A5059186978- Remote Sensing in Agriculture
- Remote-Sensing Image Classification
- Advanced Image Fusion Techniques
- Remote Sensing and LiDAR Applications
- Urban Heat Island Mitigation
- Atmospheric and Environmental Gas Dynamics
- Remote Sensing and Land Use
- Land Use and Ecosystem Services
- Atmospheric aerosols and clouds
- Calibration and Measurement Techniques
- Geochemistry and Geologic Mapping
- Atmospheric chemistry and aerosols
- Satellite Image Processing and Photogrammetry
- Climate change and permafrost
- Fire effects on ecosystems
- Solar Radiation and Photovoltaics
- Species Distribution and Climate Change
- Image and Signal Denoising Methods
- Environmental Changes in China
- Leaf Properties and Growth Measurement
- Soil Geostatistics and Mapping
- Image Enhancement Techniques
- Image Processing and 3D Reconstruction
- Plant Water Relations and Carbon Dynamics
- Smart Agriculture and AI
South Dakota State University
2015-2024
Dakota State University
2018
Chinese University of Hong Kong
2011-2014
University of Hong Kong
2013
Zhejiang University
2008-2011
The medium spatial resolution satellite data from the polar-orbiting Sentinel-2A Multi Spectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) sensors provide 10 m to 30 multi-spectral global coverage with a better than 5-day revisit. There are number of differences between sensor that need be considered before can used together reliably. for approximately 10° × southern Africa acquired in two summer (December January) winter (June July) months 2016 were compared. registered...
Data that have been processed to allow analysis with a minimum of additional user effort are often referred as Analysis Ready (ARD). The ability perform large scale Landsat relies on the access observations geometrically and radiometrically consistent, had non-target features (clouds) poor quality flagged so they can be excluded. United States Geological Survey (USGS) has all 4 5 Thematic Mapper (TM), 7 Enhanced Plus (ETM+), 8 Operational Land Imager (OLI) Thermal Infrared Sensor (TIRS)...
Classification is a fundamental process in remote sensing used to relate pixel values land cover classes present on the surface. Over large areas classification challenging particularly due cost and difficulty of collecting representative training data that enable classifiers be consistent locally reliable. A novel methodology classify volume Landsat using high quality derived from 500 m MODIS product demonstrated generate 30 for all North America between 20°N 50°N. Publically available...
The free-availability of global coverage Landsat-8 and Sentinel-2 data provides the opportunity for systematic generation medium spatial resolution land products. This paper presents a combined burned area mapping algorithm. handling integrates recent research on pre-processing to generate registered, surface nadir BRDF-adjusted reflectance (NBAR) sensor time series that are used as an input. different through random forest change regression, trained with synthetic built from laboratory...
The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed across satellite series, United States Geological Survey (USGS) initiated collection-based processing entire that was as Collection 1 in 2016. preparation from successfully launched 9, USGS reprocessed 2 2020. This paper describes rationale for, and contents advancements provided by 2, highlights differences between products. Notably, products have improved geolocation...
The 30 m resolution U.S. Department of Agriculture (USDA) crop data layer (CDL) is a widely used type map for agricultural management and assessment, environmental impact food security. A finer can potentially reduce errors related to area estimation, field size characterization, precision agriculture activities that requires growth information at scales than field. This study develop method mapping using Sentinel-2 10 bands (i.e., red, green, blue, near-infrared) examine the benefit derived...
Biomass burning is a global phenomenon and systematic burned area mapping of increasing importance for science applications. With high spatial resolution novelty in band design, the recently launched Sentinel-2A satellite provides new opportunity moderate mapping. This study examines performance Multi Spectral Instrument (MSI) bands derived spectral indices to differentiate between unburned areas. For this purpose, five pairs pre-fire post-fire top atmosphere (TOA reflectance)...
The Sentinel-2A multi-spectral instrument (MSI) acquires reflective wavelength observations with directional effects due to surface reflectance anisotropy and changes in the solar viewing geometry. Directional were examined by considering two ten day periods of data acquired close principal orthogonal planes over approximately 20° × 10° southern Africa. More than 6.6 million (January 2016) 10.6 (April pairs sensed 3 or 7 days apart forward backscatter directions overlapping orbit swaths...
Moderate spatial resolution satellite data from the Landsat-8 OLI and Sentinel-2A MSI sensors together offer 10 m to 30 multi-spectral reflective wavelength global coverage, providing opportunity for improved combined sensor mapping monitoring of Earth’s surface. However, standard geolocated L1T L1C products are currently found be misaligned. An approach automated registration is presented demonstrated using contemporaneous data. The computationally efficient because it implements feature...
Current satellite remote-sensing systems compromise between spatial resolution and spectral and/or temporal resolution, which potentially limits the use of remotely sensed data in various applications. Image fusion processes, including (SSF) (STF), provide powerful tools for addressing these technological limitations. Although SSF STF have been extensively studied separately, they not yet integrated into a unified framework to generate synthetic images with high spatial, resolution. By...
Over large areas, land cover classification has conventionally been undertaken using satellite time series. Typically temporal metric percentiles derived from single pixel location series have used to take advantage of spectral differences among classes over and minimize the impact missing observations. Deep convolutional neural networks (CNNs) demonstrated potential for date images. However, areas their application is complicated because they are sensitive observations may misclassify small...
For Landsat land cover classification, the time series observations are typically irregular in number of a period (e.g., year) and acquisition dates due to cloud variations over large areas plan long periods. Compositing or temporal percentile calculation usually used transform regular variables so that machine deep learning classifiers can be applied. Recognizing composite calculations have information loss, this study presents method directly Classifying Raw Irregular Time (CRIT) ('raw'...
Data that have been processed to allow analysis with a minimum of additional user effort are often referred as Analysis Ready (ARD). The ability perform large scale Landsat relies on the access observations geometrically and radiometrically consistent, had non-target features (clouds) poor quality flagged so they can be excluded. United States Geological Survey (USGS) has all 4 5 Thematic Mapper (TM), 7 Enhanced Plus (ETM+), 8 Operational Land Imager (OLI) Thermal Infrared Sensor (TIRS)...
Owing to technical limitations the acquisition of fine spatial resolution images (e.g. Landsat data) with frequent daily) coverage remains a challenge. One approach is generate surface reflectances through blending coarse Moderate Resolution Imaging Spectroradiometer, MODIS). Existing implementations for data blending, such as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) Enhanced STARFM (ESTARFM), have their shortcomings, particularly in predicting reflectance...
The standard geolocated Sentinel-2 Multi Spectral Instrument (MSI) L1C data products are defined in spatially overlapping tiles different Universal Transverse Mercator (UTM) map projection zones. Best practices for reprojection and resampling to properly utilize benefit from the format presented. Three sets of 10 m acquired same orbit at latitudes examined illustrate quantify (a) spatial properties provide insights into occurrence UTM zones MSI swath, (b) geometric implications approaches...
Remotely sensed surface parameters, such as vegetation index, leaf area temperature, and evapotranspiration, show diverse spatial scales temporal dynamics. Generally the resolutions of remote-sensing data should match characteristics parameters under observation. These requirements sometimes cannot be provided by a single sensor due to trade-off between resolutions. Many fusion (STF) methods have been proposed derive required data. However, methodology suffers from disorderly development. To...
Surface reflectance can be derived from satellite measurements for the top of atmosphere and provides an important dataset monitoring land change reliably. In this study, Sentinel-2A surface was generated using Sentinel-2 atmospheric correction (Sen2Cor) processor. To evaluate dataset, data at 40 sites aerosol robotic network over North America January 2016 to August 2017 were collected processed. The reference second simulation signal in solar spectrum-vector (6SV) code. optical thickness...
In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure human population density variations. The recent availability of medium resolution Landsat-8 Sentinel-2 satellite data provide the opportunity for optical depth (AOD) estimation at higher spatial than provided by other satellites. AOD retrieved from 30 m 10 Sentinel-2A using Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network...
The Landsat Analysis Ready Data (ARD) are designed to make the U.S. archive straightforward use. In this paper, availability of 4 and 5 Thematic Mapper (TM) 7 Enhanced Plus (ETM+) ARD over conterminous United States (CONUS) quantified for a 36-year period (1 January 1982 31 December 2017). Complex patterns occur due satellite orbit sensor geometry, cloud, acquisition health issues because changing relative orientation tiles with respect paths. Quantitative per-pixel summary tile results...