Moderate Resolution Imaging Spectrometer Research Articles

Retrieving Soil Moisture in Hebei by Using Temperature Vegetation Dryness Index

The temperature-vegetation index space coupled with information of surface temperature and vegetation, is an important method to realize soil moisture estimation and agricultural drought monitoring. In order to estimate the soil moisture in the study area, we collected soil relative humidity of Agricultural meteorological station and downloaded Moderate Resolution Imaging Spectrometer (MODIS) image data. Then, the temperature vegetation dryness index was calculated based on the MODIS Normalized difference vegetation index (NDVI) and land surface temperature (LST). A correlation analysis of TVDI and soil relative humidity at depth of 10 cm was carried out and an empirical model of moisture estimation was established. Finally, another set of data was used to validate the accuracy of model. The results show that the TVDI method can be used to achieve the soil moisture in the study area. The empirical model has certain universality in the study area, and obtains a high accuracy of soil moisture estimation with an R2 of 0.374 and RMSE of 11.73%.

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

Haze Spectral Analysis and Detection Algorithm Using Satellite Remote Sensing Data

Frequent occurring of haze pollution events and high fine particulate matter (PM(2.5)) concentration in China have attracted more and more attention in the world. Satellite remote sensing can be used to characterize the air pollution. However, haze is usually misidentified as fog, thin cloud or bright surface in NASA’s Moderate Resolution Imaging Spectrometer (MODIS) cloud and clear days’ aerosol products, and the retrieval of its optical properties is not included in MODIS cloud detection and dark target algorithm. This approach first studies the spectral characters of cloud, fog, haze, and land cover pixels. Second, following the previous cloud detection and aerosol retrieval literatures, a threshold algorithm is developed to distinguish haze from other pixels based on MODIS multi-band apparent reflectance and brightness temperature. This algorithm is used to detect the haze distribution over North China Plain in 2008 spring and summer. Our result shows a good agreement with the true-color satellite images, which enhances MODIS’s ability to monitor the severe air pollution episodes. In addition, the high AOD data from Beijing and Xiang Aerosol Robotic NETwork (AERONET) sites indicate nearly 80% haze days are detected by our approach. Finally, we analyze the errors and uncertainties in haze detection algorithm, and put forward the potential improvements.

Inundation Process in the Lower Mekong River Basin

This study simulated the inundation process in the Lower Mekong River Basin (LMB). The LMB has suffered from severe floods, especially in 2000 and 2011. To quantify the inundation of water in a basin where large-scale inundation by river water occurs, understanding the conveyance of a river channel during a flood is particularly important. Therefore, we conducted a field survey using an acoustic Doppler current profiler (aDcp) to understand the longitudinal distribution of the width and depth of the river channel and the variation in hydraulic resistance with respect to shear stress on the riverbed. It was found that the width and depth vary longitudinally, and the relationship between them can be estimated by an equation derived from governing equations of water and sediment and the bed load formula. Furthermore, it was revealed that hydraulic resistance decreases with increasing non-dimensional shear stress. Then, the characteristics of the river channel were incorporated into the runoff-inundation simulation. Furthermore, inundation water should be validated not only in terms of inundation extent but also with respect to water depth and velocity. These were estimated using 8-day composite surface reflectance data from the Moderate Resolution Imaging Spectrometer (MODIS) and the SRTM. Simulation results indicated that water level and discharge within the river channel were able to reproduce observed values. Additionally, simulated inundation extent, water velocity, and water depth over the floodplain showed reasonable agreement with the results using the data from the MODIS and the SRTM. Although there are some elements that should be improved, the inundation process in the LMB was simulated appropriately despite its complexity. The method described in this study to set a calculation condition and to validate variables over a floodplain should be useful for runoff-inundation simulation in various large-scale basins.

Urban–Rural Contrasts in Central-Eastern European Cities Using a MODIS 4 Micron Time Series

A primary impact of urbanization on the local climate is evident in the phenomenon recognized as the Urban Heat Island (UHI) effect. This urban thermal anomaly can increase the health risks of vulnerable populations to heat waves. The surface UHI results from emittance in the longer wavelengths of the thermal infrared; however, there are also urban anomalies that are detectable from radiance in the shorter wavelengths (3–5 micron) of the Middle Infrared (MIR). Radiance in the MIR can penetrate urban haze which frequently obscures urban areas by scattering visible and near infrared radiation. We analyzed seasonal and spatial variations in MIR for three Central European cities from 2003 through 2012 using Moderate Resolution Imaging Spectrometer (MODIS) band 23 (~4 micron) to evaluate whether MIR radiance could be used to characterize heat anomalies associated with urban areas. We examined the seasonality of MIR radiance over urban areas and nearby croplands and found that the urban MIR anomalies varied due to time of year: cropland MIR could be larger than urban MIR when there was more exposed soil at planting and harvest times. Further, we compared monthly mean MIR with the Normalized Difference Vegetation Index (NDVI) to analyze contrasts between urban and rural areas. We found that the seasonal dynamic range of the MIR could exceed that of the NDVI. We explored the linkage between meteorological data and MIR radiance and found a range of responses from strong to weak dependence of MIR radiance on maximum temperature and accumulated precipitation. Our results extend the understanding of the anomalous characteristics of urban areas within a rural matrix.

A Consistent AVHRR Visible Calibration Record Based on Multiple Methods Applicable for the NOAA Degrading Orbits. Part II: Validation

AbstractConsistent cross-sensor Advanced Very High Resolution Radiometer (AVHRR) calibration coefficients are determined using desert, polar ice, and deep convective cloud (DCC) invariant Earth targets. The greatest AVHRR calibration challenge is the slow orbit degradation of the host satellite, which precesses toward a terminator orbit. This issue is solved by characterizing the invariant targets with NOAA-16 AVHRR observed radiances that have been referenced to the Aqua Moderate Resolution Imaging Spectrometer (MODIS) calibration using simultaneous nadir overpass (SNO) observations. Another benefit of the NOAA-16 invariant target–modeled reflectance method is that, because of the similarities among the AVHRR spectral response functions, a smaller spectral band adjustment factor is required than when establishing calibrations relative to a non-AVHRR reference instrument. The sensor- and band-specific calibration uncertainties, with respect to the calibration reference, are, on average, 2% and 3% for channels 1 and 2, respectively. The uncertainties are smaller for sensors that are in afternoon orbits, have longer records, and spend less time in terminator conditions.The multiple invariant targets referenced to Aqua MODIS (MITRAM) AVHRR calibration coefficients are evaluated for individual target consistency, compared against Aqua MODIS/AVHRR SNOs, and selected published calibration gains. The MITRAM and SNO relative calibration biases mostly agree to within 1% for channels 1 and 2, respectively. The individual invariant target and MITRAM sensor relative calibration biases are mostly consistent to within 1% and 2% for channels 1 and 2, respectively. The differences between the MITRAM and other published calibrations are mostly attributed to the reference instrument calibration differences.

Spatial heterogeneity of greening and browning between and within bioclimatic zones in northern West Siberia

Studies of the normalized difference vegetation index (NDVI) have found broad changes in vegetation productivity in high northern latitudes in the past decades, including increases in NDVI (‘greening’) in tundra regions and decreases (‘browning’) in forest regions. The causes of these changes are not well understood but have been attributed to a variety of factors. We use Moderate Resolution Imaging Spectrometer (MODIS) satellite data for 2000–2014 and focus on northern West Siberia—a hot spot of extensive landcover change due to rapid resource development, geomorphic change, climate change and reindeer grazing. The region is relatively little-studied in terms of vegetation productivity patterns and trends. This study examines changes between and within bioclimatic sub-zones and reveals differences between forest and treeless areas and differences in productivity even down to the tree species level. Our results show that only 18% of the total northern West Siberia area had statistically significant changes in productivity, with 8.4% increasing (greening) and 9.6% decreasing (browning). We find spatial heterogeneity in the trends, and contrasting trends both between and within bioclimatic zones. A key finding is the identification of contrasting trends for different species within the same bioclimatic zone. Browning is most prominent in areas of denser tree coverage, and particularly in evergreen coniferous forest with dark (Picea abie, Picea obovata) or light (Pinus sylvestris) evergreen and evergreen-majority mixed forests. In contrast, low density deciduous needle-leaf forest dominated by larch (Larix sibirica), shows a significant increase in productivity, even while neighboring different species show productivity decrease. These results underscore the complexity of the patterns of variability and trends in vegetation productivity, and suggest the need for spatially and thematically detailed studies to better understand the response of different northern forest types and species to climate and environmental change.

Quantifying the human influence on fire ignition across the western USA.

Humans have a profound effect on fire regimes by increasing the frequency of ignitions. Although ignition is an integral component of understanding and predicting fire, to date fire models have not been able to isolate the ignition location, leading to inconsistent use of anthropogenic ignition proxies. Here, we identified fire ignitions from the Moderate Resolution Imaging Spectrometer (MODIS) Burned Area Product (2000-2012) to create the first remotely sensed, consistently derived, and regionally comprehensive fire ignition data set for the western United States. We quantified the spatial relationships between several anthropogenic land-use/disturbance features and ignition for ecoregions within the study area and used hierarchical partitioning to test how the anthropogenic predictors of fire ignition vary among ecoregions. The degree to which anthropogenic features predicted ignition varied considerably by ecoregion, with the strongest relationships found in the Marine West Coast Forest and North American Desert ecoregions. Similarly, the contribution of individual anthropogenic predictors varied greatly among ecoregions. Railroad corridors and agricultural presence tended to be the most important predictors of anthropogenic ignition, while population density and roads were generally poor predictors. Although human population has often been used as a proxy for ignitions at global scales, it is less important at regional scales when more specific land uses (e.g., agriculture) can be identified. The variability of ignition predictors among ecoregions suggests that human activities have heterogeneous impacts in altering fire regimes within different vegetation types and geographies.

Study on Regional Variations of Aerosol Loading Using Long Term Satellite Data Over Indian Region

Satellite-based measurements of aerosols are one of the most effective ways to understand the role of aerosols in climate in terms of spatial and temporal variability. In the present study, we attempted to analyse spatial and temporal variations of satellite derived aerosol optical depth (AOD) over Indian region using moderate resolution imaging spectrometer over a period of 2001–2011. Due to its vast spatial extent, Indian region and adjacent oceanic regions are divided into different zones for analysis. The land mass is sub divided into five different zones such as Indo Gangetic Plain (IGP), Indian mainland, North Eastern India (NE), South India-1 (SI-1), South India-2 (SI-2). Oceanic areas are divided into Arabian Sea and Bay of Bengal. Arabian Sea is further divided as three zones viz. Northern AS (NAS), Central AS (CAS) and Eastern AS (EAS) zones. Bay of Bengal is divided as North BoB (NBoB), West BoB (WBoB), Central BoB (CBoB), and East BoB (EBoB). The study revealed that among all the land regions, IGP showed the highest peak AOD value (0.52 ± 0.17) while SI-2 showed the lower values of AOD in all the months compared to all India average. The maximum AOD is observed during premonsoon season for all regions. During the winter, average AOD levels were substantially lower than the summer averages. Peak of aerosol loading (0.35 ± 0.159) is observed in March over NE region, whereas in all other regions, peak is observed during May. Frequency distribution of long term AOD ( 0.5) shows a shift of frequency distribution of AOD from 0.5. Oceanic areas also shows seasonal variation of AOD. Over Arabian Sea, high AOD values with greater variations were observed in summer monsoon season while in Bay of Bengal it is observed during winter monsoon. This is due to the high wind speed prevailing in Arabian Sea during monsoon season which results in production of more sea salt aerosol. Highest AOD values are observed over NAS during monsoon season and over NBOB during winter season. Lowest AOD values with its lower variations observed in both the central region of Arabian Sea and Bay of Bengal.

Influences of cloud heterogeneity on cirrus optical properties retrieved from the visible and near-infrared channels of MODIS/SEVIRI for flat and optically thick cirrus clouds

11Nonstandard abbreviations: Cloud Optical Thickness (COT), Effective Diameter (De), Independent Pixel Approximation (IPA), Ice Water Content (IWC), Near-infrared (NIR), Plane Parallel Approximation (PPA), Severely Roughened Aggregated Columns (SRAC), Thermal infrared (TIR), Three-dimensional (3-D) ,Visible (VIS). The influences of three-dimensional radiative effects and horizontal heterogeneity effects on the retrieval of cloud optical thickness (COT) and effective diameter (De) for cirrus clouds are explored by the SHDOM radiative transfer model. The stochastic cirrus clouds are generated by the Cloudgen model based on the Atmospheric Radiation Measurement program data. Incorporating a new ice cloud spectral model, we evaluate the retrieval errors for two solar zenith angles (SZAs) (30° and 60°), four solar azimuth angles (0°, 45°, 90°, and 180°), and two sensor settings (Moderate Resolution Imaging Spectrometer (MODIS) onboard Aqua and Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard METEOSAT-8). The domain-averaged relative error of COT (μ) ranges from −24.1 % to -1.0 % (SZA = 30°) and from −11.6 % to 3.3 % (SZA = 60°), with the uncertainty within 7.5 % to –12.5 % (SZA = 30°) and 20.0 % - 27.5 % (SZA = 60°). For the SZA of 60° only, the relative error and uncertainty are parameterized by the retrieved COT by linear functions, providing bases to correct the retrieved COT and estimate their uncertainties. Besides, De is overestimated by 0.7–15.0μm on the domain average, with the corresponding uncertainty within 6.7–26.5μm. The retrieval errors show no discernible dependence on solar azimuth angle due to the flat tops and full coverage of the cirrus samples. The results are valid only for the two samples and for the specific spatial resolution of the radiative transfer simulations.

Estimation of melt pond fraction over high‐concentration Arctic sea ice using AMSR‐E passive microwave data

AbstractMelt pond fraction (MPF) on sea ice is an important factor for ice‐albedo feedback throughout the Arctic Ocean. We propose an algorithm to estimate MPF using satellite passive microwave data in this study. The brightness temperature (TB) data obtained from the Advanced Microwave Scanning Radiometer‐Earth observing system (AMSR‐E) were compared to the ship‐based MPF in the Beaufort Sea and Canadian Arctic Archipelago. The difference between the TB at horizontal and vertical polarizations of 6.9 and 89.0 GHz (MP06H–89V), respectively, depends on the MPF. The correlation between MP06H–89V and ship‐based MPF was higher than that between ship‐based MPF and two individual channels (6.9 and 89.0 GHz of horizontal and vertical polarizations, respectively). The MPF determined with the highest resolution channel, 89.0 GHz (5 km × 5 km), provides spatial information with more detail than the 6.9 GHz channel. The algorithm estimates the relative fraction of ice covered by water (1) over areas where sea ice concentration is higher than 95%, (2) during late summer, and (3) in areas with low atmospheric humidity. The MPF estimated from AMSR‐E data (AMSR‐E MPF) in early summer was underestimated at lower latitudes and overestimated at higher latitudes, compared to the MPF obtained from the Moderate Resolution Image Spectrometer (MODIS MPF). The differences between AMSR‐E MPF and MODIS MPF were less than 5% in most the regions and the periods. Our results suggest that the proposal algorithm serves as a basis for building time series of MPF in regions of consolidated ice pack.

The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean

Abstract. Retrievals of aerosol optical depth (AOD) at 388 nm over the ocean from the Ozone Monitoring Instrument (OMI) two-channel near-UV algorithm (OMAERUV) have been compared with independent AOD measurements. The analysis was carried out over the open ocean (OMI and MODerate-resolution Imaging Spectrometer (MODIS) AOD comparisons) and over coastal and island sites (OMI and AERONET, the AErosol RObotic NETwork). Additionally, a research version of the retrieval algorithm (using MODIS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) information as constraints) was utilized to evaluate the sensitivity of the retrieval to different assumed aerosol properties. Overall, the comparison resulted in differences (OMI minus independent measurements) within the expected levels of uncertainty for the OMI AOD retrievals (0.1 for AOD < 0.3, 30 % for AOD > 0.3). Using examples from case studies with outliers, the reasons that led to the observed differences were examined with specific purpose to determine whether they are related to instrument limitations (i.e., pixel size, calibration) or algorithm assumptions (such as aerosol shape, aerosol height). The analysis confirms that OMAERUV does an adequate job at rejecting cloudy scenes within the instrument's capabilities. There is a residual cloud contamination in OMI pixels with quality flag 0 (the best conditions for aerosol retrieval according to the algorithm), resulting in a bias towards high AODs in OMAERUV. This bias is more pronounced at low concentrations of absorbing aerosols (AOD 388 nm ∼ < 0.5). For higher aerosol loadings, the bias remains within OMI's AOD uncertainties. In pixels where OMAERUV assigned a dust aerosol model, a fraction of them (< 20 %) had retrieved AODs significantly lower than AERONET and MODIS AODs. In a case study, a detailed examination of the aerosol height from CALIOP and the AODs from MODIS, along with sensitivity tests, was carried out by varying the different assumed parameters in the retrieval (imaginary index of refraction, size distribution, aerosol height, particle shape). It was found that the spherical shape assumption for dust in the current retrieval is the main cause of the underestimate. In addition, it is demonstrated in an example how an incorrect assumption of the aerosol height can lead to an underestimate. Nevertheless, this is not as significant as the effect of particle shape. These findings will be incorporated in a future version of the retrieval algorithm.

Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations

The objective of this study was to assess the performance of the simulated start of the photosynthetically active season by a large-scale biosphere model in boreal forests in Finland with remote sensing observations. The start of season for two forest types, evergreen needle- and deciduous broad-leaf, was obtained for the period 2003–2011 from regional JSBACH (Jena Scheme for Biosphere–Atmosphere Hamburg) runs, driven with climate variables from a regional climate model. The satellite-derived start of season was determined from daily Moderate Resolution Imaging Spectrometer (MODIS) time series of Fractional Snow Cover and the Normalized Difference Water Index by applying methods that were targeted to the two forest types. The accuracy of the satellite-derived start of season in deciduous forest was assessed with bud break observations of birch and a root mean square error of seven days was obtained. The evaluation of JSBACH modelled start of season dates with satellite observations revealed high spatial correspondence. The bias was less than five days for both forest types but showed regional differences that need further consideration. The agreement with satellite observations was slightly better for the evergreen than for the deciduous forest. Nonetheless, comparison with gross primary production (GPP) determined from CO2 flux measurements at two eddy covariance sites in evergreen forest revealed that the JSBACH-simulated GPP was higher in early spring and led to too-early simulated start of season dates. Photosynthetic activity recovers differently in evergreen and deciduous forests. While for the deciduous forest calibration of phenology alone could improve the performance of JSBACH, for the evergreen forest, changes such as seasonality of temperature response, would need to be introduced to the photosynthetic capacity to improve the temporal development of gross primary production.

RAPID EXPOSURE ASSESSMENT OF NATIONWIDE RIVER FLOOD FOR DISASTER RISK REDUCTION

Abstract. considerably increased. For flood disaster risk reduction, it is important to identify and characterize flood area, locations (particularly lowland along rivers), and durations. For this purpose, flood mapping and monitoring are an imperative process and the fundamental part of risk management as well as emergency response. Our ultimate goal is to detect flood inundation areas over a nationwide scale despite limitations of optical and multispectral images, and to estimate flood risk in terms of affected people. We propose a methodological possibility to be used as a standard approach for nationwide rapid flood exposure assessment with the use of the multi-temporal Moderate Resolution Imaging Spectrometer (MODIS), a big contributor to progress in near-real-time flood mapping. The preliminary results in Bangladesh show that a propensity of flood risk change strongly depends on the temporal and spatial dynamics of exposure such as distributed population.

RAPID EXPOSURE ASSESSMENT OF NATIONWIDE RIVER FLOOD FOR DISASTER RISK REDUCTION

considerably increased. For flood disaster risk reduction, it is important to identify and characterize flood area, locations (particularly lowland along rivers), and durations. For this purpose, flood mapping and monitoring are an imperative process and the fundamental part of risk management as well as emergency response. Our ultimate goal is to detect flood inundation areas over a nationwide scale despite limitations of optical and multispectral images, and to estimate flood risk in terms of affected people. We propose a methodological possibility to be used as a standard approach for nationwide rapid flood exposure assessment with the use of the multi-temporal Moderate Resolution Imaging Spectrometer (MODIS), a big contributor to progress in near-real-time flood mapping. The preliminary results in Bangladesh show that a propensity of flood risk change strongly depends on the temporal and spatial dynamics of exposure such as distributed population.

Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology

There is great potential value in linking geographically dispersed multitemporal observations collected by lay volunteers (or “citizen scientists”) with remotely-sensed observations of plant phenology, which are recognized as useful indicators of climate change. However, challenges include a large mismatch in spatial scale and diverse sources of uncertainty in the two measurement types. These challenges must be overcome if the data from each source are to be compared and jointly used to understand spatial and temporal variation in phenology, or if remote observations are to be used to predict ground-based observations. We investigated the correlation between land surface phenology derived from Moderate Resolution Imaging Spectrometer (MODIS) data and citizen scientists’ phenology observations from the USA National Phenology Network (NPN). The volunteer observations spanned 2004 to 2013 and represented 25 plant species and nine phenophases. We developed quality control procedures that removed observations outside of an a priori determined acceptable period and observations that were made more than 10 days after a preceding observation. We found that these two quality control steps improved the correlation between ground- and remote-observations, but the largest improvement was achieved when the analysis was restricted to forested MODIS pixels. These results demonstrate a high degree of correlation between the phenology of individual trees (particularly dominant forest trees such as quaking aspen, white oak, and American beech) and the phenology of the surrounding forested landscape. These results provide helpful guidelines for the joint use of citizen scientists’ observations and remote sensing phenology in work aimed at understanding continental scale variation and temporal trends.

Magnitude, spatial distribution and uncertainty of forest biomass stocks in Mexico

Abstract Existing forest biomass stock maps show discrepancies with in-situ observations in Mexico. Ground data from the National Forest and Soil Inventory of Mexico (INFyS) were used to calibrate a maximum entropy (MaxEnt) algorithm to generate forest biomass ( AGB ), its associated uncertainty, and forest probability maps. The input predictor layers for the MaxEnt algorithm were extracted from the moderate resolution imaging spectrometer (MODIS) vegetation index (VI) products, ALOS PALSAR L-band dual-polarization backscatter coefficient images, and the Shuttle Radar Topography Mission (SRTM) digital elevation model. A Jackknife analysis of the model accuracy indicated that the ALOS PALSAR layers have the highest relative contribution (50.9%) to the estimation of AGB , followed by MODIS-VI (32.9%) and SRTM (16.2%). The forest cover mask derived from the forest probability map showed higher accuracy ( κ = 0.83) than alternative masks derived from ALOS PALSAR ( κ = 0.72–0.78) or MODIS vegetation continuous fields (VCF) with a 10% tree cover threshold ( κ = 0.66). The use of different forest cover masks yielded differences of about 30 million ha in forest cover extent and 0.45 Gt C in total carbon stocks. The AGB map showed a root mean square error (RMSE) of 17.3 t C ha − 1 and R 2 = 0.31 when validated at the 250 m pixel scale with inventory plots. The error and accuracy at municipality and state levels were RMSE = ± 4.4 t C ha − 1 , R 2 = 0.75 and RMSE = ± 2.1 t C ha − 1 , R 2 = 0.94 respectively. We estimate the total carbon stored in the aboveground live biomass of forests of Mexico to be 1.69 Gt C ± 1% (mean carbon density of 21.8 t C ha − 1 ), which agrees with the total carbon estimated by FAO for the FRA 2010 (1.68 Gt C). The new map, derived directly from the biomass estimates of the national inventory, proved to have similar accuracy as existing forest biomass maps of Mexico, but is more representative of the shape of the probability distribution function of AGB in the national forest inventory data. Our results suggest that the use of a non-parametric maximum entropy model trained with forest inventory plots, even at the sub-pixel size, can provide accurate spatial maps for national or regional REDD + applications and MRV systems.

Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America

Accurate estimation of the gross primary production (GPP) of terrestrial ecosystems is vital for a better understanding of the spatial-temporal patterns of the global carbon cycle. In this study,we estimate GPP in North America (NA) using the satellite-based Vegetation Photosynthesis Model (VPM), MODIS (Moderate Resolution Imaging Spectrometer) images at 8-day temporal and 500 meter spatial resolutions, and NCEP-NARR (National Center for Environmental Prediction-North America Regional Reanalysis) climate data. The simulated GPP (GPP (sub VPM)) agrees well with the flux tower derived GPP (GPPEC) at 39 AmeriFlux sites (155 site-years). The GPP (sub VPM) in 2010 is spatially aggregated to 0.5 by 0.5-degree grid cells and then compared with sun-induced chlorophyll fluorescence (SIF) data from Global Ozone Monitoring Instrument 2 (GOME-2), which is directly related to vegetation photosynthesis. Spatial distribution and seasonal dynamics of GPP (sub VPM) and GOME-2 SIF show good consistency. At the biome scale, GPP (sub VPM) and SIF shows strong linear relationships (R (sup 2) is greater than 0.95) and small variations in regression slopes ((4.60-5.55 grams Carbon per square meter per day) divided by (milliwatts per square meter per nanometer per square radian)). The total annual GPP (sub VPM) in NA in 2010 is approximately 13.53 petagrams Carbon per year, which accounts for approximately 11.0 percent of the global terrestrial GPP and is within the range of annual GPP estimates from six other process-based and data-driven models (11.35-22.23 petagrams Carbon per year). Among the seven models, some models did not capture the spatial pattern of GOME-2 SIF data at annual scale, especially in Midwest cropland region. The results from this study demonstrate the reliable performance of VPM at the continental scale, and the potential of SIF data being used as a benchmark to compare with GPP models.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Temporal Variability and Characterization of Aerosols across the Pakistan Region during the Winter Fog Periods

Fog is a meteorological/environmental phenomenon which happens across the Indo-Gangetic Plains (IGP) and leads to significant social and economic problems, especially posing significant threats to public health and causing disruptions in air and road traffic. Meteorological stations in Pakistan provide limited information regarding fog episodes as these provide only point observations. Continuous monitoring, as well as a spatially coherent picture of fog distribution, is possible through the use of satellite observations. This study focuses on the 2012–2015 winter fog episodes over the Pakistan region using the Moderate Resolution Image Spectrometer (MODIS), the Ozone Monitoring Instrument and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) products. The main objective of the study was to map the spatial distribution of aerosols, their types, and to identify the aerosol origins during special weather conditions like fog in Pakistan. The study also included ground monitoring of particulate matter (PM) concentrations, which were conducted during the 2014–2015 winter period only. Overall, this study is part of a multi-country project supported by the International Centre for Integrated Mountain Development (ICIMOD), started in 2014–2015 winter period, whereby scientists from Bangladesh, India and Nepal have also conducted measurements at their respective sites. A significant correlation between MODIS (AOD) and AERONET Station (AOD) data from Lahore was identified. Mass concentration of PM10 at all sampling sites within Lahore city exceeded the National Environmental Quality Standards (NEQS) levels on most of the occasions. Smoke and absorbing aerosol were found to be major constituents of winter fog in Pakistan. Furthermore, an extended span of winter fog was also observed in Lahore city during the winter of 2014–2015. The Vertical Feature Mask (VFM) provided by CALIPSO satellite confirmed the low-lying aerosol layers, instead of clouds for the vertical profiles of selected case studies.