Satellite Input Research Articles

Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment

Satellite-based PM2.5 estimation has been widely used to assess health impact associated with PM2.5 exposure and might be affected by spatial resolutions of satellite input data, e.g., aerosol optical depth (AOD). Here, based on Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD in 2020 over the Yangtze River Delta (YRD) and three PM2.5 retrieval models, i.e., the mixed effects model (ME), the land-use regression model (LUR) and the Random Forest model (RF), we compare these model performances at different spatial resolutions (1, 3, 5 and 10 km). The PM2.5 estimations are further used to investigate the impact of spatial resolution on health assessment. Our cross-validated results show that the model performance is not sensitive to spatial resolution change for the ME and LUR models. By contrast, the RF model can create a more accurate PM2.5 prediction with a finer AOD spatial resolution. Additionally, we find that annual population-weighted mean (PWM) PM2.5 concentration and attributable mortality strongly depend on spatial resolution, with larger values estimated from coarser resolution. Specifically, compared to PWM PM2.5 at 1 km resolution, the estimation at 10 km resolution increases by 7.8%, 22.9%, and 9.7% for ME, LUR, and RF models, respectively. The corresponding increases in mortality are 7.3%, 18.3%, and 8.4%. Our results also show that PWM PM2.5 at 10 km resolution from the three models fails to meet the national air quality standard, whereas the estimations at 1, 3 and 5 km resolutions generally meet the standard. These findings suggest that satellite-based health assessment should consider the spatial resolution effect.

Daily time series of river water levels derived from a seasonal linear model using multisource satellite products under uncertainty

Satellite altimetry helps to monitor continental water levels with global coverage and near-real time accessibility. However, a main drawback of satellite altimetry is its low acquisition frequency that hinders the use of altimeters in flood forecasting or reservoir operation processes. This study provides a new method to estimate daily river heights from multisource hydro-climate satellite products without using ground-based data. First, the approach develops a simple statistical model, a seasonal linear model, using satellite altimetry, land surface temperature (LST), satellite precipitation and satellite soil moisture products. Second, a Monte Carlo simulation is used to understand the model accuracy by attributing the model output errors to uncertainties from the multiple satellite products. For the first aim, the study shows that using high-frequency satellite data to improve the temporal resolution of the satellite radar altimetry is better than an approach merging multiple satellite altimetry data. With respect to the second objective, it is found that higher variances are found in seasonal transition periods where there are significant changes in satellite observations. The overall error of the model is attributed to both uncertainties of satellite input data and the interactions among their uncertainties. The results demonstrate the capability of using multisource satellite data to predict daily time series of river heights in data-poor areas. The results also highlight the importance of quantifying uncertainty to increase the reliability of flood forecasting models using satellite products.

Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model

The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.

A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation

A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles.

An attempt for development of pavements temperature prediction models based on remote sensing data and artificial neural network

ABSTRACT Results of Falling Weight Deflectometer (FWD) test are dependent upon pavement temperature and its changes daily or seasonally. It is crucial to have the temperature of pavement when FWD data are collected, and also to compare the FWD data for different temperatures. Conventional models for pavement temperature rely on parameters such as air temperature, solar radiation, wind speed, and humidity. However, the accuracy of these data might not be high when the pavement is far from meteorological stations. Thus, this study is investigating the applicability of using remote sensing technology for the estimation of pavement temperature. Satellite input data are used to develop three models as linear, non-linear and Artificial Neural Network (ANN) for depths of 20 and 25 cm and in different layers of asphalt pavement. The predicted temperatures are very close to the measured temperature(correlation coefficients 0.79–0.99). Moreover, the results of this model are compared with the BELLS3 model for Chosen sites in Raton Pass, Aguilar, and Schurz-Nevada, based on the data in IOWA State University in the United States. Nonetheless, the results of this model can be extended for similar conditions with hot weather, subtropical area and low vegetation.

Large-scale crop type and crop area mapping across Brazil using synthetic aperture radar and optical imagery

Improved data on crop type and crop area from satellite imagery are invaluable for agronomy managers and are crucial for balancing agricultural expansion and forest degradation. However, large-scale maps of crop type and crop area using satellite imagery are not easily available in some regions, especially Brazil. Reasons for this include limited ground truth data, inadequate spatial and temporal satellite data availability, computational challenges, lack of cropland data and field boundaries. In this paper, we attempted to overcome some of these obstacles by using an ensemble of approaches to generate crop classification maps for Brazil. In order to compensate for the lack of abundant ground truth data in Brazil, we combined extensive field data and satellite input features from the United States with available field data and satellite input features from Brazil to train crop classification model for Brazil. Before applying the crop classification model for Brazil, we classified cropland areas using harmonic functions and delineated field boundaries using a supervised deep learning approach. Cropland masking and field boundary delineation allowed field-level mapping of crop type and crop area. Applying the crop classification model for Brazil in the states of Mato Grosso and Goias gave a true positive accuracy of 88% in the 2017/2018 summer growing season for soybean classification, 95% in the 2018 safrinha growing season for corn classification, and 86% in the 2018/2019 summer growing season for soybean classification. Our crop area estimates also showed a good agreement (correlation of 0.95 and mean absolute error of 0.64) with state-scale statistical data provided by the Companhia Nacional de Abastecimento (CONAB) in both summer and safrinha growing seasons adding further confidence to the results. These results suggest that extensive data from one geography can be used to train machine learning models in conjunction with limited field data from another geography. Accuracy assessments support the portability of crop classification model for Brazil with reasonable accuracy spatially, as tested in the state of Parana, and temporally, to the following year. The approaches and datasets presented in this paper provide building blocks for large-scale crop monitoring benefitting both public and private sectors.

BESS-STAIR: a framework to estimate daily, 30 m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt

Abstract. With increasing crop water demands and drought threats, mapping and monitoring of cropland evapotranspiration (ET) at high spatial and temporal resolutions become increasingly critical for water management and sustainability. However, estimating ET from satellites for precise water resource management is still challenging due to the limitations in both existing ET models and satellite input data. Specifically, the process of ET is complex and difficult to model, and existing satellite remote-sensing data could not fulfill high resolutions in both space and time. To address the above two issues, this study presents a new high spatiotemporal resolution ET mapping framework, i.e., BESS-STAIR, which integrates a satellite-driven water–carbon–energy coupled biophysical model, BESS (Breathing Earth System Simulator), with a generic and fully automated fusion algorithm, STAIR (SaTallite dAta IntegRation). In this framework, STAIR provides daily 30 m multispectral surface reflectance by fusing Landsat and MODIS satellite data to derive a fine-resolution leaf area index and visible/near-infrared albedo, all of which, along with coarse-resolution meteorological and CO2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET. We applied BESS-STAIR from 2000 through 2017 in six areas across the US Corn Belt and validated BESS-STAIR ET estimations using flux-tower measurements over 12 sites (85 site years). Results showed that BESS-STAIR daily ET achieved an overall R2=0.75, with root mean square error RMSE =0.93 mm d−1 and relative error RE =27.9 % when benchmarked with the flux measurements. In addition, BESS-STAIR ET estimations captured the spatial patterns, seasonal cycles, and interannual dynamics well in different sub-regions. The high performance of the BESS-STAIR framework primarily resulted from (1) the implementation of coupled constraints on water, carbon, and energy in BESS, (2) high-quality daily 30 m data from the STAIR fusion algorithm, and (3) BESS's applicability under all-sky conditions. BESS-STAIR is calibration-free and has great potentials to be a reliable tool for water resource management and precision agriculture applications for the US Corn Belt and even worldwide given the global coverage of its input data.

Dim Prospects for Transmission Spectra of Ocean Earths around M Stars

Abstract The search for water-rich Earth-sized exoplanets around low-mass stars is rapidly gaining attention because they represent the best opportunity to characterize habitable planets in the near future. Understanding the atmospheres of these planets and determining the optimal strategy for characterizing them through transmission spectroscopy with our upcoming instrumentation is essential in order to constrain their environments. For this study, we present simulated transmission spectra of tidally locked Earth-sized ocean-covered planets around late-M to mid-K stellar spectral types, utilizing the results of general circulation models previously published by Kopparapu et al. as inputs for our radiative transfer calculations performed using NASA’s Planetary Spectrum Generator (psg.gsfc.nasa.gov). We identify trends in the depth of H2O spectral features as a function of planet surface temperature and rotation rate. These trends allow us to calculate the exposure times necessary to detect water vapor in the atmospheres of aquaplanets through transmission spectroscopy with the upcoming James Webb Space Telescope as well as several future flagship space telescope concepts under consideration (the Large UV Optical Infrared Surveyor and the Origins Space Telescope) for a target list constructed from the Transiting Exoplanet Survey Satellite (TESS) Input Catalog (TIC). Our calculations reveal that transmission spectra for water-rich Earth-sized planets around low-mass stars will be dominated by clouds, with spectral features <20 ppm, and only a small subset of TIC stars would allow for the characterization of an ocean planet in the habitable zone. We thus present a careful prioritization of targets that are most amenable to follow-up characterizations with next-generation instrumentation, in order to assist the community in efficiently utilizing precious telescope time.

Spatial Information System Architecture for the Geographic Information System

Geographic Information System (GIS) combines software, hardware and data for the catching, handling, analyzing and displaying the different types of geographic information. This information can be in the form of digital maps, images taken by the satellite, aerial pictures, data captured through Global Positioning System (GPS), etc. Satellite imagery or Aircrafts specifically unmanned aircraft systems are used to capture images or geographical information. GIS has been greatly in use in various fields such as in Street Network, Land Information System, Facilities Management, Environment and Natural Resources Management, Planning and Engineering field, etc. GIS is the foundation of the various types of location enabled services. GIS system provides accurate maps or the pictures data captured with the help of various tools and techniques for the analysis of particular situation. In this paper, we are going to study the different aspects of the geographic information system such as land management, roof top analysis, traffic analysis, demographic analysis, watershed analysis, etc. Later we have compared LiDAR, satellite input processed with different feature extracted techniques with our proposed hybrid CNN approach. The result achieved is promising as compared with others

The operational method of filling information gaps in satellite imagery using numerical models

Occurrence of cloud cover over remotely sensed area is a significant limitation in the ocean colour and infra-red remote sensing applications, especially when operational use of such a data is considered. A method for the reconstruction of missing data in remote sensing images has been proposed. It is based on complementing satellite data with the corresponding information from other sources of data, in our tested case it was the ecohydrodynamic model. The method solves the problem the presence of a cloud cover also during an extended period. Unlike in many other similar methods, emphasis has been put on retaining remotely sensed information to a high degree and preserving local phenomena that are usually difficult to capture by other methods than satellite remote sensing. The method has been tested on the Baltic Sea. Sea surface temperature and chlorophyll a concentration estimated from satellite data, ecohydrodynamic models and merged product were compared with in situ data. The algorithm was optimized for the two parameters that are crucial for e.g. creating algae bloom forecasts. The root mean square error (RMSE) of the final product of sea surface temperature was 0.73 °C, whereas of the input satellite images 1.26 °C or 1.33 °C and of model maps 0.89 °C. The error factor of chlorophyll a concentration product was 1.8 mg m−3, in comparison to 2.55 mg m−3 for satellite input source and 2.28 mg m−3 for the model one. The results show that the proposed method well utilizes advantages of both satellite and numerical simulation data sources, at the same time reducing the errors of estimation of merged parameters compared to similar errors for both primary sources. It would be a valuable component of fuzzy logic and rule-based HABs prediction.

Processing Image to Geographical Information Systems (PI2GIS)—A Learning Tool for QGIS

Education, together with science and technology, is the main driver of the progress and transformations of a country. The use of new technologies of learning can be applied to the classroom. Computer learning supports meaningful and long-term learning. Therefore, in the era of digital society and environmental issues, a relevant role is provided by open source software and free data that promote universality of knowledge. Earth observation (EO) data and remote sensing technologies are increasingly used to address the sustainable development goals. An important step for a full exploitation of this technology is to guarantee open software supporting a more universal use. The development of image processing plugins, which are able to be incorporated in Geographical Information System (GIS) software, is one of the strategies used on that front. The necessity of an intuitive and simple application, which allows the students to learn remote sensing, leads us to develop a GIS open source tool, which is integrated in an open source GIS software (QGIS), in order to automatically process and classify remote sensing images from a set of satellite input data. The application was tested in Vila Nova de Gaia municipality (Porto, Portugal) and Aveiro district (Portugal) considering Landsat 8 Operational Land Imager (OLI) data.

A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean

Phytoplankton are composed of diverse taxonomical groups, which are manifested as distinct morphology, size and pigment composition. These characteristics, modulated by their physiological state, impact their light absorption and scattering, allowing them to be detected with ocean color satellite radiometry. There is a growing volume of literature describing satellite algorithms to retrieve information on phytoplankton composition in the ocean. This synthesis provides a review of current methods and a simplified comparison of approaches. The aim is to provide an easily comprehensible resource for non-algorithm developers, who desire to use these products, thereby raising the level of awareness and use of these products and reducing the boundary of expert knowledge needed to make a pragmatic selection of output products with confidence. The satellite input and output products, their associated validation metrics, as well as assumptions, strengths and limitations of the various algorithm types are described, providing a framework for algorithm organization to assist users and inspire new aspects of algorithm development capable of exploiting the higher spectral, spatial and temporal resolutions from the next generation of ocean color satellites.

A Lagrangian View of Moisture Dynamics During DYNAMO.

Column water vapor (CWV) is studied using data from the Dynamics of the Madden-Julian Oscillation (DYNAMO) field experiment. A distinctive moist mode in tropical CVW probability distributions motivates the work. The Lagrangian CWV tendency (LCT) leaves together the compensating tendencies from phase change and vertical advection, quantities which cannot be measured accurately by themselves, to emphasize their small residual, which governs evolution. The slope of LCT vs. CWV suggests that the combined effects of phase changes and vertical advection act as a robust positive feedback on CWV variations, while evaporation adds a broadscale positive tendency. Analyzed diabatic heating profiles become deeper and stronger as CWV increases. Stratiform heating is found to accompany Lagrangian drying at high CWV, but its association with deep convection makes the mean LCT positive at high CWV. Lower-tropospheric wind convergence is found in high-CVW airmasses, acting to shrink their area in time. When ECMWF heating profile indices and S-POL and TRMM radar data are binned jointly by CWV and LCT, bottom-heavy heating associated with shallow and congestus convection is found in columns transitioning through Lagrangian moistening into the humid, high-rainrate mode of the CWV distribution near 50-55mm, while non-raining columns and columns with widespread stratiform precipitation are preferentially associated with Lagrangian drying. Interpolated sounding-array data produce substantial errors in LCT budgets, because horizontal advection is inaccurate without satellite input to constrain horizontal gradients.

Resolution Enhancement of the SatelliteImage Processing using DT CWT Techniques

An image is defined as an array, or a matrix, of square pixels (elements of picture) arranged in rows and columns. Image processing is a procedure of converting an image into digital form and carry out some operation on it, in order to get an improved image and take out several helpful information from it. Actually Signal or image processing enhances certain features of the data while suppressing others. For instance, in analysing a fingerprint image against a textured background it may be important to enhance the fingerprint to identify its owner. The appropriate processing would need to focus on features such as the overall texture pattern to be suppressed and the fingerprint's parallel, smoothly curving lines to be enhanced. Typical Image & Signal Processing Applications are used for Computer vision, Face detection ,Feature detection, extraction, and analysis ,Medical image processing, Remote sensing, Speech recognition, Speech synthesis, Speech compression, Audio, noise suppression, Automated map analysis. In this paper Resolution enhancement (RE) schemes (which are not based on wavelets) suffer from the drawback of losing highfrequency contents (which results in blurring). The discrete- wavelet-transform-based (DWT) RE scheme generates artifacts (due to a DWT shift-variant property). A wavelet-domain approach based on dual-tree complex wavelet transform (DT-CWT) and nonlocal means (NLM) is proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly shift invariant) to obtain high-frequency subbands. The high-frequency subbands and the low-resolution (LR) input image are interpolated using the Lanczos interpolator. The high- frequency subbands are passed through an NLM filter to cater for the artifacts generated by DT-CWT (despite of its nearly shift invariance). The filtered high-frequency subbands and the LR input image are combined using inverse DT-CWT to obtain a resolution-enhanced image. Objective and subjective analyses reveal superiority of the proposed technique over the conventional and state-of-the-art RE techniques.

Satellite Inference of Thermals and Cloud-Base Updraft Speeds Based on Retrieved Surface and Cloud-Base Temperatures

Abstract Updraft speeds of thermals have always been difficult to measure, despite the significant role they play in transporting pollutants and in cloud formation and precipitation. In this study, updraft speeds in buoyancy-driven planetary boundary layers (PBLs) measured by Doppler lidar are found to be correlated with properties of the PBL and surface over the Southern Great Plains (SGP) site operated by the U.S. Department of Energy’s Atmospheric Radiation Measurement Program (ARM). Based on the relationships found here, two approaches are proposed to estimate both maximum (Wmax) and cloud-base (Wcb) updraft speeds using satellite data together with some ancillary meteorological data of PBL depth, wind speed at 10-m height, and air temperature at 2-m height. The required satellite input data are cloud-base and surface skin temperatures. PBL depth can be determined by using cloud-base temperature in combination with European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis data. Validation against lidar-measured updraft speeds demonstrated the feasibility of retrieving Wmax and Wcb using high-resolution Suomi–National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi-NPP VIIRS) measurements over land for PBLs with thermally driven convective clouds during the satellite overpass time. The root-mean-square errors (RMSE) of Wmax and Wcb are 0.32 and 0.42 m s−1, respectively. This method does not work for a stable or a mechanically driven PBL.

Satellite Image Enhancement Using Framelet Transform And Non-Local Means Filter

Resolution enhancement (RE) schemes suffer from the drawback of losing high frequency contents (which results in blurring). The wavelet-transform-based RE scheme, generates artifacts (due to a shift-variant property). Therefore a framelet-domain approach and non-local means (NLM) filter is proposed for RE of the satellite images. A satellite input image is decomposed by Framelet transform ( FT) to obtain high-frequency subbands. The high-frequency subbands and the low-resolution (LR) input image are interpolated using the Lanczos interpolator. The high frequency subbands are passed through an NLM (despite of its nearly shift invariance). The filtered high-frequency subbands and the LR input image are combined using inverse FT to obtain a resolution-enhanced image. Objective and subjective analyses reveal superiority of the proposed technique over the RE techniques.

Satellite Image Resolution Enhancement Using Dual-Tree Complex Wavelet Transform and Nonlocal Means

Resolution enhancement (RE) schemes (which are not based on wavelets) suffer from the drawback of losing high-frequency contents (which results in blurring). The discrete-wavelet-transform-based (DWT) RE scheme generates artifacts (due to a DWT shift-variant property). A wavelet-domain approach based on dual-tree complex wavelet transform (DT-CWT) and nonlocal means (NLM) is proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly shift invariant) to obtain high-frequency subbands. The high-frequency subbands and the low-resolution (LR) input image are interpolated using the Lanczos interpolator. The high-frequency subbands are passed through an NLM filter to cater for the artifacts generated by DT-CWT (despite of its nearly shift invariance). The filtered high-frequency subbands and the LR input image are combined using inverse DT-CWT to obtain a resolution-enhanced image. Objective and subjective analyses reveal superiority of the proposed technique over the conventional and state-of-the-art RE techniques.

Impacts of reference time series on the homogenization of radiosonde temperature

Using radiosonde temperatures of 92 selected stations in China, the uncertainties in homogenization processes caused by different reference series, including nighttime temperature, the NCEP (National Centers for Environmental Prediction) and ERA-40 (European Centre for Medium-Range Weather Forecasts) forecasting background, are examined via a two-phase regression approach. Although the results showed limited consistency in the temporal and spatial distribution of identified break points (BPs) in the context of metadata events of instrument model change and correction method, significant uncertainties still existed in BP identification, adjustment, and impact on the estimated trend. Reanalysis reference series generally led to more BP identification in homogenization. However, those differences were parts of global climatic shifts, which may have confused the BP calculations. Discontinuities also existed in the reanalysis series due to changes in the satellite input. The adjustment values deduced from the reanalysis series ranged widely and were larger than those from the nighttime series and, therefore, impacted the estimated temperature trend.

The application of simulated NPP data in improving the assessment of the spatial distribution of biomass in Europe

Efficient integration of increasing shares of renewable resources into electricity supply requires information about spatial and temporal variations in the availability of energy carriers. Regarding biomass, the spatial variation is crucial for the estimation of the regional potential and costs of biomass for electricity generation. Described in this paper is the examination of the applicability of net primary productivity data for improving the quality of a high resolution inventory of potentials of biomass energy. The net primary productivity is simulated with remotely sensed input data taking into account site-specific information such as precipitation, irradiation and temperature. For the examples of forest wood and surplus straw, two different methods of inventorying were applied and compared: 1) disaggregating biomass potentials derived from statistical crop data using a land cover map and 2) additionally weighting the distribution with the net primary productivity. We found that in the case of surplus straw, this method yielded better results than only disaggregating the potential with the land cover data. No clear conclusions could be drawn from the statistical test applied regarding forest wood, because the sample size of reference values was too small. Based on a critical reflection of the data which is input to the NPP simulation, it was concluded that the distribution of forest wood potentials should not be weighted with the NPP data unless more appropriate satellite input data is available.

Novel superconductive self-equalized planar filter configuration for C-band input multiplexers

The objective of this paper is to present the synthesis, the design, and the measured results of an eight-pole self-equalized quasi-elliptic superconductive planar filter for a satellite input multiplexer. These filters placed at the entry of the amplifying channels allow to select a part of the total band. They are narrow-band filters (<<1.7%) around 4 GHz with a high selectivity in order to optimize the exploitation useful band. A new design is developed using cross-loop resonators and a square patch, to annihilate the undesired couplings between the various elements. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.