Quantitative Retrieval of Tidal Flat Moisture Based on TM Images

2013 ◽  
Vol 448-453 ◽  
pp. 1066-1071
Author(s):  
Li Jun Yang ◽  
Ming Fei Wu ◽  
Yun Hong Zhu
Keyword(s):  
Remote Sensing ◽  
Relative Error ◽  
Tidal Flat ◽  
Spectral Characteristics ◽  
Atmospheric Correction ◽  
Maximum Error ◽  
Exponential Model ◽  
Wave Band ◽  
Gradient Distribution ◽  
Distribution Features

Based on spectrometry, the remote sensing inversion researches of the surface tidal flat moisture are conducted in combination with spectral values measured in the field and moisture measured in the laboratory. Firstly, the remote sensing images are preprocessed, including geometric correction, atmospheric correction and image enhancement. Then, the spectral characteristics of typical ground objects are analyzed to partition the whole image and separate the bare tidal flats. At last, TM5 wave band and exponential model are determined to be the best wave band and optimal model for the inversion of the bare tidal flat moisture. The experiment shows: (1)This method can help to improve the accuracy of the surface tidal flat moisture inversion, with the maximum error of moisture inversion is 3%, the relative error is 7.1% and the average relative error is 6.5%. (2)The surface tidal flat moisture is of evident gradient distribution features, which can be used as basis of tidal flat topographic survey.

scholarly journals Estimation of Total Dissolved Solids in Water Bodies by Spectral Indices Case Study: Shatt al-Arab River

2020 ◽  
Vol 231 (9) ◽  
Author(s):  
Ali A. A. Maliki ◽  
Ali Chabuk ◽  
Maitham A. Sultan ◽  
Bassim M. Hashim ◽  
Hussain M. Hussain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Pearson Correlation ◽  
Spectral Characteristics ◽  
Atmospheric Correction ◽  
Total Dissolved Solids ◽  
City Centre ◽  
Agricultural Activity ◽  
Landsat 8 ◽  
Dissolved Solids

Abstract In recent years, the problem of rising salinity levels in the Shatt al-Arab river in southern Iraq has been repeated, which has directly affected the living and health situation and the agricultural activity of these areas. Six sampling stations were selected along Shatt al-Arab to estimate the concentration of total dissolved solids (TDS) in the river; these stations included the following: Qurna, Labani, City Centre, Kateban, Corniche, and Sihan. In addition, three Landsat-8 satellite images which were taken at the same time as collected samples also used for detecting the salinity in the river. After processing of atmospheric correction and inserted remote sensing indices, the reflectance of water extracted from satellite images was used to express the spectral characteristics of different TDS concentrations. Correlation and regression were used to obtain accurate models for detecting the salinity depending on the spectral reflectance of Landsat 8 operational land image OLI. The results presented Pearson correlation (r) value of 0.70, 0.97, and 0.71, and correlation coefficient (R2) of 0.56, 0.94, and 0.85 between field data with spectral data of salinity index 2 (SI-2) derived from the green and blue bands of Landsat obtained in 2015, 2017, and 2018 respectively. In conclusion, remote sensing and GIS technologies coupled with spectral modeling are useful tools for providing a solution of future water resources planning and management, and also offer great undertaking as a means to improve knowledge of water quality and support water decision making.

scholarly journals Correção Atmosférica em Sensoriamento Remoto: Uma Revisão

2020 ◽  
Vol 13 (1) ◽  
pp. 229
Author(s):  
William Gaida ◽  
Fábio Marcelo Breunig ◽  
Lênio Soares Galvão ◽  
Flávio Jorge Ponzoni
Keyword(s):  
Remote Sensing ◽  
Hybrid Methods ◽  
A Priori ◽  
Spectral Characteristics ◽  
Atmospheric Correction ◽  
Atmospheric Effects ◽  
Atmospheric Parameters ◽  
Empirical Methods ◽  
Physical Methods ◽  
Technical Specifications

A correção atmosférica é um procedimento necessário em estudos de sensoriamento remoto com foco nas propriedades físicas, químicas e biológicas de alvos. Em abordagens multitemporais e/ou multisensor, a correção atmosférica é fundamental para a obtenção de medidas de reflectância de superfície representativas das características espectrais dos alvos. Embora de grande importância, este procedimento e seus conceitos ainda são pouco difundidos e estudados no âmbito da Geografia brasileira. O objetivo do estudo foi apresentar uma revisão dos principais métodos e parâmetros envolvidos no processo de correção atmosférica de imagens de sensoriamento remoto. Este procedimento visa minimizar os efeitos decorrentes da interação dos constituintes atmosféricos com a radiação eletromagnética, como a absorção provocada pelos gases e o espalhamento produzido por partículas de aerossóis. Sua eficiência depende de um conjunto de fatores, como o método de correção adotado e as características do sistema sensor. Diversos métodos têm sido propostos, sendo classificados em: métodos físicos, fundamentados em modelos de transferência radiativa; métodos empíricos, que estimam os efeitos atmosféricos a partir de alvos específicos da imagem; e métodos híbridos, que combinam informações físicas da atmosfera e estatísticas da cena. Embora os métodos empíricos sejam de fácil aplicação, eles são menos precisos do que os métodos físicos. Estes, por sua vez, necessitam a priori do conhecimento de alguns parâmetros atmosféricos que, dependendo da resolução espectral do sensor, podem ser obtidos da própria imagem. Os métodos híbridos constituem uma alternativa por demandarem um menor conhecimento de parâmetros atmosféricos. Um exemplo dos efeitos atmosféricos sobre o cálculo de índices de vegetação é apresentado.  Atmospheric Correction in Remote Sensing: A Review A B S T R A C TAtmospheric correction is a necessary procedure in remote sensing studies focusing on the physical, chemical and biological properties of targets. In multi-temporal and/or multi-sensor approaches, the atmospheric correction is fundamental to obtain surface reflectance measurements representative of the spectral characteristics of targets. Although of great importance, this procedure and its concepts are still little diffused and studied in the scope of the Geography in Brazil. The objective of the study was to present an overview of the main methods and parameters involved in the process of atmospheric correction of remote sensing images. This procedure aims to minimize the effects of the interaction of atmospheric constituents with electromagnetic radiation, such as the absorption caused by gases and the scattering produced by aerosol particles. Its efficiency depends on a set of factors such as the selected correction method and the technical specifications of the sensor system. Several methods have been proposed. They are classified in physical methods, based on radiative transfer models; empirical methods, which estimate atmospheric effects from selected targets in the scene; and hybrid methods, which combine physical information from the atmosphere and scene statistics. Although empirical methods are easy to apply, they are less precise than physical methods. Physical approaches require a priori knowledge of some atmospheric parameters. However, depending on the spectral resolution of the sensor, these parameters can be estimated from the image itself. The hybrid methods are an alternative because they require less knowledge of atmospheric parameters. An example of the atmospheric effects on the determination of vegetation indices is presented.Key-words: image processing, atmospheric effects, spectral behavior, radiometric errors.

Classification Method of Muddy Tidal Flat Wetlands Based on Remote Sensing

2013 ◽  
Vol 15 (4) ◽  
pp. 590 ◽  
Author(s):  
Cong WANG ◽  
Hongyu LIU ◽  
Minghang HOU ◽  
Qingmei TAN
Keyword(s):  
Remote Sensing ◽  
Tidal Flat ◽  
Classification Method

scholarly journals Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

2021 ◽  
Vol 13 (2) ◽  
pp. 184
Author(s):  
Rongjie Liu ◽  
Jie Zhang ◽  
Tingwei Cui ◽  
Haocheng Yu
Keyword(s):  
Remote Sensing ◽  
Indian Ocean ◽  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Spatiotemporal Pattern ◽  
Anthropogenic Aerosols ◽  
Remote Sensing Reflectance ◽  
The Indian Ocean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

scholarly journals Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Yahui Guo ◽  
Jing Zeng ◽  
Wenxiang Wu ◽  
Shunqiang Hu ◽  
Guangxu Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Linear Trend ◽  
Spectral Characteristics ◽  
Growth Conditions ◽  
Temporal Changes ◽  
Vegetation Coverage ◽  
Dynamic Changes ◽  
Spatial And Temporal Changes ◽  
Temporal And Spatial

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

Spectral characteristics of leafy spurge (Euphorbia esula) leaves and flower bracts

Weed Science ◽  
2004 ◽  
Vol 52 (4) ◽  
pp. 492-497 ◽  
Author(s):  
E. Raymond Hunt ◽  
James E. McMurtrey ◽  
Amy E. Parker Williams ◽  
Lawrence A. Corp
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Leafy Spurge ◽  
Hyperspectral Remote Sensing ◽  
Total Carotenoids ◽  
Physiological Basis ◽  
Infrared Wavelengths ◽  
Pigment Concentrations

Leafy spurge can be detected during flowering with either aerial photography or hyperspectral remote sensing because of the distinctive yellow-green color of the flower bracts. The spectral characteristics of flower bracts and leaves were compared with pigment concentrations to determine the physiological basis of the remote sensing signature. Compared with leaves of leafy spurge, flower bracts had lower reflectance at blue wavelengths (400 to 500 nm), greater reflectance at green, yellow, and orange wavelengths (525 to 650 nm), and approximately equal reflectances at 680 nm (red) and at near-infrared wavelengths (725 to 850 nm). Pigments from leaves and flower bracts were extracted in dimethyl sulfoxide, and the pigment concentrations were determined spectrophotometrically. Carotenoid pigments were identified using high-performance liquid chromatography. Flower bracts had 84% less chlorophylla, 82% less chlorophyllb, and 44% less total carotenoids than leaves, thus absorptance by the flower bracts should be less and the reflectance should be greater at blue and red wavelengths. The carotenoid to chlorophyll ratio of the flower bracts was approximately 1:1, explaining the hue of the flower bracts but not the value of reflectance. The primary carotenoids were lutein, β-carotene, and β-cryptoxanthin in a 3.7:1.5:1 ratio for flower bracts and in a 4.8:1.3:1 ratio for leaves, respectively. There was 10.2 μg g−1fresh weight of colorless phytofluene present in the flower bracts and none in the leaves. The fluorescence spectrum indicated high blue, red, and far-red emission for leaves compared with flower bracts. Fluorescent emissions from leaves may contribute to the higher apparent leaf reflectance in the blue and red wavelength regions. The spectral characteristics of leafy spurge are important for constructing a well-documented spectral library that could be used with hyperspectral remote sensing.

A New Index for Steel Framed Roof Information Extraction Based on Remote Sensing

2013 ◽  
Vol 726-731 ◽  
pp. 4682-4685 ◽  
Author(s):  
Jie Ying Xiao ◽  
Na Ji ◽  
Xing Li
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Spectral Characteristics ◽  
Threshold Method ◽  
Rapid Urbanization ◽  
Surface Information ◽  
Remote Sensing Technology ◽  
Economic Significance ◽  
Tm Image ◽  
Sensing Technology

There are a great number of index methods used to extract impervious surface from satellite images. However, these indices are not robust enough to detect steel framed roof due to the diversity of impervious materials. The extraction of steel framed roof information by remote sensing technology is becoming increasingly important because of its environmental and socio-economic significance. A new index, Normalized Difference Steel framed roof Index (NDSI) is proposed to extract steel framed roof surface information from TM images. The NDSI was created based on its spectral characteristics of TM image and the steel framed roof information can be extracted fast by NDSI threshold method. Additionally, Shijiazhuang city, which has experienced rapid urbanization, was chosen as the study area. And the classification results show that the new index NDSI can effectively extract steel framed roof information with higher accuracy.

scholarly journals Remote sensing of <i>Trichodesmium</i> spp. mats in the western tropical South Pacific

2018 ◽  
Vol 15 (16) ◽  
pp. 5203-5219 ◽  
Author(s):  
Guillaume Rousset ◽  
Florian De Boissieu ◽  
Christophe E. Menkes ◽  
Jérôme Lefèvre ◽  
Robert Frouin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
New Caledonia ◽  
Hot Spot ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
South Pacific ◽  
Aggregation Processes ◽  
Modis Imagery ◽  
Generate Surface

Abstract. Trichodesmium is the major nitrogen-fixing species in the western tropical South Pacific (WTSP) region, a hot spot of diazotrophy. Due to the paucity of in situ observations, remote-sensing methods for detecting Trichodesmium presence on a large scale have been investigated to assess the regional-to-global impact of this organism on primary production and carbon cycling. A number of algorithms have been developed to identify Trichodesmium surface blooms from space, but determining with confidence their accuracy has been difficult, chiefly because of the scarcity of sea-truth information at the time of satellite overpass. Here, we use a series of new cruises as well as airborne surveys over the WTSP to evaluate their ability to detect Trichodesmium surface blooms in the satellite imagery. The evaluation, performed on MODIS data at 250 m and 1 km resolution acquired over the region, shows limitations due to spatial resolution, clouds, and atmospheric correction. A new satellite-based algorithm is designed to alleviate some of these limitations, by exploiting optimally spectral features in the atmospherically corrected reflectance at 531, 645, 678, 748, and 869 nm. This algorithm outperforms former ones near clouds, limiting false positive detection and allowing regional-scale automation. Compared with observations, 80 % of the detected mats are within a 2 km range, demonstrating the good statistical skill of the new algorithm. Application to MODIS imagery acquired during the February-March 2015 OUTPACE campaign reveals the presence of surface blooms northwest and east of New Caledonia and near 20∘ S–172∘ W in qualitative agreement with measured nitrogen fixation rates. Improving Trichodesmium detection requires measuring ocean color at higher spectral and spatial (<250 m) resolution than MODIS, taking into account environment properties (e.g., wind, sea surface temperature), fluorescence, and spatial structure of filaments, and a better understanding of Trichodesmium dynamics, including aggregation processes to generate surface mats. Such sub-mesoscale aggregation processes for Trichodesmium are yet to be understood.

scholarly journals SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3009 ◽  
Author(s):  
Shanshan Du ◽  
Liangyun Liu ◽  
Xinjie Liu ◽  
Jian Guo ◽  
Jiaochan Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Seasonal Variations ◽  
Photosynthetic Activity ◽  
Gross Primary Production ◽  
Atmospheric Correction ◽  
Gansu Province ◽  
Optical Measurements ◽  
Calibration Data ◽  
Flux Measurements

Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.

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