Terra Moderate Resolution Imaging Spectroradiometer Data Research Articles

Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis

This study examined long-term aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify aerosol conditions on the Korean Peninsula. Time-series machine learning (ML) techniques and spatial interpolation methods were used to predict future aerosol trends. This investigation utilized AOT data from Terra MODIS and meteorological data from Automatic Weather System (AWS) in eight selected cities in Korea (Gangneung, Seoul, Busan, Wonju, Naju, Jeonju, Jeju, and Baengyeong) to assess atmospheric aerosols from 2000 to 2021. A machine-learning-based AOT prediction model was developed to forecast future AOT using long-term observations. The accuracy analysis of the AOT prediction results revealed mean absolute error of 0.152 ± 0.15, mean squared error of 0.048 ± 0.016, bias of 0.002 ± 0.011, and root mean squared error of 0.216 ± 0.038, which are deemed satisfactory. By employing spatial interpolation, gridded AOT values within the observation area were generated based on the ML prediction results. This study effectively integrated the ML model with point-measured data and spatial interpolation for an extensive analysis of regional AOT across the Korean Peninsula. These findings have substantial implications for regional air pollution policies because they provide spatiotemporal AOT predictions.

Relationship between Boro Rice Production and MODIS Derived NDVI for Rice Production Forecasting: A Case Study on Bangladesh

The present investigation illustrates an inclusive approach to extract remotely sensed Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) (AQUA/TERRA) imageries to find out a relationship with Boro rice production for forecasting crop production in the context of Bangladesh. This study utilizes AQUA/TERRA MODIS reflectance data (250 m resolution) for the month of March (Peak-greenness period) to calculate the average NDVI values by following MODIS based algorithm at district level during 2011-2016. The linear regression analysis of calculated average NDVI and BBS estimated Boro rice production statistics reveals a significant positive relationship due to maximize photosynthetic activities. Among the regression equations from (2011-2016), the highest regression coefficients R2=0.87 and R2=0.85 for AQUA and TERRA MODIS data have been found respectively in 2015. Therefore this regression equation can be used for future estimation of Boro rice production at country scale. However, further testing and simulation of this regression model is required to generate Boro rice production forecasting dataset on timely basis. Hence this study summarizes that, NDVI based regression equation may be an effective process to forecast the Boro rice production which can play an important role in decision-making process relevant to the food security issues of Bangladesh.
 The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(1), 2019, P 33-40

Calibration of global MODIS cloud amount using CALIOP cloud profiles

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOVs) as either “confident clear”, “probably clear”, “probably cloudy”, or “confident cloudy”. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by the MODIS Science Team assumes that confident clear and probably clear IFOVs are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this “best-guess” approach is unreliable, especially on a regional/local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with Aqua MODIS IFOV. Based on 33 793 648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in polar regions at night, and in selected locations over the Northern Hemisphere (e.g. China, the north-west coast of Africa, and eastern parts of the United States). Consequently, applications of MODIS data on a regional/local scale should first assess the extent of the uncertainty. We suggest using CALIPSO-based cloud fractions to improve MODIS cloud amount estimates. This approach can also be used for Terra MODIS data, and other passive cloud imagers, where the footprint is collocated with CALIPSO.

Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China

ABSTRACTHuman activities and climate change have changed the vegetation in China. The analysis of the changes in vegetation that have occurred over the past 30 years in China remains a great challenge due to intense human activity and lack of field observations. The use of various Normalized Difference Vegetation Index (NDVI) datasets to study vegetation coverage changes has received much attention. In this paper, we selected the early versions of Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Monitoring and Modelling Studies (GIMMS), GIMMS3g (third generation GIMMS NDVI from AVHRR sensors) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data including the fusion data (GIMMS+MODIS). We analysed spatial and temporal changes in vegetation cover in different ecosystems and basins in the mainland of China. Different contributions of ecosystems and variations in NDVI trends exist in different ecosystems in 17 basins. The results show that different NDVI from different data sources yield different results: (1) Vegetation increased in 74.62–77.7% of the area of the Chinese mainland during 1982–2015, mainly in the Yellow River and the middle reaches of the Yangtze River basin; (2) 2000–2017 MODIS NDVI in mainland China has increased more area (79.67%). (3) Farmland and Forest ecosystems were significantly enhanced in the eastern monsoon region; (4) High-resolution NDVI can provide more information than domain average NDVI. GIMMS and MODIS NDVI data have complementary spatial and temporal distributions. Our study improves the understanding of vegetation dynamics over long time periods and large areas and, moreover, has potential for supporting ecological managers in mainland China.

Developing a fused vegetation temperature condition index for drought monitoring at field scales using Sentinel-2 and MODIS imagery

Time-series high spatial resolution drought monitoring is essential for effective agricultural management. For more than a decade, the multiyear vegetation temperature condition index (VTCI) based on the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) has been applied to regional drought monitoring. However, the spatial resolutions of AVHRR and MODIS (1 km) often represent mixtures of built-up areas and different vegetation or crop types. In this paper, a framework is proposed to obtain a ten-day interval multiyear VTCI at field scales, which is fused from Sentinel-2 data with a fine spatial resolution (20 m) and ten-day interval Terra MODIS data with a coarse spatial resolution using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). This framework includes the disaggregation of the Terra MODIS land surface temperature (LST) using Sentinel-2 data and digital elevation model (DEM) data, the spatiotemporal fusion of normalized difference vegetation index (NDVI) and LST, and drought monitoring by the fused VTCI. The accuracy of this framework for regional drought monitoring was tested in the Guanzhong Plain of China. The results indicate that 1) the Sentinel-2 biophysical products combined with DEM data based on support vector regression can accurately disaggregate the Terra MODIS LST; 2) the ESTARFM has good capability to fuse the NDVI and LST derived from Sentinel-2 and Terra MODIS; 3) the multiyear spatiotemporally fused VTCI, a quantitative drought monitoring index at field scales, can be calculated by using the linear regression between the single-year fused VTCI based on the data in 2018 and the multiyear Terra MODIS VTCI based on an 18-year data record; and 4) the spatiotemporal fusion of the multiyear VTCI can significantly improve the drought monitoring accuracy in the winter wheat and woodland area of the Guanzhong Plain. From mid-March to early May, the multiyear spatiotemporally fused VTCIs have better correlation with the cumulative precipitation over the past 20 days (R2 is 0.83 in the entire study area) than the multiyear Terra MODIS VTCIs (R2 is 0.80 in the entire study area). The results of this study demonstrate the potential of using the spatiotemporal fusion algorithm to obtain the multiyear VTCI at field scales and provide an effective method for improving the accuracy of drought monitoring.

Climatology of aerosol optical depth over Eastern Europe based on 19 years (2000–2018) MODIS TERRA data

AbstractThis study investigates the spatial–temporal evolution of aerosols, their optical properties (aerosol optical depth [AOD] and ångström exponent [AE]) and the trend over a period of 19 years in the Eastern European countries. The data used for the study are from moderate‐resolution imaging spectroradiometer (MODIS) Terra Collection 6.1 aerosol products with the use of Dark Target algorithm for the period from 2000 to 2018. The satellite‐based AOD products were validated against the AERONET AOD measurements in four stations, located in different regions. The results demonstrate high coherence in the setting of high values of correlation (R = 0.8–0.9) and low values of root‐mean‐square error (RMSE [0.066–0.130]) and mean absolute error (MAE [0.048–0.067]). However, of the total amount of data from AERONET stations, only 68.9% of available data fall within expected error (EE) over land. Generally, all countries recorded a low AOD (83.2% less than 0.2) and high AE (82.5% higher than 1). Mean AOD and AE values varied from 0.17 ± 0.12 and 1.31 ± 0.27 (Russia) to 0.24 ± 0.14 and 1.31 ± 0.33 (Czech Republic). There was a gradual decrease in aerosol load in all countries, the highest tendencies of AOD reduction are observed in Czech Republic, Bulgaria, Slovakia and Hungary (by −0.0028, −0.0027, −0.0026 and −0.0025 per year), while the lowest tendencies of reduction are observed in Russia and Moldova (by −0.001 and −0.0006 per year). Seasonal averaged AOD values have maximum values in summer and minimum values in winter. We studied predominant aerosol types, which are based on AOD and AE interrelation, the results showed the predominance of clean continental and mixed aerosols over all countries of the region.

Satellite data cloud detection using deep learning supported by hyperspectral data

ABSTRACTDeep learning methods can play an important role in satellite data cloud detection. The number and quality of training samples directly affect the accuracy of cloud detection based on deep learning. Therefore, selecting a large number of representative and high-quality training samples is a key step in cloud detection based on deep learning. For different satellite data sources, choosing sufficient and high-quality training samples has become an important factor limiting the application of deep learning in cloud detection. This paper presents a fast method for obtaining high-quality learning samples, which can be used for cloud detection of different satellite data with deep learning methods. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data, which have 224 continuous bands in the spectral range from 400–2500 nm, are used to provide cloud detection samples for different types of satellite data. Through visual interpretation, a sufficient number of cloud and clear sky pixels are selected from the AVIRIS data to construct a hyperspectral data sample library, which is used to simulate different satellite data (such as data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Operational Land Imager (OLI) satellites) as training samples. This approach avoids selecting training samples for different satellite sensors. Based on the Keras deep learning framework platform, a backpropagation (BP) neural network is employed for cloud detection from Landsat 8 OLI, National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and Terra MODIS data. The results are compared with cloud coverage results interpreted via artificial vision. The results demonstrate that the algorithm achieves good cloud detection results for the above data, and the overall accuracy is greater than 90%.

Northern Hemisphere contrail properties derived from Terra and Aqua MODIS data for 2006 and 2012

Abstract. Linear contrail coverage, optical property, and radiative forcing data over the Northern Hemisphere (NH) are derived from a year (2012) of Terra and Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) imagery and compared with previously published 2006 results (Duda et al., 2013; Bedka et al., 2013; Spangenberg et al., 2013) using a consistent retrieval methodology. Differences in the observed Terra-minus-Aqua screened contrail coverage and patterns in the 2012 annual-mean air traffic estimated with respect to satellite overpass time suggest that most contrails detected by the contrail detection algorithm (CDA) form approximately 2 h before overpass time. The 2012 screened NH contrail coverage (Mask B) shows a relative 3 % increase compared to 2006 data for Terra and increases by almost 7 % for Aqua, although the differences are not expected to be statistically significant. A new post-processing algorithm added to the contrail mask processing estimated that the total contrail cirrus coverage visible in the MODIS imagery may be 3 to 4 times larger than the linear contrail coverage detected by the CDA. This estimate is similar in magnitude to the spreading factor estimated by Minnis et al. (2013). Contrail property retrievals of the 2012 data indicate that both contrail optical depth and contrail effective diameter decreased approximately 10 % between 2006 and 2012. The decreases may be attributed to better background cloudiness characterization, changes in the waypoint screening, or changes in contrail temperature. The total mean contrail radiative forcings (TCRFs) for all 2012 Terra observations were −6.3, 14.3, and 8.0 mW m−2 for the shortwave (SWCRF), longwave (LWCRF), and net forcings, respectively. These values are approximately 20 % less than the corresponding 2006 Terra estimates. The decline in TCRF results from the decrease in normalized CRF, partially offset by the 3 % increase in overall contrail coverage in 2012. The TCRFs for 2012 Aqua are similar, −6.4, 15.5, and 9.0 mW m−2 for shortwave, longwave, and net radiative forcing. The strong correlation between the relative changes in both total SWCRF and LWCRF between 2006 and 2012 and the corresponding relative changes in screened contrail coverage over each air traffic region suggests that regional changes in TCRF from year to year are dominated by year-to-year changes in contrail coverage over each area.

Snow Cover Estimation From Image Time Series Based on Spectral Unmixing

A method based on spectral unmixing (SU) for snow cover estimation from a time series of optical images is proposed in this letter. Specifically, we have developed an endmember estimation procedure that exploits the temporal continuity of a scene. Consecutive dates are jointly processed for a more precise description of background materials improving the estimation of a fractional snow cover map. The proposed workflow relies on up to three consecutive acquisitions over the same area to extract an appropriate set of background spectra to be considered as endmembers. In greater details, multiple sets of endmembers are extracted from different images in the time series by a geometrical automated endmember extraction algorithm and the most relevant one is selected in terms of reconstruction error. Snow cover maps are then estimated by SU considering as endmembers the snow spectra coming from a spectral library and those associated with the background materials as estimated by the proposed procedure. The proposed technique is quantitatively validated considering Moderate-Resolution Imaging Spectroradiometer Terra data over the French Alps and Moroccan High Atlas image time series and comparing the estimated snow cover maps with high-resolution reference data. The experiment clearly demonstrates the effectiveness of the generated set of endmembers using three different approaches to abundance estimation.

Global Validation of MODIS Near‐Surface Air and Dew Point Temperatures

AbstractThis analysis is the first global validation of the Moderate Resolution Imaging Spectroradiometer (MODIS)‐derived near‐surface air temperature and dew point estimates, which both serve as crucial input data in models of energy, water, and carbon exchange between terrestrial ecosystems and the atmosphere. By hypsometrically interpolating the MOD07 Level‐2 atmospheric profile product to surface pressure level, we obtained near‐surface air temperature and dew point observations at 5 km pixel resolution. We compared these daily data, retrieved over a 14‐year record, to corresponding measurements from 109 ground meteorological stations (FLUXNET). Our results show strong agreement between satellite and in situ near‐surface air temperature measurements (R2 = 0.89, root‐mean‐square error = 3.47°C, and bias = −0.19°C) and dew point observations (R2 = 0.76, root‐mean‐square error = 5.04°C, and bias = 0.79°C) with insignificant differences in error across climate zones. This validation is among the earliest assessments of the reprocessed, crosstalk‐corrected Collection 6.1 Terra MODIS data and provides support for widespread applications of near‐surface atmospheric data.

Temporal and spatial changes in estimated near‐surface air temperature lapse rates on Tibetan Plateau

Lapse rate (LR) of near‐surface (2 m) air temperature is essential for determining spatially distributed and gridded air temperature interpolated from in situ observational sites. However, due to the limitation of sparse observational networks, high‐resolution LRs are not usually available on regional scales, especially in mountainous regions. The purpose of this study is to estimate LRs for the entire Tibetan Plateau (TP) using observed air temperature and moderate‐resolution imaging spectroradiometer (MODIS) night‐time land surface temperatures (LSTs) and to analyse the spatio‐temporal changes of the estimated LRs. First, diurnal cycles of LRs derived from in situ observations in three subregions of the TP were analysed, which shows that the LRs in the western and northeastern regions were shallow in the cold season and steep in the warm season, whereas the southeastern region exhibited a different pattern. Further comparisons revealed that the LRs for night‐time air temperatures better represented the LRs for daily mean air temperatures than the daytime ones, and the night‐time MODIS LSTs correlated well with the night‐time air temperatures, especially for the MODIS Terra data sets. Therefore, the night‐time MODIS LSTs from the Terra data sets were used to estimate high‐resolution (10 km) LRs for the daily mean temperatures over the entire TP. Estimated LRs over most areas of the TP were shallower than the commonly used environmental LR (−6.5 K/km). The LRs in the southeastern region were steeper than those in the northeastern region, while steeper LR values occurred in the northwestern region with lower temperatures and less humidity.

Estimation of the Near-Surface Air Temperature during the Day and Nighttime from MODIS in Berlin, Germany

Air temperature (Tair or T2m) is an important climatological variable for forest biosphere processes and climate change research. Due to the low density and the uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of Tair. In this study, Tair in Berlin is estimated during the day and night time over six land cover/land use (LC/LU) types by satellite remote sensing data over a large domain and a relatively long period (7 years). Aqua and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the period from 2007 to 2013 were collected to estimate Tair. Twelve environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), Julian day, latitude, longitude, Emissivity31, Emissivity32, altitude, albedo, wind speed, wind direction and air pressure) were selected as predictors. Moreover, a comparison between LST from MODIS Terra and Aqua with daytime and night time air temperatures (Tday, Tnight) was done respectively and in addition, the spatial variability of LST and Tair relationship by applying a varying window size on the MODIS LST grid was examined. An analysis of the relationship between the observed Tair and the spatially averaged remotely sensed LST, indicated that 3 × 3 and 1 × 1 pixel size was the optimal window size for the statistical model estimating Tair from MODIS data during the day and night time, respectively. Three supervised learning methods (Adaptive Neuro Fuzzy Inference system (ANFIS), Artificial Neural Network (ANN) and Support vector machine (SVR)) were used to estimate Tair during the day and night time, and their performances were validated by cross-validation for each LC/LU. Moreover, tuning the hyper parameters of some models like SVR and ANN were investigated. For tuning the hyper parameters of SVR, Simulated Annealing (SA) was applied (SA-SVR model) and a multiple-layer feed-forward (MLF) neural networks with three layers and different nodes in hidden layers are used with Levenber-Marquardt back-propagation (LM-BP), in order to achieve higher accuracy in the estimation of Tair. Results indicated that the ANN model achieved better accuracy (RMSE= 2.16°C, MAE = 1.69°C, R2 = 0.95) than SA_SVR model (RMSE= 2.50°C, MAE = 1.92°C, R2 = 0.91) and ANFIS model (RMSE= 2.88°C, MAE= 2.2°C, R2 = 0.89) over six LC/LU during the day and night time. The Q-Q diagram of SA-SVR, ANFIS and NN show that all three models slightly tended to underestimate and overestimate the extreme and low temperatures for all LC/LU classes during the day and night time. The weak performance in the extreme and low temperatures are a consequence of the small number of data in these temperatures. These satisfactory results indicate that this approach is proper for estimating air temperature and spatial window size is an important factor that should be considered in the estimation of air temperature.

The Modified SEBAL for Mapping Daily Spatial Evapotranspiration of South Korea Using Three Flux Towers and Terra MODIS Data

Evapotranspiration (ET) is expected to increase by a considerable amount because of the impact of future temperature increase. Nowadays, the daily to seasonal ET maps can be used to provide information for a sustainable and adaptive watershed eco-environment. This study attempts to estimate the spatial ET of South Korea (99,900 km2), located within the latitudes of 33°06′N to 43°01′N and the longitudes of 124°04′E to 131°05′E, on a daily basis. The satellite-based image-processing model Surface Energy Balance Algorithms for Land (SEBAL) was adopted and modified to generate the spatial ET data. The SEBAL was calibrated using two years (2012–2013) of measured ETs by an eddy covariance (EC) flux tower at three locations (two in a mixed forest area and one in a rice paddy area). The primary inputs for the model were land surface temperature/emissivity (LST/E), the Normalized Distribution Vegetation Index (NDVI), albedo (Ab) from a Terra Moderate-resolution Imaging Spectroradiometer (MODIS) satellite, a digital elevation model, and wind speed and solar radiation (Rs) from 76 ground-based weather stations. When LST data were unavailable because of clouds and/or snow, the bias-corrected ground temperature measured at the weather stations was used. The NDVI and Ab were used as the monthly average value to maintain relatively stable values rather than using the original time interval data. The determination coefficient (R2) between SEBAL and the flux tower ET was 0.45–0.54 for the two mixed forest towers and 0.79 for the rice paddy tower reflecting the known characteristics of closed and open space ET estimation. The spatial distribution of SEBAL showed that the spatial ET reflected the geographical characteristics, revealing that the ET of lowland areas was higher than that of highland areas.

Fundamental Research on Spring Season Daytime Sea Fog Detection Using MODIS in the Yellow Sea

For the safety of sea, it is important to monitor sea fog, one of the dangerous meteorological phenomena which cause marine accidents. To detect and monitor sea fog, Moderate Resolution Imaging Spectroradiometer (MODIS) data which is capable to provide spatial distribution of sea fog has been used. The previous automatic sea fog detection algorithms were focused on detecting sea fog using Terra/MODIS only. The improved algorithm is based on the sea fog detection algorithm by Wu and Li (2014) and it is applicable to both Terra and Aqua MODIS data. We have focused on detecting spring season sea fog events in the Yellow Sea. The algorithm includes application of cloud mask product, the Normalized Difference Snow Index (NDSI), the STandard Deviation test using infrared channel (STDIR) with various window size, Temperature Difference Index(TDI) in the algorithm (BTCT - SST) and Normalized Water Vapor Index (NWVI). Through the calculation of the Hanssen-Kuiper Skill Score (KSS) using sea fog manual detection result, we derived more suitable threshold for each index. The adjusted threshold is expected to bring higher accuracy of sea fog detection for spring season daytime sea fog detection using MODIS in the Yellow Sea.

Effects of crop residue burning on aerosol properties, plume characteristics, and long‐range transport over northern India

AbstractAerosol emissions from biomass burning are of specific interest over the globe due to their strong radiative impacts and climate implications. The present study examines the impact of paddy crop residue burning over northern India during the postmonsoon (October–November) season of 2012 on modification of aerosol properties, as well as the long‐range transport of smoke plumes, altitude characteristics, and affected areas via the synergy of ground‐based measurements and satellite observations. During this period, Moderate Resolution Imaging Spectroradiometer (MODIS) images show a thick smoke/hazy aerosol layer below 2–2.5 km in the atmosphere covering nearly the whole Indo‐Gangetic Plains (IGP). The air mass trajectories originating from the biomass‐burning source region over Punjab at 500 m reveal a potential aerosol transport pathway along the Ganges valley from west to east, resulting in a strong aerosol optical depth (AOD) gradient. Sometimes, depending upon the wind direction and meteorological conditions, the plumes also influence central India, the Arabian Sea, and the Bay of Bengal, thus contributing to Asian pollution outflow. The increased number of fire counts (Terra and Aqua MODIS data) is associated with severe aerosol‐laden atmospheres (AOD500 nm > 1.0) over six IGP locations, high values of Ångström exponent (>1.2), high particulate mass 2.5 (PM2.5) concentrations (>100–150 µgm−3), and enhanced Ozone Monitoring Instrument Aerosol Index gradient (~2.5) and NO2 concentrations (~6 × 1015 mol/cm2), indicating the dominance of smoke aerosols from agricultural crop residue burning. The aerosol size distribution is shifted toward the fine‐mode fraction, also exhibiting an increase in the radius of fine aerosols due to coagulation processes in a highly turbid environment. The spectral variation of the single‐scattering albedo reveals enhanced dominance of moderately absorbing aerosols, while the aerosol properties, modification, and mixing atmospheric processes differentiate along the IGP sites depending on the distance from the aerosol source, urban influence, and local characteristics.

The performance of vegetation indices for operational monitoring of CORINE vegetation types

Vegetation monitoring has been performed using remotely sensed images to secure food production, prevent fires, and protect natural ecosystems. Recent satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), provide frequent wide-scale coverage in multiple areas of the spectrum, allowing the estimation of a wide range of specialized vegetation indices (VIs), each offering several advantages. It is not, however, clear which VI performs better during operational monitoring of wide-scale vegetation patches, such as CORINE Land Cover (CLC) classes. The aim of this work was to investigate the performance of several VIs in operational monitoring of vegetation condition of CLC vegetation types, using Terra MODIS data. Comparison among the VIs within each CLC class was conducted using the sensitivity ratio, a statistical measure that has not been used to compare VIs and does not require calibration curves between each VI and a biophysical parameter. In addition, the VI’s sensitivity to factors such as the aspect, viewing angle, signal saturation, and partial cloud cover was estimated with correlation analysis in order to identify their operational monitoring ability. Results indicate the enhanced vegetation index as superior for monitoring vegetation condition among CLC types, but not always optimum in performance tests for operational monitoring.

Assessment of Snowmelt Impact on Chungju Dam Watershed Inflow Using Terra MODIS Data and SWAT Model

본 연구에서는 충주댐 유역(<TEX>$6,642.0km^2$</TEX>)을 대상으로 Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) 위성영상과 SWAT (Soil and Water Assessment Tool) 모형을 이용하여 융설이 댐유입량에 미치는 영향을 분석하였다. SWAT 모형의 융설 매개변수인 적설면적 감소곡선(Snow Cover Depletion Curve; SCDC)을 결정하기 위해 10 sets (2000-2010)의 Terra MODIS 위성영상으로부터 추출한 적설분포도와 지상 강설자료를 이용하여 구축한 적설심분포도(snow depth distribution; SDD)를 도출한 결과, 50% 적설면적일 때의 적설면적감소계수(sno50cov)는 0.4~0.7의 범위를 가지는 것으로 분석되었다. 매년의 융설 매개변수를 적용하여 융설기간 (11~4월)동안의 유출을 보정한 결과 평균 <TEX>$R^2$</TEX>는 0.54이었다. 10년 동안 융설기간의 유출량은 평균 104.3 mm로 연 유출량의 12.0%를 차지하는 것으로 분석되었다. This study is to evaluate the snowmelt impact on dam inflow for the Chungju Dam watershed <TEX>$6,642.0km^2$</TEX> using Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) and Soil and Water Assessment Tool (SWAT). To determine the SWAT snowmelt parameter; snow cover depletion curve (SCDC) the snow depth distribution (SDD) using Terra MODIS was used, the snow depth was spatially interpolated using snowfall data of ground meteorological stations. For 10 sets (2000-2010) data during snowmelt period (November-April), the sno50cov parameter, that is, the 50% coverage at a fraction of SCDC which determines the shape of snow depletion process, showed the values of 0.4 to 0.7. The SWAT model was calibrated with average <TEX>$R^2$</TEX> of 0.54 using the sno50cov of each year. The 10 years average streamflow during snowmelt period was 104.3 mm which covers 12.0% of the annual streamflow.

Estimation of Daily Sunshine Duration from Terra and Aqua MODIS Data

Some studies have shown that the estimation of global sunshine duration can be done with the help of geostationary satellites because they can record several images of the same location in a day. In this paper, images obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors of polar orbiting satellites Aqua and Terra were used to estimate daily global sunshine duration for any region in Turkey. A new quadratic correlation between daily mean cloud cover index and relative sunshine duration was also introduced and compared with the linear correlation. Results have shown that polar orbiting satellites can be used for the estimation of sunshine duration. The quadratic model introduced here works better than the linear model especially for the winter months in which very low sunshine duration values were recorded at the ground stations for many days.

Surface ozone photolysis rate trends in the Eastern Mediterranean: Modeling the effects of aerosols and total column ozone based on Terra MODIS data

The surface ozone photolysis rate (J(O1D)) was computed on a daily basis and on a 50 km × 50 km resolution for the 11-year period 2000–2010 at Finokalia meteorological station in Crete, Greece. A radiative transfer model was used, with climatological data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra satellite. The area is representative of the Eastern Mediterranean, a region with high variability in aerosol loads and total column ozone. Instantaneous values of J(O1D) computed from the model were validated against corresponding station measurements available during the 5-year period 2002–2006. Monthly mean values of J(O1D) during the 11-year period examined, reveal a statistically significant decreasing trend, based on Terra MODIS data, which shows an overall 13% decrease. The aerosol effect on J(O1D) varies on a daily basis, depending on the aerosol load, and can exceed −10% during dust events, with a median value of −2.3% over the whole period examined. On a seasonal basis, the aerosol effect on J(O1D) follows the seasonal pattern of the aerosol load, with higher values in spring and autumn, due to the increased Saharan dust episodes during these seasons. Linear regression analysis on monthly mean values of total column ozone revealed a statistically significant increasing trend in both Finokalia and Thessaloniki stations. Total column ozone MODIS data were validated against spectroradiometric (columnar) measurements at Thessaloniki station. Sensitivity analysis on the effect of total column ozone on J(O1D) showed that a 10% variation in total ozone causes a corresponding 15–17% change in J(O1D). These results suggest that the decreasing trend in J(O1D) found in the case of Terra MODIS should be attributed mainly to the corresponding increasing trend in total column ozone.

Comparing Ship-Track Droplet Sizes Inferred from Terra and Aqua MODIS Data

AbstractIn this study of ship tracks, Moderate Resolution Imaging Spectroradiometer (MODIS) measurements from late-morning (Terra) and early-afternoon (Aqua) Earth Observing System platforms are analyzed in five separate geographically distributed cases to compare estimates of the sizes (and their changes in time) of droplets associated with ship exhaust. Ship tracks are readily detected in near-infrared imagery as bright features, especially in 2.13-μm observations. The Terra “MOD06” and Aqua “MYD06” cloud products are used to determine the effective radius of the ship-track droplets; droplet age (time in the atmosphere) is estimated as a function of the distance from the ship. Terra and Aqua MODIS estimates of droplet sizes in ship-track plumes are found to be in agreement, with a correlation greater than 0.90; for the cases studied, droplet sizes in the ship plumes are between 6 and 18 μm. Moreover, the droplets’ size growth rates inferred from the length of the ship track were found to average between 0.5 and 1.0 μm h−1.