Research on Generic Optical Remote Sensing Products: A Review of Scientific Exploration, Technology Research, and Engineering Application
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Junfeng Tian | Xianyu Zuo | Yang Liu | Shenshen Li | Kun Cai | Wanjun Zhang | Junfeng Tian | Shenshen Li | Wanjun Zhang | Xianyu Zuo | K. Cai | Yang Liu
[1] Catherine Champagne,et al. Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies , 2013 .
[2] Peng Yue,et al. A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images , 2020, Int. J. Appl. Earth Obs. Geoinformation.
[3] Zhao-Liang Li,et al. Sensitivity study of soil moisture on the temporal evolution of surface temperature over bare surfaces , 2013 .
[4] M. Claverie,et al. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.
[5] W. Ju,et al. Photochemical reflectance index (PRI) can be used to improve the relationship between gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) , 2020 .
[6] Yunqiang Zhu,et al. Trends and variability in atmospheric precipitable water over the Tibetan Plateau for 2000–2010 , 2015 .
[7] Juan C. Jiménez-Muñoz,et al. A Single-Channel Algorithm for Land-Surface Temperature Retrieval From ASTER Data , 2010, IEEE Geoscience and Remote Sensing Letters.
[8] Bo Yang,et al. A New Model for Surface Soil Moisture Retrieval From CBERS-02B Satellite Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[9] Guangjian Yan,et al. Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover , 2015, Remote. Sens..
[10] Gerhard Krieger,et al. Generation and performance assessment of the global TanDEM-X digital elevation model , 2017 .
[11] Rachel T. Pinker,et al. Retrieval of surface temperature from the MSG‐SEVIRI observations: Part I. Methodology , 2007 .
[12] Roni Avissar,et al. Which type of soil-vegetation-atmosphere transfer scheme is needed for general circulation models: A proposal for a higher-order scheme , 1998 .
[13] Yu Liu,et al. Generating a High-Precision True Digital Orthophoto Map Based on UAV Images , 2018, ISPRS Int. J. Geo Inf..
[14] Didier Tanré,et al. Aerosol Remote Sensing over Clouds Using A-Train Observations , 2009 .
[15] C. Chen,et al. Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters , 2013 .
[16] Leif Toudal Pedersen,et al. Construction of a climate data record of sea surface temperature from passive microwave measurements , 2020 .
[17] Yu Wang,et al. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. , 2020, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[18] C. Merchant,et al. A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Initial validation, accounting for skin and diurnal variability effects , 2012 .
[19] Mizuki Tomita,et al. Evaluating multiple classifier system for the reduction of salt-and-pepper noise in the classification of very-high-resolution satellite images , 2018, International Journal of Remote Sensing.
[20] Chen Chen,et al. Improvement of mono-window algorithm for land surface temperature retrieval integrated with subpixel mapping for Landsat imagery , 2016, 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA).
[21] Delu Pan,et al. A regional remote sensing algorithm for total suspended matter in the East China Sea , 2012 .
[22] B. Markham,et al. Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..
[23] A. Yilmaz,et al. Detection of building damage caused by Van Earthquake using image and Digital Surface Model (DSM) difference , 2018, International Journal of Remote Sensing.
[24] P. Hostert,et al. Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data , 2020 .
[25] Li Yan,et al. Semi-supervised center-based discriminative adversarial learning for cross-domain scene-level land-cover classification of aerial images , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[26] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[27] W. Verhoef. Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .
[28] B. Markham,et al. Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .
[29] Xu Han-qiu,et al. A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI) , 2005, National Remote Sensing Bulletin.
[30] T. Carlson,et al. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .
[31] J. Gerber,et al. Assessing land use/cover dynamics and exploring drivers in the Amazon's arc of deforestation through a hierarchical, multi-scale and multi-temporal classification approach , 2019, Remote Sensing Applications: Society and Environment.
[32] Z. Wan. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product , 2014 .
[33] Zhanqing Li,et al. Improved merge schemes for MODIS Collection 6.1 Dark Target and Deep Blue combined aerosol products , 2019, Atmospheric Environment.
[34] Miquel Ninyerola,et al. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[35] Long Thanh Ngo,et al. Multiple kernel collaborative fuzzy clustering algorithm with weighted super-pixels for satellite image land-cover classification , 2019, Eng. Appl. Artif. Intell..
[36] C. Evangelides,et al. Red-Edge Normalised Difference Vegetation Index (NDVI705) from Sentinel-2 imagery to assess post-fire regeneration , 2020 .
[37] T. Cheng,et al. Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[38] Changchun Huang,et al. Assessment of NIR-red algorithms for observation of chlorophyll-a in highly turbid inland waters in China , 2014 .
[39] Jingfeng Xiao,et al. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA , 2005 .
[40] A. Karnieli,et al. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region , 2001 .
[41] Di Long,et al. An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach , 2020 .
[42] S. Leblanc,et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements , 2002 .
[43] Lingbo Yang,et al. Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets , 2019, Geocarto International.
[44] J. Sobrino,et al. A generalized single‐channel method for retrieving land surface temperature from remote sensing data , 2003 .
[45] Lei Yan,et al. Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries , 2019 .
[46] Ning Wang,et al. Temperature and Emissivity Retrievals From Hyperspectral Thermal Infrared Data Using Linear Spectral Emissivity Constraint , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[47] E. Vermote,et al. Measuring the Directional Variations of Land Surface Reflectance From MODIS , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[48] E. Uhlhorn,et al. Off-Nadir SFMR Brightness Temperature Measurements in High-Wind Conditions , 2018, Journal of Atmospheric and Oceanic Technology.
[49] John A. Gamon,et al. Monitoring drought effects on vegetation water content and fluxes in chaparral with the 970 nm water band index , 2006 .
[50] P. Pahlavani,et al. Generation of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data , 2018, Survey Review.
[51] Keith S. Krause,et al. QuickBird relative radiometric performance and on-orbit long term trending , 2006, SPIE Optics + Photonics.
[52] B. Gao. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .
[53] Zhang Bai,et al. Estimation of Total Suspended Matter Concentration in Shitoukoumen Reservoir Based on a Semi-empirical Model , 2010 .
[54] Didier Tanré,et al. Evaluation of seven European aerosol optical depth retrieval algorithms for climate analysis , 2015 .
[55] R. Houborg,et al. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data , 2007 .
[56] B. Holben,et al. Validation of MODIS aerosol optical depth retrieval over land , 2002 .
[57] Zhiming Zhan,et al. Designing of the perpendicular drought index , 2007 .
[58] Thierry Tormos,et al. Retrieving water surface temperature from archive LANDSAT thermal infrared data: Application of the mono-channel atmospheric correction algorithm over two freshwater reservoirs , 2014, Int. J. Appl. Earth Obs. Geoinformation.
[59] B. Markham,et al. Forty-year calibrated record of earth-reflected radiance from Landsat: A review , 2012 .
[60] Chun Yang,et al. A Kernel Spectral Angle Mapper algorithm for remote sensing image classification , 2013, 2013 6th International Congress on Image and Signal Processing (CISP).
[61] Gang Cheng,et al. Object oriented land cover classification using ALS and GeoEye imagery over mining area , 2011 .
[62] M. S. Moran,et al. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index , 1994 .
[63] Partha Sarathi Roy,et al. Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach , 2008, Int. J. Appl. Earth Obs. Geoinformation.
[64] H. Vijith,et al. Applicability of MODIS land cover and Enhanced Vegetation Index (EVI) for the assessment of spatial and temporal changes in strength of vegetation in tropical rainforest region of Borneo , 2020 .
[65] Diofantos G. Hadjimitsis,et al. Using simple ratio (SR) vegetation index to detect deep man-made infrastructures in Cyprus , 2020, Defense + Commercial Sensing.
[66] Juan C. Jiménez-Muñoz,et al. Land surface temperature retrieval from thermal infrared data: An assessment in the context of the Surface Processes and Ecosystem Changes Through Response Analysis (SPECTRA) mission , 2005 .
[67] Kun-Shan Chen,et al. Retrieval of surface parameters using dynamic learning neural network , 1993, Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium.
[68] Xiaohan Liu,et al. Remote sensing of diffuse attenuation coefficient of photosynthetically active radiation in Lake Taihu using MERIS data , 2014 .
[69] Z. Wan. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products , 2008 .
[70] T. Cui,et al. A three-band semi-analytical model for deriving total suspended sediment concentration from HJ-1A/CCD data in turbid coastal waters , 2014 .
[71] B. Matsushita,et al. A simple and effective method for removing residual reflected skylight in above-water remote sensing reflectance measurements , 2020 .
[72] Didier Tanré,et al. Estimation of Saharan aerosol optical thickness from blurring effects in thematic mapper data , 1988 .
[73] Zhiqiang Wang,et al. Validation plays the role of a “bridge” in connecting remote sensing research and applications , 2018, Advances in Space Research.
[74] Rasmus Fensholt,et al. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery , 2014 .
[75] Hang Zhou,et al. Deep learning based multi-temporal crop classification , 2019, Remote Sensing of Environment.
[76] Yu Zheng,et al. Evaluation of radiosonde, MODIS-NIR-Clear, and AERONET precipitable water vapor using IGS ground-based GPS measurements over China , 2017 .
[77] J. Gong,et al. Vegetation temperature condition index and its application for drought monitoring , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).
[78] Jinsong Tang,et al. Method of phase unwrapping-free DEM reconstruction of InSAS , 2010, 2010 International Conference on Image Analysis and Signal Processing.
[79] Süleyman Savas Durduran,et al. Automatic classification of high resolution land cover using a new data weighting procedure: The combination of k-means clustering algorithm and central tendency measures (KMC-CTM) , 2015, Appl. Soft Comput..
[80] Zhongfeng Qiu,et al. Reconstruction of hyperspectral reflectance for optically complex turbid inland lakes: test of a new scheme and implications for inversion algorithms. , 2015, Optics express.
[81] Zhongfeng Qiu,et al. A hybrid method to estimate suspended particle sizes from satellite measurements over Bohai Sea and Yellow Sea , 2016 .
[82] K. Di,et al. High-Resolution Large-Area Digital Orthophoto Map Generation Using LROC NAC Images , 2019, Photogrammetric Engineering & Remote Sensing.
[83] Jianping Wu,et al. Automatic building rooftop extraction using a digital surface model derived from aerial stereo images , 2020, Journal of Spatial Science.
[84] C. Daughtry,et al. Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes , 2003 .
[85] Shihao Tang,et al. An improved physical split-window algorithm for precipitable water vapor retrieval exploiting the water vapor channel observations , 2017 .
[86] M. Shima,et al. Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan. , 2018, The Science of the total environment.