MODIS Satellite Sensor Research Articles

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

AbstractThis study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8‐day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission.

Contributions of Biomass Burning Events to Daily PM10 Concentrations and The Short-Term Associations with Hospital Admissions in Malaysia

Background and Aim: Despite serious biomass burning in the Southeast Asian region, there is a lack of epidemiological studies that directly evaluate the health effects of particulate matter attributed to biomass burning. This study aims to estimate the short-term effects of particulate matter ≤10µm (PM₁₀) on hospital admissions in Peninsular Malaysia, distinguishing between PM₁₀ originating from biomass burning and other sources. Methods: We identified biomass burning by evaluating daily hotspot information from MODIS satellite sensor and the HYSPLIT backward wind trajectories at five districts during 2005 – 2015. Days with wind trajectories passing through biomass burning were known as "haze days”. We estimated PM₁₀ concentrations attributed to biomass burning by decomposing total concentrations using the moving mean of PM₁₀ on non-haze days. We fitted district-specific quasi-Poisson regression models to estimate associations of PM₁₀ attributable to biomass burning and non-biomass burning with daily hospitalizations due to respiratory and cardiovascular diseases (ICD-10: J00-J99 and I00-I99, respectively). To obtain a regional average, we pooled district-specific results using random-effects meta-analysis. Results: We analyzed respiratory (210,960 cases) and cardiovascular (178,952 cases) hospitalizations. On average, 50% of study period was identified as haze days (PM₁₀ due to biomass burning >0µg/m³). Average PM₁₀ levels attributed to biomass burning and non-biomass burning were 6.6µg/m³ (SD: 51.7) and 41.6µg/m³ (SD: 20.6) respectively. Increases of 10µg/m³ biomass burning PM₁₀ and non-biomass burning PM₁₀ during non-haze day (lag 0–1 days) were associated with increases in respiratory hospital admissions of 0.44% (95% CI: 0.06, 0.82%) and 0.86% (95% CI: 0.31, 1.41%), respectively. We could not see any significant associations for cardiovascular hospital admissions. District-specific estimates of the associations appeared to be homogeneous. Conclusions: PM₁₀ from both biomass burning and other emission sources were positively associated with respiratory hospitalizations in Peninsular Malaysia. Keywords: Biomass burning event, Particulate matter, Hospital admission, Respiratory disease

Forest browning trends in response to drought in a highly threatened mediterranean landscape of South America

• Effect on forest NDVI of recent 7 years “mega-drought” was assessed. • One-third of Mediterranean forest showed a significant NDVI decrease during drought. • Local wet conditions provide drought refuges. • Browning occurs in communities with species showing different drought resistance. • Potential resistance loss is suggested by high browning in the driest zones. Deforestation is widely studied throughout the world. However, a less evident issue is the effect of climate change and drought on remnants of native forests. The objective of this work was to understand the geographic variations in resistance to drought of the Mediterranean sclerophyllous forests of central Chile. These forests have been historically reduced and fragmented and in recent years were subjected to the most prolonged drought occurred between 2010 and 2017. Using data from the MODIS satellite sensor, temporal trends in the NDVI (Normalized Difference Vegetation Index) were quantified. We related these trends with different environmental variables to understand the effects of geographical variation and forest type as indicators of resistance to drought. We observed a significant direct effect of drought, attributable to the reduced precipitation in central Chile, and a significantly reduced NDVI in near one-third of the region forests (browning). However, NDVI and therefore forest productivity were more stable in some mesic sites such as ravine bottoms, but not on south-facing slopes. This suggests that under a regime of reduced precipitations, a greater available soil humidity would be a more important factor than the fact of receiving less solar radiation. Finally, the highest degree of browning was observed in semi-arid sclerophyllous forest dominated by species tolerant to drought. Our findings emphasize the need to consider topographic site conditions to adequately assess forest productivity and vulnerability where local wet conditions could provide drought refuges. This recent drought may be analogous to forecasted warmer and drier climate conditions with more frequent and severe droughts, so our results may serve as a general framework for climate-smart decisions in highly threatened forest restoration and conservation.

Detection of Changes in Terrestrial Ecosystems of Ukraine Using Remote Sensing Data

In recent years, Ukraine has been affected by climate change. This has led to frequent extreme weather events (heavy / high rains, floods, droughts, squalls). As a result of droughts, desertification is one of the most dangerous and transient consequences of modern climate change. The research is devoted to the diagnostic assessment of the modern climate of Ukraine. Remote sensing data and instrumental observations of 30 weather stations of Ukraine were used. Temperature increase was registered in the study area by all stations, which significantly affected the level of precipitation. At the moment there is not enough moisture for the Earth’s surface. Precipitation in Ukraine is currently characterized by an uneven distribution. It leads to accelerated processes of soil degradation and it’s fertility loss. The aim of the study was to identify areas prone to desertification using satellite imagery and meteorological observations. Over the past 17 years (2000-2017), the average air temperature in Ukraine has increased by 1.5 ºC. Particularly anomalous warming has been recorded in recent years, starting in 2015. During the XXI century, a slight decrease in precipitation was observed in Ukraine. Both a decrease in precipitation and an increase in temperature may lead to a decrease in soil moisture levels. According to ground meteorological data, the tendency of dryness in Ukraine was confirmed. Lack of water leads to prompt manifestation of this process. Water indexes were used to estimate the moisture content of surface soils. It is possible to assess the susceptibility of the desert area to climate change. Relevant quantitative information on water availability in Ukraine is provided. Two water indices (Normalized Difference Infrared Index NDII and Ratio Drought Index RDI) have been taken estimate the moisture content. It can be estimated from the MODIS MOD13C2 product data obtained from the MODIS satellite sensor and used for regional research. The main conclusion of this study is to determine the changes in natural terrestrial ecosystems in Ukraine. This was shown on the basis of temperature and humidity. Such trends may lead to changes in the biodiversity of the territory and loss of natural soil properties.

Spatio-temporal variations in satellite based aerosol optical depths & aerosol index over Indian subcontinent: Impact of urbanization and climate change

All season multiyear characterization of the aerosol optical depths (AOD) & absorbing aerosol index (AI) was carried out over Indian subcontinent using simultaneous data from MODIS satellite sensors. Significant spatio-temporal heterogeneity was found in the aerosol optical depths over Indian subcontinent. Latitudinal, MODIS derived Aerosol optical depths (AOD), its Small mode fraction (SMF, except pre monsoon) has shown increasing trends towards northern latitudes of Indian subcontinent. Pre monsoon mean AI values were found to increase linearly from a minimum of ~0.52 over the southern India region to a maximum of 3 over the central India region head at around 26.50 N at a rate of 0.001 per degree latitude while there was an exponential decay during winter and post monsoon. Longitudinally, the variations in the aerosol properties are subdued comparatively. SMF values tend to increase weakly towards eastern longitudes during all the seasons. Variations in the aerosol properties are parameterised using best fit analytical functions. By simultaneous examination of AI, AOD and SMF, it was inferred that the aerosol load was found responsible for the higher values of AI over Indian subcontinent during pre-monsoon which was further dominated by the transported UV-absorbing dust.

Integrating camera imagery, crowdsourcing, and deep learning to improve high-frequency automated monitoring of snow at continental-to-global scales.

Snow is important for local to global climate and surface hydrology, but spatial and temporal heterogeneity in the extent of snow cover make accurate, fine-scale mapping and monitoring of snow an enormous challenge. We took 184,453 daily near-surface images acquired by 133 automated cameras and processed them using crowdsourcing and deep learning to determine whether snow was present or absent in each image. We found that the crowdsourced data had an accuracy of 99.1% when compared with expert evaluation of the same imagery. We then used the image classification to train a deep convolutional neural network via transfer learning, with accuracies of 92% to 98%, depending on the image set and training method. The majority of neural network errors were due to snow that was present not being detected. We used the results of the neural networks to validate the presence or absence of snow inferred from the MODIS satellite sensor and obtained similar results to those from other validation studies. This method of using automated sensors, crowdsourcing, and deep learning in combination produced an accurate high temporal dataset of snow presence across a continent. It holds broad potential for real-time large-scale acquisition and processing of ecological and environmental data in support of monitoring, management, and research objectives.

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

Comparative assessment of spatiotemporal snow cover changes and hydrological behavior of the Gilgit, Astore and Hunza River basins (Hindukush–Karakoram–Himalaya region, Pakistan)

The Upper Indus Basin (UIB), situated in the Himalaya–Karakoram–Hindukush (HKH) mountain ranges, is the major contributor to the supply of water for irrigation in Pakistan. Improved management of downstream water resources requires studying and comparing spatiotemporal changes in the snow cover and hydrological behavior of the river basins located in the HKH region. This study explored in detail the recent changes that have occurred in the Gilgit River basin (12,656 km2; western sub-basin of UIB), which is characterized by a mean catchment elevation of 4250 m above sea level (m ASL). The basin’s snow cover was monitored through the snow products provided by the MODIS satellite sensor, while analysis of its hydrological regime was supported by hydrological and climatic data recorded at different altitudes. The Gilgit basin findings were compared to those previously obtained for the lower-altitude Astore basin (mean catchment elevation = 4100 m ASL) and the higher-altitude Hunza basin (mean catchment elevation = 4650 m ASL). These three catchments were selected because of their different glacier coverage, contrasting area distribution at high altitudes and significant impact on the Upper Indus River flow. Almost 7, 5 and 33 % of the area of the Gilgit, Astore and Hunza basins, respectively, are situated above 5000 m ASL, and approximately 8, 6 and 25 %, respectively, are covered by glaciers. The UIB region was found to follow a stable or slightly increasing trend in snow coverage and had a discharge dominated by snow and glacier melt in its western (Hindukush–Karakoram), southern (Western-Himalaya) and northern (Central-Karakoram) sub-basins.

Lidar Measurements of Canadian Forest Fire Smoke Episode Observed in July 2013 over Warsaw, Poland

This paper presents a preliminary study of aerosol optical properties of air-mass advected on 10 th July 2013 from Canada above Warsaw, Poland, during the forest fire event that occurred in Quebec at the beginning of July 2013. The observations were conducted with use of the modern version of 8-channel Polly XT lidar capable of measuring at 3β+2α+2δ+VW and interpreted with available information from the MACC model, the CALIPSO and MODIS satellite sensors, the AERONET data products and the data gathered within the Poland-AOD network.

Potential role of ignition management in reducing unplanned burning in Arnhem Land, Australia

AbstractFire management attempts to coerce fire into a desired regime using three primary strategies: prescribed burning, fire suppression and ignition management. The West Arnhem Land Fire Abatement project (WALFA), where prescribed Early Dry Season burning is used to reduce unplanned Late Dry Season burning, is heralded as model for prescribed burning. However, a previous analysis found that Late Dry Season area burnt in WALFA had been reduced further than would be expected based purely on the Early Dry Season treatment area. This study investigated whether treatment has reduced the number and size of unplanned fires. Daily burnt area mapping from MODIS satellite sensors was used to identify individual fires to compare fire activity before and after management was introduced in WALFA (2005) and in a control region in East Arnhem Land. Late Dry Season area burnt reduced after treatment in WALFA but also in the control region. The number of fires in August–October increased after treatment. There is a period from early August until late September when human ignitions can cause huge fires. Late Dry Season area burnt was strongly influenced by the size of the largest single fire and only weakly by the number of ignitions. Early Dry Season area burnt had modest effects on both the number and maximum size of Late Dry Season fires. Eliminating the largest fire in each 1600 km2 sample block would have halved the total Late Dry Season area burnt. A similar reduction could be obtained from a 14% annual treatment with Early Dry Season fire, but this may not reduce the overall area burnt. If overall fire frequency is the main threat to biodiversity in the savannas, then the best solution will be to prevent the small subset of fires that have the potential to become very large.

Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland

Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.

Is Switzerland suitable for the invasion of Aedes albopictus [corrected

BackgroundOver the last 30 years, the Asian tiger mosquito, Aedes albopictus, has rapidly spread around the world. The European distribution comprises the Mediterranean basin with a first appearance in Switzerland in 2003. Early identification of the most suitable areas in Switzerland allowing progressive invasion by this species is considered crucial to suggest adequate surveillance and control plans.Methodology/Principal FindingsWe identified the most suitable areas for invasion and establishment of Ae. albopictus in Switzerland. The potential distribution areas linked to the current climatic suitability were assessed using remotely sensed land surface temperature data recorded by the MODIS satellite sensors. Suitable areas for adult survival and overwintering of diapausing eggs were also identified for future climatic conditions, considering two different climate change scenarios (A1B, A2) for the periods 2020–2049 and 2045–2074. At present, the areas around Lake Geneva in western Switzerland provide suitable climatic conditions for Ae. albopictus. In northern Switzerland, parts of the Rhine valley, around Lake Constance, as well as the surroundings of Lake Neuchâtel, appear to be suitable for the survival at least of adult Ae. albopictus. However, these areas are characterized by winters currently being too cold for survival and development of diapausing eggs. In southern Switzerland, Ae. albopictus is already well-established, especially in the Canton of Ticino. For the years 2020–2049, the predicted possible spread of the tiger mosquito does not differ significantly from its potential current distribution. However, important expansions are obtained if the period is extended to the years 2045–2074, when Ae. albopictus may invade large new areas.Conclusions/SignificanceSeveral parts of Switzerland provide suitable climatic conditions for invasion and establishment of Ae. albopictus. The current distribution and rapid spread in other European countries suggest that the tiger mosquito will colonize new areas in Switzerland in the near future.

월면 디지털 영상 분석을 이용한 대기 광학두께 산출

이 연구에서는 상용 디지털 카메라를 이용하여 야간에 촬영된 월면 영상을 분석하여 야간의 대기 광학두께와 에어로솔 광학두께를 추정하였다. 기본적으로 랑리회귀법을 이용하였으며 구름이 없고 대기의 광학적 특성이 비교적 안정한 날에 관측을 수행하였다. 카메라의 적색(R), 녹색(G), 청색(B) 채널의 파장별 반응함수를 이용하여 월광관측에 대한 유효 중심파장 및 레일리 광학두께를 추정하였으며, 랑리 회귀법에서 유도된 대기광학두께로부터 레일리 광학두께를 제하여 에어로솔 광학 두께를 산출하였다. 야간에는 독립적인 방법으로 산출된 검증자료나 다른 에어로솔 광학두께 자료가 거의 없으므로 월면 관측이 이루어지기 수 시간 전의 주간에 정밀한 태양분광광도계로 측정된 에어로솔 광학두께 자료와 MODIS 위성센서 관측으로부터 산출된 에어로솔 광학두께 자료를 본 연구에서 월면 관측을 통해 산출된 자료와 비교하였다. 비교 결과 R, G, B 채널에서 대략 0.1정도의 오차 범위에서 월면 영상분석을 통해 에어로솔 광학두께의 추정이 가능함을 알 수 있었다. 단, 대기 중의 에어로솔 입자들의 크기를 나타내는 모수인 앙스트롬지수(<TEX>${\AA}$</TEX>ngstr<TEX>$\ddot{o}$</TEX>m Exponent)는 파장별 광학두께의 작은 오차에도 큰 오차를 가질 수 있기 때문에 에어로솔 광학두께의 오차에 비해 비교적 큰 오차를 보일 수 있음이 나타났다. 그럼에도 불구하고, 야간의 에어로솔 광학두께 자료가 많지 않은 현실에서 저비용으로 월면 관측을 통하여 에어로솔 광학두께를 산출할 수 있는 가능성을 찾았다는 점에서 본 연구의 의의가 있으며 앞으로 보다 많은 관측과 분석을 통해 보다 향상된 야간 에어로솔 광학두께 추정이 가능할 것으로 보인다. Atmospheric optical thickness during nighttime was estimated in this study using analysis on the images of the moon taken from commercial digital camera. Basically the Langely Regression method was applied to the observations of the moon for the cloudless and optically stable sky conditions. The spectral response functions for the red(R), green(G), and blue(B) channels were employed to derive effective wavelength centers of each channel for the observations of the moon, and the correspondent Rayleigh optical thickness were also calculated. Aerosol optical thickness (AOT) was calculated by subtracting Rayleigh optical thickness from the atmospheric optical thickness derived from the Langley regression method. As there are only handful of nighttime AOT observations, the AOT from the moon observations was compared with the AOT from sun-photometers and the MODIS satellite sensor, which was taken several hours before the moon observations of this study. As a result, the values of AOT from moon observations agree with those from sun-photometers and MODIS within 0.1 for the R, G, B channels of the digital camera. On the other hand, <TEX>${\AA}$</TEX>ngstr<TEX>$\ddot{o}$</TEX>m Exponent seems to be subject to larger errors due to its sensitiveness to the spectral errors of AOT. Nevertheless, the results of this study indicate that the method reported in this study is promising as it can provide nighttime AOT relatively easily with a low cost instrument like digital camera. More observations and analyses are warranted to attain improved nighttime AOT observations in the future.

Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques

Fire in forested areas can be regarded as an environmental disaster which is triggered by either natural forces or anthropogenic activities. Fires are one of the major hazards in forested and grassland areas in the north of Iran. Control of fire is difficult, but it is feasible to map fire risk by geospatial technologies and thereby minimize the frequency of fire occur- rences and damages caused by fire. The fire risk models provide a suitable concept to understand characterization of fire risk. Some models are map based, and they combine effectively different forest fire-causing variables with remote sensing data in a GIS environ- ment for identifying and mapping forest fire risk. In this study, Structural Fire Index, Fire Risk Index, and a new index called Hybrid Fire Index were used to delineate fire risk in northeastern Iran that is subjected to frequent forest fire. Vegetation moisture, slope, aspect, elevation, distance from roads, and vicinity to settlements were used as the factors influencing accidental fire starts. These indices were set up by assigning subjective weight values to the classes of the layers based on their sensitivity ratio to fire. Hot spots data derived from MODIS satellite sensor were used to validate the indices. Assessment of the indices with receiver operating characteristic (ROC) curves shows that 76.7 % accuracy of the HFI outperformed the other two indices. According to the Hybrid Fire Index, 57.5 % of the study area is located under high-risk zone, 33 % in medium-risk zone, and the remaining 9.5 % area is located in low-risk zone.

Comparing mesoscale chemistry-transport model and remote-sensed Aerosol Optical Depth

A comparison of modeled and observed Aerosol Optical Depth (AOD) is presented. 3D Eulerian multiphase chemistry-transport model TCAM is employed for simulating AOD at mesoscale. MODIS satellite sensor and AERONET photometer AOD are used for comparing spatial patterns and temporal timeseries. TCAM simulations for year 2004 over a domain containing Po-Valley and nearly whole Northern Italy are employed. For the computation of AOD, a configuration of external mixing of the chemical species is considered. Furthermore, a parametrization of the effect of moisture affecting both aerosol size and composition is used. An analysis of the contributions of the granulometric classes to the extinction coefficient reveals the dominant role of the inorganic compounds of submicron size. For the analysis of spatial patterns, summer and winter case study are considered. TCAM AOD reproduces spatial patterns similar to those retrieved from space, but AOD values are generally smaller by an order of magnitude. However, accounting also for moisture, TCAM AOD significantly increases to values of the same magnitude of the observed ones. The temporal performance of model AOD is tested in correspondence of AERONET site "Venise" and some temporal structures are reproduced. The results suggest encouraging perspectives in view of satellite AOD assimilation also at the mesoscale and not only at the global and regional scale as already tested in the literature.

Transport and evolution of a pollution plume from northern China: A satellite‐based case study

On 5 April 2005, during the East Asian Study of Tropospheric Aerosols: An International Regional Experiment (EAST‐AIRE) aircraft campaign, heavy loadings of SO2 (20 ppb near ground, 1–3 ppb at ∼2 km altitude) and dust with aerosol optical depth of ∼1 were measured over Shenyang, an industrialized city ∼600 km NE of Beijing. In this study, Ozone Monitoring Instrument (OMI) and MODIS satellite sensors are employed to look into this air pollution episode at a regional scale and to track the transport and evolution of the plume from China to the NW Pacific on the following days. A method is proposed to combine in situ measurements and trajectory tracer modeling with satellite observations to quantify the change in the SO2 mass during plume transport. We demonstrate that an air mass factor correction is needed for quantitative use of the OMI SO2 data, to account for the effects of the viewing geometry, the SO2 profile shape, and the aerosol/cloud interference on retrievals. The total SO2 loading of the plume decreased from ∼1.1 × 1011 g on 5 April to ∼5.0 × 1010 g on 7 April. The overall, e‐folding lifetime of SO2 in this plume, empirically derived from the rate of SO2 decay, was ∼2 days (range of 1–4 days). SO2 to sulfate conversion increased the aerosol optical depth by ∼0.1–0.4 near the center of the plume on 6 and 7 April, while the loss of primary dust particles reduced the aerosol loading of the plume by a similar amount. Simulations with a chemical transport model suggest similar loss of dust and formation of sulfate within the plume during transport. The method established in this study can be further developed and applied to study other episodes of pollution transport and their impact on weather and climate.

Risk Maps of Lassa Fever in West Africa

BackgroundLassa fever is caused by a viral haemorrhagic arenavirus that affects two to three million people in West Africa, causing a mortality of between 5,000 and 10,000 each year. The natural reservoir of Lassa virus is the multi-mammate rat Mastomys natalensis, which lives in houses and surrounding fields. With the aim of gaining more information to control this disease, we here carry out a spatial analysis of Lassa fever data from human cases and infected rodent hosts covering the period 1965–2007. Information on contemporary environmental conditions (temperature, rainfall, vegetation) was derived from NASA Terra MODIS satellite sensor data and other sources and for elevation from the GTOPO30 surface for the region from Senegal to the Congo. All multi-temporal data were analysed using temporal Fourier techniques to generate images of means, amplitudes and phases which were used as the predictor variables in the models. In addition, meteorological rainfall data collected between 1951 and 1989 were used to generate a synoptic rainfall surface for the same region.Methodology/Principal FindingsThree different analyses (models) are presented, one superimposing Lassa fever outbreaks on the mean rainfall surface (Model 1) and the other two using non-linear discriminant analytical techniques. Model 2 selected variables in a step-wise inclusive fashion, and Model 3 used an information-theoretic approach in which many different random combinations of 10 variables were fitted to the Lassa fever data. Three combinations of absence∶presence clusters were used in each of Models 2 and 3, the 2 absence∶1 presence cluster combination giving what appeared to be the best result. Model 1 showed that the recorded outbreaks of Lassa fever in human populations occurred in zones receiving between 1,500 and 3,000 mm rainfall annually. Rainfall, and to a much lesser extent temperature variables, were most strongly selected in both Models 2 and 3, and neither vegetation nor altitude seemed particularly important. Both Models 2 and 3 produced mean kappa values in excess of 0.91 (Model 2) or 0.86 (Model 3), making them ‘Excellent’.Conclusion/SignificanceThe Lassa fever areas predicted by the models cover approximately 80% of each of Sierra Leone and Liberia, 50% of Guinea, 40% of Nigeria, 30% of each of Côte d'Ivoire, Togo and Benin, and 10% of Ghana.

Spatial and temporal variations of aerosols around Beijing in summer 2006: Model evaluation and source apportionment

Regional aerosol model calculations were made using the Weather Research and Forecasting (WRF)‐Community Multiscale Air Quality (CMAQ) and WRF‐chem models to study spatial and temporal variations of aerosols around Beijing, China, in the summer of 2006, when the Campaigns of Air Quality Research in Beijing and Surrounding Region 2006 (CAREBeijing) intensive campaign was conducted. Model calculations captured temporal variations of primary (such as elemental carbon (EC)) and secondary (such as sulfate) aerosols observed in and around Beijing. The spatial distributions of aerosol optical depth observed by the MODIS satellite sensors were also reproduced over northeast China. Model calculations showed distinct differences in spatial distributions between primary and secondary aerosols in association with synoptic‐scale meteorology. Secondary aerosols increased in air around Beijing on a scale of about 1000 × 1000 km2 under an anticyclonic pressure system. This air mass was transported northward from the high anthropogenic emission area extending south of Beijing with continuous photochemical production. Subsequent cold front passage brought clean air from the north, and polluted air around Beijing was swept to the south of Beijing. This cycle was repeated about once a week and was found to be responsible for observed enhancements/reductions of aerosols at the intensive measurement sites. In contrast to secondary aerosols, the spatial distributions of primary aerosols (EC) reflected those of emissions, resulting in only slight variability despite the changes in synoptic‐scale meteorology. In accordance with these results, source apportionment simulations revealed that primary aerosols around Beijing were controlled by emissions within 100 km around Beijing within the preceding 24 h, while emissions as far as 500 km and within the preceding 3 days were found to affect secondary aerosols.

Mapping surface temperature in large lakes with MODIS data

Satellite sensor MODIS on two platforms can produce Sea Surface Temperature over certain regions about three to four times per day. Our objective was to test if the MODIS SST product can be applied for lakes whose surface areas are large enough to be observed at the MODIS spatial resolution and to compare the satellite-derived lake surface temperatures with in situ measurements. Surface temperatures for Lakes Vänern and Vättern in Sweden, two of the largest European lakes, are extracted from the MODIS/Terra images for period 2001–2003. The results are analyzed on different quality levels, as all MODIS L2 products are equipped with an additional quality flag. We present temperature development over 2001–2003, and show the capability of the MODIS SST product to couple the known thermodynamical features in the lakes under study, where temperature varies greatly with space and time. These results can complement lake monitoring programs anywhere.

Agricultural and forest productivity for modelling policy scenarios: Evaluating approaches for New Zealand greenhouse gas mitigation

The New Zealand Government is currently reviewing policies for the Kyoto Protocol's first commitment period, and entering international negotiations that will define the rules for the second commitment period. Policy makers and stakeholders require robust tools to evaluate national scale policy scenarios. Models for robust scenario analysis must combine the best available biophysical and economic information, and include understanding of uncertainties. I evaluate three different datasets as candidates for estimating the biological net primary production (NPP) of pastoral and forest land, as well as C sequestration potential under exotic or indigenous forest. For scenario analysis of land‐use change, estimating NPP is critical to represent the flow of agricultural and forest production into the economy, while estimating C sequestration potential in forests is required to estimate the quantity of C available for credits and liabilities. First, a 4‐year average of NASA's 1 km annual NPP product from the MODIS satellite sensor was verified using published New Zealand data, and used to test and calibrate the following indices. The MODIS NPP data were compared to average stocking indices for pasture and site indices for Pinus radiata forests derived from 10‐ to 3 0‐year‐old mapping in the Land Resources Inventory (LRI). Third, a “Storie Index” approach was developed using factors representing climate and soil properties that co‐limit potential productivity, and calibrated to MODIS NPP. Each approach represents the apparent variation in NPP at the national scale, but may risk serious errors at a more local scale. The calibrated Storie Index approach offers the best predictions for pastoral and tussock land (R2= 0.71) and indigenous forest (R2 = 0.38), while the LRI‐derived site index is most predictive for exotic forests (R2 = 0.16). While both the LRI‐derived indices and the “Storie Index” are recommended for modelling policy scenarios, the “Storie Index” approach shows the greatest versatility because it does not depend on current or past land use for estimation, provides the highest level of spatial detail, and can easily be improved.