scholarly journals Ushering in the New Era of Radiometric Intercomparison of Multispectral Sensors with Precision SNO Analysis

Climate ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 81 ◽  
Author(s):  
Chu ◽  
Dodd
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Polar Regions ◽  
New Era ◽  
High Performing ◽  
Comparison Time ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Multispectral Sensors

A “nadir-only” framework of the radiometric intercomparison of multispectral sensors using simultaneous nadir overpasses (SNOs) is examined at the 1-km regimes and below using four polar-orbiting multispectral sensors: the twin MODerate-resolution Imaging Spectroradiometer (MODIS) in the Terra and Aqua satellites, the Visible Imaging Infrared Radiometer Suite (VIIRS) in the Suomi National Polar-orbiting Partnership (SNPP) satellite, and the Ocean and Land Colour Instrument (OLCI) in the Sentinel-3A satellite. The study is carried out in the context of isolating the on-orbit calibration of the reflective solar bands (RSBs) under the “nadir-only” restriction. With a homogeneity-ranked, sample size constrained procedure designed to minimize scene-based variability and noise, the overall approach successfully stabilizes the radiometric ratio and tightens the precision of each SNO-generated comparison event. Improvements to the multiyear comparison time series are demonstrated for different conditions of area size, sample size, and other refinements. The time series demonstrate the capability at 1% precision or better under general conditions but can attain as low as 0.2% in best cases. Solar zenith angle is examined not to be important in the “nadir-only” framework, but the spectral mismatch between two bands can give rise to significant yearly modulation in the comparison time series. A broad-scaled scene-based variability of ~2%, the “scaling phenomenon”, is shown to have pervasive presence in both northern and the southern polar regions to impact inter-RSB comparison. Finally, this paper highlights the multi-instrument cross-comparisons that are certain to take on a more important role in the coming era of high-performing multispectral instruments.

scholarly journals Spatiotemporal Mapping and Monitoring of Whiting in the Semi-Enclosed Gulf Using Moderate Resolution Imaging Spectroradiometer (MODIS) Time Series Images and a Generic Ensemble Tree-Based Model

2019 ◽  
Vol 11 (10) ◽  
pp. 1193 ◽  
Author(s):  
Abdallah Shanableh ◽  
Rami Al-Ruzouq ◽  
Mohamed Barakat A. Gibril ◽  
Cristina Flesia ◽  
Saeed AL-Mansoori
Keyword(s):  
Time Series ◽  
Spectral Band ◽  
Classification Model ◽  
Series Data ◽  
Natural Phenomenon ◽  
Adaptive Boosting ◽  
Rule Based ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Whiting events in seas and lakes are a natural phenomenon caused by suspended calcium carbonate (CaCO3) particles. The Arabian Gulf, which is a semi-enclosed sea, is prone to extensive whiting that covers tens of thousands of square kilometres. Despite the extent and frequency of whiting events in the Gulf, studies documenting the whiting phenomenon are lacking. Therefore, the primary objective of this study was to detect, map and document the spatial and temporal distributions of whiting events in the Gulf using daily images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites from 2002 to 2018. A method integrating a geographic object-based image analysis, the correlation-based feature selection technique (CFS), the adaptive boosting decision tree (AdaBoost DT) and the rule-based classification were used in the study to detect, quantify and assess whiting events in the Gulf from the MODIS data. Firstly, a multiresolution segmentation was optimised using unsupervised quality measures. Secondly, a set of spectral bands and indices were investigated using the CFS to select the most relevant feature(s). Thirdly, a generic AdaBoost DT model and a rule-based classification were adopted to classify the MODIS time series data. Finally, the developed classification model was compared with various tree-based classifiers such as random forest, a single DT and gradient boosted DT. Results showed that both the combination of the mean of the green spectral band and the normalised difference index between the green and blue bands (NDGB), or the combination of the NDGB and the colour index for estimating the concentrations of calcium carbonates (CI) of the image objects, were the most significant features for detecting whiting. Moreover, the generic AdaBoost DT classification model outperformed the other tested tree-based classifiers with an overall accuracy of 97.86% and a kappa coefficient of 0.97. The whiting events during the study period (2002–2018) occurred exclusively during the winter season (November to March) and mostly in February. Geographically, the whiting events covered areas ranging from 12,000 km2 to 60,000 km2 and were mainly located along the southwest coast of the Gulf. The duration of most whiting events was 2 to 6 days, with some events extending as long as 8 to 11 days. The study documented the spatiotemporal distribution of whiting events in the Gulf from 2002 to 2018 and presented an effective tool for detecting and motoring whiting events.

Wetlands classification and assessment of Ramsar sites in China based on time series Moderate Resolution Imaging Spectroradiometer (MODIS) imagery

2018 ◽  
Vol 69 (5) ◽  
pp. 658 ◽  
Author(s):  
Liwei Xing ◽  
Zhenguo Niu ◽  
Panpan Xu ◽  
Dachong Li
Keyword(s):  
Time Series ◽  
Landscape Metrics ◽  
Time Series Data ◽  
Wetland Loss ◽  
Series Data ◽  
Landscape Disturbance ◽  
Landscape Integrity ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Globally, wetland loss and degradation have become serious environmental and ecological issues. Wetland monitoring of Ramsar sites in China is important for developing reasonable strategies to protect wetlands. Satellite image time series may be used for the long-term monitoring of wetland ecosystems. The present study used moderate-resolution imaging spectroradiometer (MODIS) time series data collected in 2001 and 2013 for 20 Ramsar sites in China and assessed the environmental status of these reserves using landscape metrics. The results showed that specific seasonal wetland classes, such as flooded mud, permanent water and seasonal marshes, can be identified using MODIS time series data with acceptable accuracy. In addition to wetland area, we suggest using other landscape metrics, including landscape integrity and landscape disturbance or degradation indices, to assess wetland environmental quality. The slight wetland loss (0.8%) noted in the 20 reserves evaluated herein could indicate the effectiveness of efforts of the Chinese government and local government agencies to protect Ramsar sites. The existing unfavourable environmental conditions, which were manifested by low landscape integrity and high landscape disturbance or degradation for some reserves, were caused primarily by increasing water requirements outside the reserves and by agricultural development within reserves. Therefore, determining how to balance relationships between economic development and ecological protection of the reserves will be important in the future.

scholarly journals Volcanic Ash Retrieval Using a New Geostationary Satellite

2015 ◽  
Vol XL-7/W4 ◽  
pp. 67-74
Author(s):  
K. H. Lee ◽  
K. T. Lee
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Volcanic Ash ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
New Era ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Aerosol Remote Sensing ◽  
Moderate Resolution Imaging Spectroradiometer

The paper presents currently developing method of volcanic ash detection and retrieval for the Geostationary Korea Multi-Purpose Satellite (GK-2A). With the launch of GK-2A, aerosol remote sensing including dust, smoke, will begin a new era of geostationary remote sensing. The Advanced Meteorological Imager (AMI) onboard GK-2A will offer capabilities for volcanic ash remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. Based on the physical principles for the current polar and geostationary imagers are modified in the algorithm. Volcanic ash is estimated in detection processing from visible and infrared channel radiances, and the comparison of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every 15 min for volcanic ash for pixel sizes of 2 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously, both over water and land. The algorithm has been tested with proxy data generated from existing satellite observations and forward radiative transfer simulations. Operational assessment of the algorithm will be made after the launch of GK-2A scheduled in 2018.

scholarly journals Calibration of global MODIS cloud amount using CALIOP cloud profiles

2020 ◽  
Author(s):  
Andrzej Z. Kotarba
Keyword(s):  
Cloud Amount ◽  
Satellite Observation ◽  
Cloud Fraction ◽  
Orthogonal Polarization ◽  
Polar Regions ◽  
Cloud Detection ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOV) as either confident cloudy, probably cloudy, probably clear, or confident clear. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by NASA assumes that confident clear and probably clear IFOV 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 MODIS/ Aqua 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 the polar regions at night, and in selected locations over the northern hemisphere. Applications of MODIS data at ~10 degrees resolution (or finer) should first assess the extent of the error. Uncertainties were related to the efficiency of the cloud masking algorithm. Until the algorithm can be significantly modified, our method is a robust way to calibrate (correct) MODIS estimates. It can be also used for MODIS/ Terra data, and other missions where the footprint is collocated with CALIPSO.

scholarly journals CHARACTERIZATION OF THE AGRICULTURE OCCUPATION IN THE CERRADO BIOME USING MODIS TIME-SERIES

2013 ◽  
Vol 31 (3) ◽  
pp. 393 ◽  
Author(s):  
Antonio Felipe Couto Junior ◽  
Osmar Abílio de Carvalho Júnior ◽  
Éder De Souza Martins ◽  
Vinícius Vasconcelos
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Cerrado Biome ◽  
Minimum Noise Fraction ◽  
Agriculture Expansion ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Noise Fraction ◽  
Minimum Noise

ABSTRACT.This paper aims to characterize the agriculture expansion in the Cerrado biome using time-series data of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The study area is the municipality of Luís Eduardo Magalhães (Bahia State, Brazil), with recent growth of agribusiness. The methodology can be subdivided into the following steps: 1) noise reduction, 2) endmembers identification, and 3) mixing linear analysis. In the noise reduction was applied the following procedures: 1) moving median filter; 2) Minimum Noise Fraction (MNF) transformation, and 3) Inverse MNF transformation. The results provided a significant noise reduction, besides eliminating the atmospheric interferences. Three endmembers were identified: 1) Natural Vegetation; 2) Agriculture; 3) Change Areas (conversion). We used the linear mixture analysis with the selected endmembers to generate fraction images. These images evidenced the agriculture expansion from west to east. These methods overcame the spatial resolution restrictions and evidenced the potential for discriminating the phenology of growing agricultural crops.Keywords: agriculture expansion, cerrado, time-series, MODIS, change detection. RESUMO. O artigo objetiva caracterizar a expansão agrícola no bioma Cerrado utilizando dados de séries temporais do sensor Moderate Resolution Imaging Spectroradiometer (MODIS). A área de estudo é o município de Luís Eduardo Magalhães (Bahia), com recente crescimento do agronegócio. A metodologia pode ser subdividida nas seguintes etapas: (a) redução do ruído, (b) identificação dos membros finais, e (c) análise linear de mistura. Na redução do ruído foram aplicados os seguintes procedimentos: (a) filtro de mediana, (b) transformação Minimum Noise Fraction (MNF), e (c) transformação inversa MNF. Os resultados proporcionaram uma redução significativa dos ruídos, além da eliminação de interferências atmosféricas. Três membros finais foram identificados: 1) Vegetação Natural; 2) Agricultura; 3) Área de Mudança (Conversão). Foi usada a análise de mistura linear com os membros finais selecionados para gerar as imagens de fração. Estas imagens evidenciaram a expansão agrícola partindo de oeste para leste. Os métodos apresentados proporcionaram a superação da limitação da resolução espacial e evidenciaram um potencial de discriminação da fenologia de cultivos agrícolas.Palavras-chave: expansão agrícola, cerrado, séries temporais, MODIS, detecção de mudança.

scholarly journals Modis time series for irrigated-area mapping in hydrographic basins of the Brazilian Northeastern region

2018 ◽  
Vol 53 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Andre Keiiti Ide ◽  
Gustavo Macedo de Mello Baptista
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Northeastern Region ◽  
Irrigated Area ◽  
Analysis Technique ◽  
Agricultural Census ◽  
Moderate Resolution ◽  
Annual Time Series ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract: The objective of this work was to evaluate the applicability of time series of the enhanced vegetation index (EVI), from the moderate resolution imaging spectroradiometer (Modis), in the mapping of irrigated areas in the Northeastern region of Brazil. Annual time series from 2006 to 2015 were classified with the iterative self-organizing data analysis technique (Isodata) algorithm, generating a binary map of irrigated and nonirrigated areas for each year. In the Sertão region, the classification showed an average kappa coefficient of 0.66, underestimating the irrigated area by 7.6%, compared with data of the 2006 agricultural census. In regions more humid than the Sertão, such as Agreste and Zona da Mata Nordestina, the methodology showed limitations to distinguish irrigated areas from natural vegetation, presenting average kappa coefficients of 0.26 and 0.00, respectively. The EVI time series from Modis are applicable for the mapping of irrigated areas in the Sertão of the Northeastern region of Brazil.

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