered one of the most suitable ways to retrieve air surface temperature (T_a) – one of the most important and widely used climate variables for a wide range of applications. In fact, many successful studies have been reported in many regions of the world. Each day, four MODIS LST data are available; from two sensors (Terra and Aqua) at two local overpass times (daytime and night-time). However, due to their different overpass times, most studies have used LST daytime and night-time for daily maximum (T_max) and minimum (T_min) air surface temperature estimation, respectively. Therefore, the performance of each individual LST data, the effect of the dynamic combination of these four LST data, the effect of land surface characteristics, and the effect of LST quality on the same estimation in the same region on the accuracy of estimated T_a remains unclear. In this study, we evaluated and tested all individual LST data as well as all possible combinations of the four MODIS LST data from two separate stations with distinct land surface characteristics in northwest Vietnam for 10 years (from 2004 to 2013) under two sky conditions (all clear sky conditions and only good data – i.e. Quality Control (QC) value of 0) for daily T_a (T_max, T_min, and T_mean) estimations. In addition, the mixed data of the two stations were also evaluated. Our results showed that Terra LST data have a higher correlation with T_a than Aqua LST; which is consistent for both stations and both quality conditions (all clear sky and only good data). A closer overpass time with T_max or T_min occurrence time did not guarantee a higher accuracy of T_a estimation. Using only good LST data produced a higher accuracy of T_a estimation than using all clear sky data. However, if the percentage of good data is low (i.e. less than 30%), then the all clear sky data will provide better results for T_max estimation. Comparing the performance of the different combinations when using the single station and mixed station data, combinations including at least one night-time LST produced stable and high accuracy T_min and T_mean estimates, while the combinations with only daytime LST produced very low accuracy results. For T_max estimation, the results were less impacted by LST quality; however, they were strongly impacted by different combinations and land surface characteristics.     

Nano particulate SnO2 based resistive films as a hydrogen and acetone vapour sensor

SnCl₂ (solution) was spin coated on soda lime glass and Al₂O₃ substrate to obtain nano-particulate tin oxide film, directly by sintering at 550 °C for 40 minutes (min). The surface morphology and crystal structure of the tin oxide films were analyzed using atomic force microscopy (AFM) and X-ray diffraction (XRD). The size of SnO₂ nanostructure was determined from UV-vis and found to be ?3 nm. These films were tested for sensing H₂ concentration of 0.1-1000 ppm at optimized operating temperature of 265 °C. The results showed that sensitivity (R_air/R_gas per ppm) goes on increasing with decreasing concentration of test gas, giving concentration dependent changes. Special studies carried out at low concentration levels (0.1-1 and 1-10 ppm) of H₂, give high sensitivity (200 × 10⁻³/ppm) for lowest concentration (0.1-1 ppm) of H₂. The selectivity for H₂ against relative humidity (RH), CO₂, CO and LPG gases is also good. The sensor, at operating temperature of 200 °C, is showing nearly zero response to 300 ppm of H₂, and offering response to acetone vapour of 11 ppm. Selectivity for acetone against RH% and CO₂ was also studied. These sensors can be used as H₂ sensor at an operating temperature of 265 °C, and as an acetone sensor at the operating temperature of 200 °C.     

Regional evaporation estimates from flux tower and MODIS satellite data

Two models were evaluated for their ability to estimate land surface evaporation at 16-day intervals using MODIS remote sensing data and surface meteorology as inputs. The first was the aerodynamic resistance-surface energy balance model, and the second was the Penman-Monteith (P-M) equation, where the required surface conductance is estimated from remotely-sensed leaf area index. The models were tested using 3 years of evaporation and meteorological measurements from two contrasting Australian ecosystems, a cool temperate, evergreen Eucalyptus forest and a wet/dry, tropical savanna. The aerodynamic resistance-surface energy balance approach failed because small errors in the radiative surface temperature translate into large errors in sensible heat, and hence into estimates of evaporation. The P-M model adequately estimated the magnitude and seasonal variation in evaporation in both ecosystems (RMSE = 27 W m^− 2, R² = 0.74), demonstrating the validity of the proposed surface conductance algorithm. This, and the ability to constrain evaporation estimates via the energy balance, demonstrates the superiority of the P-M equation over the surface temperature-based model. There was no degradation in the performance of the P-M model when gridded meteorological data at coarser spatial (0.05°) and temporal (daily) resolution were substituted for locally-measured inputs.The P-M approach was used to generate a monthly evaporation climatology for Australia from 2001 to 2004 to demonstrate the potential of this approach for monitoring land surface evaporation and constructing monthly water budgets from 1-km to continental spatial scales.