Moderate Imaging Spectroradiometer Research Articles

Variability in MODIS NDVI in Relation to Southwest Monsoon over Western Ghats, India

Eleven years (2000 to 2010) of Normalized Difference Vegetation Index (NDVI) data, derived from Moderate Imaging Spectroradiometer (MODIS) Terra with 250 m resolution is used in the present study to discuss the changes in the vegetal cover over Western Ghats, India. The NDVI over Western Ghats (number of test sites are 16) showed increasing tendency and the pronounced changes in NDVI have been studied in the context of southwest monsoon's distribution and activity. The NDVI progression is observed from June with a minimum value of 0.179 and yielded to a maximum at 0.565 during September/October, on an average. The study then relates NDVI with different rainy events to understand the connection between the ground vegetation and the south west monsoon. The results of the study inferred are as follows: i) NDVI, Antecedent Precipitation Index (API) are in good agreement throughout the monsoon which is evidenced by correlation; ii) NDVI maintained good correlation with a number of Light and Moderate Rainy days alternatively but not with the number of Heavy Rainy days; iii) Relation of NDVI with Isolated, Scattered distributions and active monsoons is substantial; and iv) Rate of Green Up is captured by the satellite during the crop growing season over the Western Ghats.

Observations of a substantial cloud‐aerosol indirect effect during the 2014–2015 Bárðarbunga‐Veiðivötn fissure eruption in Iceland

AbstractThe Bárðarbunga‐Veiðivötn fissure eruption lasted from 31 August 2014 to 28 February 2015, during which its sulfur emissions dwarfed anthropogenic emissions from Europe. This natural experiment offers an excellent opportunity to investigate the aerosol indirect effect and the effect of effusive volcanic eruptions on climate. During the eruption cloud droplet effective radius (re) over the region surrounding Iceland was at the lowest value in the 14 year Moderate Imaging Spectroradiometer data record during September and October 2014. The change in reflected solar radiation due to increased cloud reflectivity during September and October is estimated to exceed 2 W m−2 over the region surrounding Iceland, with increases of 1 W m−2 extending as far south as the Açores. The strength of the aerosol indirect effect diagnosed here reaffirms the ability of volcanic aerosols to affect cloud properties and ultimately the planetary albedo.

Evaluation of a QAA-based algorithm using MODIS land bands data for retrieval of IOPs in the Eastern China Seas.

A quasi-analytical algorithm (QAA)-based algorithm which is applied to moderate imaging spectroradiometer (MODIS) land band data (469, 555, and 645 nm) is proposed and named QAA-RGR (Red-Green-bands-Ratio). The performance has been evaluated using in situ measurements data and MODIS data from the Eastern China Seas (ECS). The QAA-RGR algorithm uses the ratio of the remote sensing reflectance at 645nm (Rrs645) to the Rrs555 to estimate the absorption coefficient at 555nm. In addition, the spectral slope of the backscattering coefficient (bb) is estimated using a statistical relationship based on bb555. The other steps of the retrieval algorithm are the same as those of the extensively used QAA version 5 (QAAv5). First, the QAA-RGR algorithm was applied to an in situ measurement data set for the ECS to retrieve inherent optical properties (IOPs), and the results were compared with the QAAv5. The results demonstrate that, the two algorithms exhibit similar performance for in situ measurements. Second, the algorithm was applied to the land bands data and ocean bands data of MODIS over the ECS to obtain the distribution of IOPs at 500m and 1000m resolutions. The results of the retrieval algorithm were evaluated against the corresponding in situ measurements and compared to those from QAAv5. The results demonstrate that, the QAA-RGR algorithm is characterized by a better performance than QAAv5 for MODIS data. For QAAv5, the averaged mean absolute percentage error (MAPE) values of retrieval results of absorption coefficients and backscattering coefficients compared with in situ match-up measurements are 25.2% and 22.2%, respectively. For QAA-RGR, the averaged MAPE values are 15.9% and 18.3%, respectively. The QAAv5 retrieval results are often significantly underestimated especially for turbid coastal waters because of the easy saturation at 667nm band in addition to a large uncertainty in the estimation of Rrs of the blue bands. The QAA-RGR algorithm may be used to retrieve IOPs from MODIS measurements over the ECS for the measurement periods used in the study.

A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity

High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies that require dense time series of high-resolution images, e.g., crop monitoring. There are several fusion methods in the bibliography, but they are time-consuming and complicated to implement. Moreover, the local evaluation of the fused images is rarely analyzed. In this paper, we present a simple and fast fusion method based on a weighted average of two input images (H and L), which are weighted by their temporal validity to the image to be fused. The method was applied to two years (2009–2010) of Landsat and MODIS (MODerate Imaging Spectroradiometer) images that were acquired over a cropped area in Brazil. The fusion method was evaluated at global and local scales. The results show that the fused images reproduced reliable crop temporal profiles and correctly delineated the boundaries between two neighboring fields. The greatest advantages of the proposed method are the execution time and ease of use, which allow us to obtain a fused image in less than five minutes.

Object-based delineation of homogeneous landscape units at regional scale based on MODIS time series

Landscapes can be described by seasonal and spatial patterns linked to vegetation type and phenology, environmental conditions, and human activities. The objective of this work is to propose and test an approach for delineating homogeneous landscape units at a regional scale by using only Earth observation data. We used MODIS (Moderate Imaging Spectroradiometer) images from 2007 to 2011, acquired over the whole continental French territory at 250m spatial resolution. The data set includes time series of the Enhanced Vegetation Index (EVI) and time series of five Haralick texture indices. A principal components analysis (PCA) allowed us to choose the most representative indices (spectral and textural) and dates to be used in the region-growing segmentation. Different combinations of input data, as well as different segmentation parameters, were tested and compared using unsupervised evaluation methods. These methods were used to analyze the radiometric homogeneity of the regions and the radiometric disparity between regions when changing the homogeneity criterion of the segmentation. The best segmentation results obtained included three EVI images, together with three images of the texture 2nd moment, corresponding to the average of the months of April, July and December from 2007 to 2011. The optimum homogeneity criterion for the region-growing segmentation using this combination of variables was 15. We believe this method is applicable at other scales and other data sets for vegetation and biodiversity studies, and for habitat mapping.

Albedo decline on Greenland's Mittivakkat Gletscher in a warming climate

Albedo is one of the parameters that govern energy availability for snow and ice surface ablation, and subsequently the surface mass balance conditions of temperate glaciers and ice caps (GIC). Here, we document snow and ice albedo changes for Mittivakkat Gletscher (MG) in Southeast Greenland (2000-2013), for which an 18-year record of direct surface mass balance measurements exists. The MODerate Imaging Spectroradiometer (MODIS MCD43A3) albedo product was used to study MG's snow and ice albedo, evaluated against supraglacial automatic weather station (AWS) observations. In general, by the end of the mass balance year (EBY), MG's AWS observed bare ice albedo reached ~0.3 only just exceeding values observed for proglacial bedrock (~0.2). The analysis reveals negative mean trends in the MODIS-derived MG EBY albedo for the period 2000-2013 with a significant decline in mean glacier-wide albedo of 0.10. The greatest decline in albedo, of 0.25, occurred near the equilibrium line altitude (ELA), an important surface cover and albedo transitional zone. The EBY albedo correlates significantly with ELA and net winter and summer glacier mass balance records. © 2014 Royal Meteorological Society.

인공위성 가뭄지수를 활용한 한반도 가뭄 평가; 2013년 남부지방 가뭄을 대상으로

본 연구의 목적은 인공위성 자료를 활용한 가뭄지수를 산정하고 이를 통해 2013년 발생한 남부지방의 가뭄을 평가하는데 있다. 다중분광센서인 MODerate resolution Imaging Spectroradiometer(MODIS) 센서의 다양한 산출물을 통해 수문기상인자 기반의 가뭄지수(Evaporative Stress Index, ESI)를 산정하고 이를 유관기관의 가뭄 정보 시스템의 가뭄 모니터링 결과와 비교 분석하였다. 또한 국내에서 널리 이용되고 있는 PDSI(Palmer Drought Severity Index), SPI(Standardized Precipitation Index)와 ESI의 시계열 분석을 통해 ESI의 국내 적용성을 판단하고자 하였다. 각 가뭄지수들의 시공간 비교를 통해 ESI의 국내 적용성이 높음을 확인할 수 있다. The objective of this study is to derive and evaluate the drought index by using satellite data. The satellite-based drought index was spatially assessed at Southern part of Korean peninsula in 2013. We estimated hydrometeorological parameter-based drought index (Evaporative Stress Index, ESI) by using various products of multispectral sensor, MODerate resoultion Imaging Spectroradiometer (MODIS). The ESI analyzed other spatial distribution of drought indices provided by drought information system from related organizations. Regional capability of ESI through time series analysis compared with PDSI(Palmer Drought Severity Index) and SPI(Standardized Precipitation Index) was assessed. Based on the results in this study, it can be concluded that the derived ESI showed better regional capability than the other drought indices.

Aerosol Optical Properties over South Asia from Ground-Based Observations and Remote Sensing: A Review

Seasonal and inter-annual variabilities in aerosol optical depth (AOD) andaerosol size distribution are investigated using ground-based measurements (sun photometersand sun/sky radiometers), and MODIS (MODerate Imaging Spectroradiometer) and MISR(Multiangle Imaging SpectroRadiometer) satellites over Ahmedabad, Gurushikhar, Karachi,Kanpur and Gandhi College in South Asia during 2006–2008. An analysis and a review onthe comparison between aerosol optical depths measured from ground-based observationsand remote sensing over South Asia is performed. Karachi and Ahmedabad AODs aretwo times higher than Gurushikhar, a high altitude remote site. AODs over Kanpur andGandhi College in the Indo-Gangetic basin are higher than those measured over Ahmedabad,Gurushikhar and Karachi. Summer monsoon AODs are higher over Ahmedabad andKarachi, while winter AODs are higher over Kanpur and Gandhi College. AOD ratio,ratio of AODs obtained at 0.38 μm and 0.87 μm, is higher during postmonsoon andwinter than premonsoon and monsoon ratio suggesting the abundance of fine mode aerosolsduring postmonsoon and winter. Monsoon AOD ratios are lowest owing to the dominanceof coarse mode (mainly sea salt) particles. Ångström wavelength exponent (α) duringpostmonsoon and winter are higher than that of premonsoon and monsoon values. Lower α values over Gurushikhar and Karachi indicate the dominance of coarse mode aerosols(dust in Gurushikhar, and dust and sea salt in Karachi). Dominance of fine mode aerosolsdue to anthropogenic activities give rise to higher α values over urban locations (e.g.,Ahmedabad and Kanpur). Comparison between ground-based and MODIS (Terra andAqua) retrieved AODs show that aerosol optical depths do not change significantly inan hour and much of the diurnal AOD variation is captured well by the two MODISinstruments. The temporal difference (about an hour) between the ground-based and remotesensing measurements contributes negligibly to the observed differences in AODs. Thedifferences between ground-based and remote sensing (MODIS and MISR) AODs vary onspatial scales. During the study period (2006–2008) MODIS underestimates AODs overwestern India by about 0.04, while over the Indo-Gangetic Plain MODIS overestimatesAOD by 0.06 in (Kanpur) and underestimates by 0.07 (Gandhi College) with respect toAODs measured by hand held sun photometer and Microtops sun photometer (Ahmedabadand Gurushikhar), and AERONET sun/sky radiometers (Karachi, Kanpur and GandhiCollege) respectively. During the same period MISR underestimates AODs in the rangeof 0.02–0.17 over Ahmedabad, Gurushikhar, Karachi and Kanpur, while in Gandhi CollegeMISR overestimates AOD by 0.2 when compared to ground-based AODs. Results on spatial,seasonal and inter-annual variations in aerosol characteristics will be useful in improving theaerosol retrieval algorithms in remote sensing, and in regional and global estimates of aerosolradiative forcing.

An Evaluation of MODIS Daily and 8-day Composite Products for Floodplain and Wetland Inundation Mapping

Wetland and floodplain inundation is well-known for its hydrological, ecological and environmental importance. Satellite remote sensing provides an effective and efficient tool for detecting inundation extent. This study compares and validates inundation maps derived from NASA’s Moderate Imaging Spectroradiometer (MODIS) imagery. The comparison was performed between MODIS daily and 8-day composite products; and the validation was conducted using Landsat Thematic Mapper (TM) images. Two floodplain wetlands in the Murray-Darling Basin in Australia were selected as case studies, and inundation extents corresponding to the peak flows of significant flood events were extracted using the Open Water Likelihood (OWL) algorithm and the modified Normalised Difference Water Index (mNDWI) for MODIS and TM images, respectively. The accuracy of the inundation maps derived from different images were assessed spatially and statistically. The evaluation results show that both MODIS products may provide a reasonable estimate of the dynamic extent of floodplain inundation at the regional scale. The accuracy of inundation mapping is mainly due to the spatial and spectral characteristics of MODIS imagery and has nothing to do with the type of products chosen, thus the 8-day composite images can be used as a surrogate for daily images for the purpose of inundation delineation.

Linking glacier annual mass balance and glacier albedo retrieved from MODIS data

Abstract. Albedo is one of the variables controlling the mass balance of temperate glaciers. Multispectral imagers, such as MODerate Imaging Spectroradiometer (MODIS) on board the TERRA and AQUA satellites, provide a means to monitor glacier surface albedo. In this study, different methods to retrieve broadband glacier surface albedo from MODIS data are compared. The effect of multiple reflections due to the rugged topography and of the anisotropic reflection of snow and ice are particularly investigated. The methods are tested on the Saint Sorlin Glacier (Grandes Rousses area, French Alps). The accuracy of the retrieved albedo is estimated using both field measurements, at two automatic weather stations located on the glacier, and albedo values derived from terrestrial photographs. For summers 2008 and 2009, the root mean square deviation (RMSD) between field measurements and the broadband albedo retrieved from MODIS data at 250 m spatial resolution was found to be 0.052 or about 10% relative error. The RMSD estimated for the MOD10 daily albedo product is about three times higher. One decade (2000–2009) of MODIS data were then processed to create a time series of albedo maps of Saint Sorlin Glacier during the ablation season. The annual mass balance of Saint Sorlin Glacier was compared with the minimum albedo value (average over the whole glacier surface) observed with MODIS during the ablation season. A strong linear correlation exists between the two variables. Furthermore, the date when the average albedo of the whole glacier reaches a minimum closely corresponds to the period when the snow line is located at its highest elevation, thus when the snow line is a good indicator of the glacier equilibrium line. This indicates that this strong correlation results from the fact that the minimal average albedo values of the glacier contains considerable information regarding the relative share of areal surfaces between the ablation zone (i.e. ice with generally low albedo values) and the accumulation zone (i.e. snow with a relatively high albedo). As a consequence, the monitoring of the glacier surface albedo using MODIS data can provide a useful means to evaluate the interannual variability of the glacier mass balance. Finally, the albedo in the ablation area of Saint Sorlin Glacier does not exhibit any decreasing trend over the study period, contrasting with the results obtained on Morteratsch Glacier in the Swiss Alps.

Regional parameterization of canopy onset models using MODIS and flowering onset data

Canopy phenological onset (CPO) is a phenomenon of start of vegetation growing season and is an important indicator for monitoring the response of vegetation to climate change. In this study, refined detection methods of CPO date were developed for three different vegetation products from the Moderate Resolution and Imaging Spectroradiometer (MODIS). The methods adopt a parameterization scheme supported by a regional observation network of cherry-blossom flowering date. Two alternative onset algorithms (i.e. chill and thermal models) were applied to develop region-scale CPO models using the MODIS-derived CPO date. The satellite-detected MODIS CPO dates were linearly correlated with the field CPO dates (r>0.9), while the model-estimated CPO dates were generally later than the field CPO dates (mean errors, ME from −1.9 to +6.9 days). Though the thermal model algorithm predicted the CPO date slightly better (ME of +2.5 days) than the chill days model algorithm (+4.3 days), the difference was not statistically significant. In the CPO detection, three kinds of MODIS vegetation indices showed similar detection errors of −1.0, +1.7, and +0.5 days for MODIS 1km LAI and NDVI, and 250m NDVI, respectively. In the CPO prediction, however, the thermal and chill models parameterized with MODIS LAI showed significantly better accuracies (ME of −1.9 and +0.5 days) than those parameterized with MODIS NDVI datasets (ME from +4.0 to +6.9). These results show that the combining datasets of field flowering onset data and MODIS vegetation products provide a useful tool to improve the satellite-based detection of CPO date with less uncertainty and to parameterize region-scale CPO models where the field CPO data is less available.

Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data

Different sources of atmospheric water vapor and temperature profiles were used with a radiative transfer model for retrieving land surface temperature (LST) from thermal infrared remote sensing data with the so-called single channel (SC) method. Retrieved LSTs were compared to concurrent ground measurements over homogeneous rice fields to assess the accuracy of the atmospheric profiles. These included radiosonde balloons launched at the test site near-concurrently to satellite overpasses, re-analysis profiles from the National Centers for Environmental Prediction (NCEP), and satellite sounder products from the Atmospheric Infrared Sounder (AIRS) and the Moderate Imaging Spectroradiometer (MODIS; MOD07 product). SC LSTs were computed for Enhanced Thematic Mapper+ (ETM+), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), MODIS, and Advanced Along-Track Scanning Radiometer (AATSR). Results show that radiosonde profiles provided the best agreement between ground-measured and satellite-derived LSTs, with root mean square difference (RMSD) better than 1.0 K and biases within ± 0.5 K for most of the cases. As an alternative to radiosonde profiles, NCEP and MOD07 data yielded reasonable results with RMSDs around 1.0 K, although LSTs derived from MOD07 profiles showed a slight overestimation (0.5 to 1.0 K) of the ground LSTs. AIRS profiles usually underestimated the ground LSTs by 1 to 2 K, probably due to the large temporal gap (2–3 h) with the other satellite measurements. We propose a test to assess the suitability of atmospheric profiles applicable to sensors with bands at 11 and 12 μm in the split-window. So this test plus the SC method may be called split-window SC algorithm, being significantly different from the simple SC method. It implies the calculation of the difference between the LST derived from both bands (T ig − T jg), which should be close to zero if the atmospheric profile is accurate and the surface emissivity is well known. A small range of T ig − T jg values around zero can be set for which the LST derived at 11 μm is accurate. Using only the profiles passing the test, LSTs derived from MODIS band 31 agreed with the ground data with a mean bias of − 0.1 K (ground minus satellite) and RMSD of 0.6 K, while AATSR band at 11 μm yielded a − 0.1 K bias and RMSD = 0.5 K.

고해상도 위성영상을 이용한 낙동강 유역의 클로로필-a 농도 추정

본 연구에서는 Moderate Imaging Spectroradiometer(MODIS), Sea-viewing Wide Fieldfo-view Sensor(SeaWiFS), Medium Resolution Imaging Spectrometer(MERIS) 등의 광역관측 위성 영상을 이용한 해수나 연안수의 클로로필 농도 분석을 통해 가능성이 확인되었던 밴드 비를 이용한 비교적 간단한 추정 모델을 수체의 크기와 폭이 현저히 작고 탁도가 있는 하천에 대해 클로로필- a농도값을 추정하고자 고해상도 위성영상에 Two-band 및 Three-band reflectance 모델을 적용하여 가능성을 파악하였다. 특히 RapidEye 영상을 이용하여 일반적으로 탁도가 있는 수체에 대해 Red와 NIR 영역을 활용하는 이들 모델에 Red-edge(RE) 밴드를 적용하였다. Red와 NIR을 이용한 Two-band Reflectance 모델은 계산식의 결정계수 R2 값이 0.38로 유의성 없는 결과를 나타내었다. 그러나 RapidEye의 Red-edge (RE) 파장대를 이용한 Red-RE Two-band 모델과 Red-RE-NIR Three-band 모델을 이용한 계산식에 대해서는, 2차함수에 의한 Three-band 모델의 결과는 Red-RE Two-band 모델의 결과와 통계적인 값이 거의 유사하였고 Two-band와 3차함수에 의한 Three-band 모델 추정식은 각각 0.66, 0.73 의 R2값을 나타내어 Red-edge 밴드의 적용 가능성을 보였고, 실측치와의 Root Mean Square Error (RMSE)는 24.8, 22.4 mg m-3, Relative Percent Difference(RPD)는 각각 1.30, 1.29로 1.5 이하의 대략적인 추정(Approximate Prediction) 수준을 나타내었다. 고해상도 위성영상에 Red-RE-NIR Three-band 모델을 적용한 계산식을 이용해 대략적인 추정이지만 가장 유의한 수준의 클로로필- a농도를 추정할 수 있었다. 영상에서 추정된 클로로필 -a분포를 비교하였을 때 3차함수에 의한 Three-band 모델 추정식이 Two-band 모델에 비해 낮은 값의 분포를 보였다. 향후 하천의 스펙트럼을 실측하여 파장별 부유물질, 유기물과의 상관성 및 클로로필 농도와의 간섭 정도를 시뮬레이션하여 보정식을 산출·적용한다면 탁도가 다소 높은 하천에서의 클로로필 -a농도 계산식의 정확도를 더욱 높일 수 있을 것으로 기대된다.

Convective cloud discrimination using multi-frequency microwave signatures of the AMSR-E sensor: evaluation over the Tibetan Plateau

Multi-frequency passive microwave remote sensing affords a unique opportunity to understand various phenomena; low-frequency microwaves penetrate clouds and are able to observe Earth surface conditions (∼6–18 GHz), while the higher frequencies are strongly impacted by prevailing atmospheric conditions. By using these relationships, an atmospheric opacity index (AOI) using Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) multi-frequency data is proposed. This index utilizes four AMSR-E frequencies spanning both high- and low-microwave frequency. This AOI can be used to discriminate cloudy atmosphere from clear-sky conditions. This index shows good agreement with current cloud indices. In this research, it is compared against the Moderate Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellite 9th series (GOES-9) atmosphere products. It offers the possibility of detecting convective clouds at all times (day and night) due to the advantage of the independence of the microwave sensors on the Sun for illumination.

A new climatology for remote sensing over land based on the inherent optical properties

The AErosol RObotic NETwork (AERONET) of CIMEL ground-based radiometers, which covers the entire globe, has already been used in many previous studies for validating the inherent optical properties (IOPs) of aerosols. The IOPs are generally computed from microphysical properties using Mie's theory. Many recent studies link the uncertainties in these IOPs with the use of aerosol climatology based on microphysical properties. The objective of this study was to propose an alternative aerosol climatology based on the aerosol IOPs derived directly from CIMEL sky radiance measurements using our in-house iterative method. To achieve this task, an AERONET database of CIMEL sky radiance sequences was first processed using a specific set of criteria. Then, a representative subset of CIMEL data collected over the Venice site was used to refine the selected criteria and to evaluate new criteria for the data quality check, such as the preliminary tests applied to CIMEL sky radiances, the speed-up of convergence of our aerosol phase function retrieval algorithm, and the consistency of its outputs. Because the extraction of the phase function requires a good knowledge of the surface reflectance, we used MODerate Imaging Spectroradiometer (MODIS) albedo maps, which are available on a monthly basis. Statistical methods were then applied to the CIMEL database to discard the incorrect sequences and to suggest a classification of the aerosol models on the basis of their IOPs. The need to include specific aerosol models in this climatology to account for Saharan dust storms, as well as to describe the Asian dust events, was also investigated in this study. Moreover, the medium-resolution imaging spectrometer (MERIS) look-up tables (LUTs) generated over land with these new aerosol IOPs are fully compatible with the current level-2 algorithms. The improvement expected with this climatology will be evaluated using the MERIS imagery.

Characteristics of aerosol types during large-scale transport of air pollution over the Yellow Sea region and at Cheongwon, Korea, in 2008

Episodes of large-scale transport of airborne dust and anthropogenic pollutant particles from different sources in the East Asian continent in 2008 were identified by National Oceanic and Atmospheric Administration satellite RGB (red, green, and blue)-composite images and the mass concentrations of ground level particulate matter. These particles were divided into dust, sea salt, smoke plume, and sulfate by an aerosol classification algorithm. To analyze the aerosol size distribution during large-scale transport of atmospheric aerosols, aerosol optical depth (AOD) and fine aerosol weighting (FW) of moderate imaging spectroradiometer aerosol products were used over the East Asian region. Six episodes of massive airborne dust particles, originating from sandstorms in northern China, Mongolia, and the Loess Plateau of China, were observed at Cheongwon. Classified dust aerosol types were distributed on a large-scale over the Yellow Sea region. The average PM10 and PM2.5 ratio to the total mass concentration TSP were 70% and 15%, respectively. However, the mass concentration of PM2.5 among TSP increased to as high as 23% in an episode where dust traveled in by way of an industrial area in eastern China. In the other five episodes of anthropogenic pollutant particles that flowed into the Korean Peninsula from eastern China, the anthropogenic pollutant particles were largely detected in the form of smoke over the Yellow Sea region. The average PM10 and PM2.5 ratios to TSP were 82% and 65%, respectively. The ratio of PM2.5 mass concentrations among TSP varied significantly depending on the origin and pathway of the airborne dust particles. The average AOD for the large-scale transport of anthropogenic pollutant particles in the East Asian region was measured to be 0.42 ± 0.17, which is higher in terms of the rate against atmospheric aerosols as compared with the AOD (0.36 ± 0.13) for airborne dust particles with sandstorms. In particular, the region ranging from eastern China, the Yellow Sea, and the Korean Peninsula to the Korea East Sea was characterized by high AOD distributions. In the episode of anthropogenic polluted aerosols, FW averaged 0.63 ± 0.16, a value higher than that in the episode of airborne dust particles (0.52 ± 0.13) with sandstorms, showing that fine anthropogenic pollutant particles contribute greatly to atmospheric aerosols in East Asia.

An accuracy assessment of the CALIOP/CALIPSO version 2/version 3 daytime aerosol extinction product based on a detailed multi-sensor, multi-platform case study

Abstract. The Cloud Aerosol LIdar with Orthogonal Polarization (CALIOP), on board the CALIPSO platform, has measured profiles of total attenuated backscatter coefficient (level 1 products) since June 2006. CALIOP's level 2 products, such as the aerosol backscatter and extinction coefficient profiles, are retrieved using a complex succession of automated algorithms. The goal of this study is to help identify potential shortcomings in the CALIOP version 2 level 2 aerosol extinction product and to illustrate some of the motivation for the changes that have been introduced in the next version of CALIOP data (version 3, released in June 2010). To help illustrate the potential factors contributing to the uncertainty of the CALIOP aerosol extinction retrieval, we focus on a one-day, multi-instrument, multiplatform comparison study during the CALIPSO and Twilight Zone (CATZ) validation campaign on 4 August 2007. On that day, we observe a consistency in the Aerosol Optical Depth (AOD) values recorded by four different instruments (i.e. space-borne MODerate Imaging Spectroradiometer, MODIS: 0.67 and POLarization and Directionality of Earth's Reflectances, POLDER: 0.58, airborne High Spectral Resolution Lidar, HSRL: 0.52 and ground-based AErosol RObotic NETwork, AERONET: 0.48 to 0.73) while CALIOP AOD is a factor of two lower (0.32 at 532 nm). This case study illustrates the following potential sources of uncertainty in the CALIOP AOD: (i) CALIOP's low signal-to-noise ratio (SNR) leading to the misclassification and/or lack of aerosol layer identification, especially close to the Earth's surface; (ii) the cloud contamination of CALIOP version 2 aerosol backscatter and extinction profiles; (iii) potentially erroneous assumptions of the aerosol extinction-to-backscatter ratio (Sa) used in CALIOP's extinction retrievals; and (iv) calibration coefficient biases in the CALIOP daytime attenuated backscatter coefficient profiles. The use of version 3 CALIOP extinction retrieval for our case study seems to partially fix factor (i) although the aerosol retrieved by CALIOP is still somewhat lower than the profile measured by HSRL; the cloud contamination (ii) appears to be corrected; no particular change is apparent in the observation-based CALIOP Sa value (iii). Our case study also showed very little difference in version 2 and version 3 CALIOP attenuated backscatter coefficient profiles, illustrating a minor change in the calibration scheme (iv).

Estimation of chlorophyll-a using MERSI and MODIS images in Taihu Lake,China

The Chlorophyll-a(Chl-a) concentrations in water are of great importance to the monitoring of water quality and ecosystem balance.Remote sensing offers a convenient and systematical tool for the observations of water at a long time scale.In this paper,we present a study of Chl-a estimation using the reflectance models(MOD3: R-1λ1-R-1λ2)×Rλ3and MOD2:R-1λ1×Rλ3) derived from the Medium Resolution Spectral Image(MERSI) onboard the newly launched FY-3A satellite and the Moderate Imaging Spectroradiometer(MODIS) onboard the AQUA platforms.Validation studies demonstrated that both models provided reliable estimates of Chl-a concentrations with determination coefficients R2 of 0.72~0.79(MOD2) and 0.52~0.76(MOD3) for MERSI standard band settings.This accuracy is slightly better than that of the MODIS results with R2 of 0.65～0.69 and 0.43~0.70 for MOD2 and MOD3,respectively.Comparison analysis between models and sensors indicated that the blue and near infrared wave ranges are of potential for Chl-a estimation.Besides,the higher spatial resolution of MERSI(250m) may explain the better performance for both models compared to that of MODIS.This research will be helpful for the development of future Chl-a estimation models using the satellite observations.

Fast Bayesian Analysis of Spatial Dynamic Factor Models for Multitemporal Remotely Sensed Imagery

SummaryRemote sensing is one example where data sets that vary across space and time have become so large that ‘standard’ approaches employed by statistical modellers for applied analysis are no longer feasible. We present a Bayesian methodology, which makes use of recently developed algorithms in applied mathematics, for the analysis of large space–time data sets. In particular, a Markov chain Monte Carlo algorithm is proposed for the efficient estimation of spatial dynamic factor models. The spatial dynamic factor model is specified whereby spatial dependence is modelled though the columns of the factor loadings matrix by using a Gaussian Markov random field. Krylov subspace methods are used to take advantage of the sparse matrix structures that are inherent in the model. The methodology is used to analyse remotely sensed data from the Moderate Imaging Spectroradiometer satellite. In particular, the methodology proposed is used in conjunction with high resolution imagery for the classification, in terms of land type, of two regions in central Queensland, Australia.

A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors

A sequential model is developed to disaggregate microwave-derived soil moisture from 40 km to 4 km resolution using MODIS (Moderate Imaging Spectroradiometer) data and subsequently from 4 km to 500 m resolution using ASTER (Advanced Scanning Thermal Emission and Reflection Radiometer) data. The 1 km resolution airborne data collected during the three-week National Airborne Field Experiment 2006 (NAFE'06) are used to simulate the 40 km pixels, and a thermal-based disaggregation algorithm is applied using 1 km resolution MODIS and 100 m resolution ASTER data. The downscaled soil moisture data are subsequently evaluated using a combination of airborne and in situ soil moisture measurements. A key step in the procedure is to identify an optimal downscaling resolution in terms of disaggregation accuracy and sub-pixel soil moisture variability. Very consistent optimal downscaling resolutions are obtained for MODIS aboard Terra, MODIS aboard Aqua and ASTER, which are 4 to 5 times the thermal sensor resolution. The root mean square error between the 500 m resolution sequentially disaggregated and ground-measured soil moisture is 0.062 vol./vol. with a bias of − 0.045 vol./vol. and values ranging from 0.08 to 0.40 vol./vol.