23.1.6 Remote Sensing Issues, Evaluations of Techniques, Validation

Chapter Contents (Back)
Remote Sensing. Evaluation, Remote Sensing.
See also Land Surface Albedo.

ISPRS Benchmarks,
Online2021
WWW Link. Dataset, Urban Data. Dataset, Building Detection. Dataset, Object Detection. Dataset, Point Cloud Segmentation. Multiple datasets. Some with associated benchmarks and challenges. Includes: VAihingen/Enz, Toronto, Potsdam, UAVid, Gaofen, EuroSDR, Urban classification.
See also ISPRS: International Society for Photogrammetry and Remote Sensing. BibRef 2100

Defries, R.S., Chan, J.C.W.[Jonathan Cheung-Wai],
Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data,
RSE(74), No. 3, 2000, pp. 503-515. 0102
BibRef

Özkan, C.[Coskun], Erbek, F.S.[Filiz Sunar],
A Comparison of Activation Functions for Multispectral Landsat TM Image Classification,
PhEngRS(69), No. 11, November 2003, pp. 1225-1234.
WWW Link. 0401
Compare linear, sigmoid, and tangent hyperbolic activation functions through the one- and two-hidden layered MLP neural network structures trained with the scaled conjugate gradient learning algorithm, and evaluate their perfornances for a multispectral Landsat TM imagery hard classification problem. BibRef

Makido, Y.[Yasuyo], Shortridge, A.[Ashton], Messina, J.P.[Joseph P.],
Assessing Alternatives for Modeling the Spatial Distribution of Multiple Land-cover Classes at Sub-pixel Scales,
PhEngRS(73), No. 8, August 2007, pp. 935-944.
WWW Link. 0709
Evaluating three methods for modeling the spatial distribution of multiple land cover classes at sub-pixel scales. BibRef

Yang, P., Shibasaki, R., Wu, W., Zhou, Q., Chen, Z., Zha, Y., Shi, Y., Tang, H.,
Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images,
GeoRS(45), No. 10, October 2007, pp. 3087-3097.
IEEE DOI 0711
BibRef

Chen, D.M.[Dong-Mei],
A Standardized Probability Comparison Approach for Evaluating and Combining Pixel-based Classification Procedures,
PhEngRS(74), No. 5, May 2008, pp. 601-610.
WWW Link. 0804
An objective approach to evaluate pixel labeling confidence in a classification and to combine classified maps generated from different classification procedures. BibRef

Aitkenhead, M.J., Flaherty, S., Cutler, M.E.J.,
Evaluating Neural Networks and Evidence Pooling for Land Cover Mapping,
PhEngRS(74), No. 8, August 2008, pp. 1019-1032.
WWW Link. 0804
Integrating evidence from a range of data sources was to produce land cover mapping based on neural networks trained to identify specific land cover classes. BibRef

Lowry, Jr., J.H.[John H.], Ramsey, R.D.[R. Douglas], Stoner, L.L.[Lisa Langs], Kirby, J.[Jessica], Schulz, K.[Keith],
An Ecological Framework for Evaluating Map Errors Using Fuzzy Sets,
PhEngRS(74), No. 12, December 2008, pp. 1509-1520.
WWW Link. 0804
Using an ecological context to define varying levels of landcover class similarity, a decision framework guides map experts' decisions and provides a more meaningful assessment of map errors using fuzzy sets. BibRef

Balaguer, A., Ruiz, L.A., Hermosilla, T., Recio, J.A.,
Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification,
CompGeoSci(36), No. 2, February 2010, pp. 231-240.
Elsevier DOI Remote sensing, Feature extraction, Texture descriptors, Image classification 1204
BibRef

Pérez-Hoyos, A., García-Haro, F.J., San-Miguel-Ayanz, J.,
Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe,
PandRS(74), No. 1, November 2012, pp. 185-201.
Elsevier DOI 1212
GlobCover; Fuzzy comparison; LCCS; CORINE; GLC2000; MODISLC BibRef

Shao, Y.[Yang], Lunetta, R.S.[Ross S.],
Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points,
PandRS(70), No. 1, June 2012, pp. 78-87.
Elsevier DOI 1206
Land-cover mapping; Support vector machine; Accuracy assessment BibRef

Li, P.[Peng], Jiang, L.G.[Lu-Guang], Feng, Z.M.[Zhi-Ming],
Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors,
RS(6), No. 1, 2013, pp. 310-329.
DOI Link 1402
BibRef

Balaguer-Besser, A., Hermosilla, T., Recio, J.A., Ruiz, L.A.,
Semivariogram calculation optimization for object-oriented image classification,
Other JournalModelling in Science Education and Learning(4), No. 7, 2011, pp. 91-104.
PDF File. 1204
BibRef

Murray-Tortarolo, G.[Guillermo], Anav, A.[Alessandro], Friedlingstein, P.[Pierre], Sitch, S.[Stephen], Piao, S.L.[Shi-Long], Zhu, Z.C.[Zai-Chun], Poulter, B.[Benjamin], Zaehle, S.[Soenke], Ahlström, A.[Anders], Lomas, M.[Mark], Levis, S.[Sam], Viovy, N.[Nicholas], Zeng, N.[Ning],
Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs,
RS(5), No. 10, 2013, pp. 4819-4838.
DOI Link 1311
BibRef
And: A2, A1, A3, A4, A5, A6, Only:
Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models,
RS(5), No. 8, 2013, pp. 3637-3661.
DOI Link 1309
BibRef

Ahmed, B.[Bayes], Ahmed, R.[Raquib], Zhu, X.[Xuan],
Evaluation of Model Validation Techniques in Land Cover Dynamics,
IJGI(2), No. 3, 2013, pp. 577-597.
DOI Link 1307
BibRef

Löw, F.[Fabian], Duveiller, G.[Grégory],
Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing,
RS(6), No. 9, 2014, pp. 9034-9063.
DOI Link 1410
BibRef

Glanz, H.[Hunter], Carvalho, L.[Luis], Sulla-Menashe, D.[Damien], Friedl, M.A.[Mark A.],
A parametric model for classifying land cover and evaluating training data based on multi-temporal remote sensing data,
PandRS(97), No. 1, 2014, pp. 219-228.
Elsevier DOI 1410
Maximum likelihood estimation BibRef

Li, Y.Z.[Yi-Zhan], Zhu, X.F.[Xiu-Fang], Pan, Y.Z.[Yao-Zhong], Gu, J.Y.[Jian-Yu], Zhao, A.Z.[An-Zhou], Liu, X.F.[Xian-Feng],
A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery,
RS(6), No. 9, 2014, pp. 8904-8922.
DOI Link 1410
BibRef

Mellor, A.[Andrew], Boukir, S.[Samia], Haywood, A.[Andrew], Jones, S.[Simon],
Exploring Issues of Training Data Imbalance and Mislabelling on Random Forest Performance for Large Area Land Cover Classification Using the Ensemble Margin,
PandRS(105), No. 1, 2015, pp. 155-168.
Elsevier DOI 1506
BibRef
Earlier:
Using ensemble margin to explore issues of training data imbalance and mislabeling on large area land cover classification,
ICIP14(5067-5071)
IEEE DOI 1502
Ensemble margin Accuracy
See also Fast Data Selection for SVM Training Using Ensemble Margin. BibRef

Mellor, A.[Andrew], Boukir, S.[Samia],
Exploring diversity in ensemble classification: Applications in large area land cover mapping,
PandRS(129), No. 1, 2017, pp. 151-161.
Elsevier DOI 1706
Diversity BibRef

Piles, M., McColl, K.A., Entekhabi, D., Das, N., Pablos, M.,
Sensitivity of Aquarius Active and Passive Measurements Temporal Covariability to Land Surface Characteristics,
GeoRS(53), No. 8, August 2015, pp. 4700-4711.
IEEE DOI 1506
Land surface BibRef

Shi, W.Z.[Wen-Zhong], Zhang, X.K.[Xiao-Kang], Hao, M.[Ming], Shao, P.[Pan], Cai, L.P.[Li-Ping], Lyu, X.Z.[Xu-Zhe],
Validation of Land Cover Products Using Reliability Evaluation Methods,
RS(7), No. 6, 2015, pp. 7846.
DOI Link 1507
BibRef

Guanter, L.[Luis], Kaufmann, H.[Hermann], Segl, K.[Karl], Foerster, S.[Saskia], Rogass, C.[Christian], Chabrillat, S.[Sabine], Kuester, T.[Theres], Hollstein, A.[André], Rossner, G.[Godela], Chlebek, C.[Christian], Straif, C.[Christoph], Fischer, S.[Sebastian], Schrader, S.[Stefanie], Storch, T.[Tobias], Heiden, U.[Uta], Mueller, A.[Andreas], Bachmann, M.[Martin], Mühle, H.[Helmut], Müller, R.[Rupert], Habermeyer, M.[Martin], Ohndorf, A.[Andreas], Hill, J.[Joachim], Buddenbaum, H.[Henning], Hostert, P.[Patrick], van der Linden, S.[Sebastian], Leitão, P.J.[Pedro J.], Rabe, A.[Andreas], Doerffer, R.[Roland], Krasemann, H.[Hajo], Xi, H.Y.[Hong-Yan], Mauser, W.[Wolfram], Hank, T.[Tobias], Locherer, M.[Matthias], Rast, M.[Michael], Staenz, K.[Karl], Sang, B.[Bernhard],
The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation,
RS(7), No. 7, 2015, pp. 8830.
DOI Link 1506
Award, Remote Sensing, Third. BibRef

Sun, L.[Liya], Schulz, K.[Karsten],
The Improvement of Land Cover Classification by Thermal Remote Sensing,
RS(7), No. 7, 2015, pp. 8368-8390.
DOI Link 1506
BibRef
And: Response to comments: RS(7), No. 10, 2015, pp. 13440.
DOI Link 1511

See also Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on 'The Improvement of Land Cover Classification by Thermal Remote Sensing'.
See also Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing, An.
See also We Must all Pay More Attention to Rigor in Accuracy Assessment: Additional Comment to The Improvement of Land Cover Classification by Thermal Remote Sensing. Remote Sens. 2015, 7, 8368-8390. BibRef

Johnson, B.A.[Brian A.],
Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on 'The Improvement of Land Cover Classification by Thermal Remote Sensing',
RS(7), No. 10, 2015, pp. 13436.
DOI Link 1511
Original paper and response.
See also Improvement of Land Cover Classification by Thermal Remote Sensing, The. BibRef

Ma, Y.[Yong], Chen, F.[Fu], Liu, J.B.[Jian-Bo], He, Y.[Yang], Duan, J.B.[Jian-Bo], Li, X.P.[Xin-Peng],
An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing,
RS(8), No. 4, 2016, pp. 272.
DOI Link 1604

See also Improvement of Land Cover Classification by Thermal Remote Sensing, The. BibRef

Castilla, G.[Guillermo],
We Must all Pay More Attention to Rigor in Accuracy Assessment: Additional Comment to 'The Improvement of Land Cover Classification by Thermal Remote Sensing'. Remote Sens. 2015, 7, 8368-8390,
RS(8), No. 4, 2016, pp. 288.
DOI Link 1604

See also Improvement of Land Cover Classification by Thermal Remote Sensing, The. BibRef

Aasen, H.[Helge], Burkart, A.[Andreas], Bolten, A.[Andreas], Bareth, G.[Georg],
Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance,
PandRS(108), No. 1, 2015, pp. 245-259.
Elsevier DOI 1511
Hyperspectral digital surface model BibRef

Aasen, H.[Helge], Bendig, J., Bolten, A.[Andreas], Bennertz, S., Willkomm, M., Bareth, G.[Georg],
Introduction and preliminary results of a calibration for full-frame hyperspectral cameras to monitor agricultural crops with UAVs,
Thematic14(1-8).
DOI Link 1404
BibRef

Mesas-Carrascosa, F.J.[Francisco-Javier], Torres-Sánchez, J.[Jorge], Clavero-Rumbao, I.[Inmaculada], García-Ferrer, A.[Alfonso], Peña, J.M.[Jose-Manuel], Borra-Serrano, I.[Irene], López-Granados, F.[Francisca],
Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management,
RS(7), No. 10, 2015, pp. 12793.
DOI Link 1511
BibRef

Verrelst, J.[Jochem], Camps-Valls, G.[Gustau], Muñoz-Marí, J.[Jordi], Rivera, J.P.[Juan Pablo], Veroustraete, F.[Frank], Clevers, J.G.P.W.[Jan G.P.W.], Moreno, J.[José],
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties: A review,
PandRS(108), No. 1, 2015, pp. 273-290.
Elsevier DOI 1511
Bio-geophysical variables BibRef

She, X.J.[Xiao-Jun], Zhang, L.[Lifu], Cen, Y.[Yi], Wu, T.[Taixia], Huang, C.P.[Chang-Ping], Baig, M.H.A.[Muhammad Hasan Ali],
Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types,
RS(7), No. 10, 2015, pp. 13485.
DOI Link 1511
BibRef

Abade, N.A.[Natanael Antunes], de Carvalho Júnior, O.A.[Osmar Abílio], Guimarães, R.F.[Renato Fontes], de Oliveira, S.N.[Sandro Nunes],
Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary,
RS(7), No. 9, 2015, pp. 12160.
DOI Link 1511
BibRef

Bontemps, S.[Sophie], Arias, M.[Marcela], Cara, C.[Cosmin], Dedieu, G.[Gérard], Guzzonato, E.[Eric], Hagolle, O.[Olivier], Inglada, J.[Jordi], Matton, N.[Nicolas], Morin, D.[David], Popescu, R.[Ramona], Rabaute, T.[Thierry], Savinaud, M.[Mickael], Sepulcre, G.[Guadalupe], Valero, S.[Silvia], Ahmad, I.[Ijaz], Bégué, A.[Agnès], Wu, B.F.[Bing-Fang], de Abelleyra, D.[Diego], Diarra, A.[Alhousseine], Dupuy, S.[Stéphane], French, A.[Andrew], ul Hassan Akhtar, I.[Ibrar], Kussul, N.[Nataliia], Lebourgeois, V.[Valentine], Le Page, M.[Michel], Newby, T.[Terrence], Savin, I.[Igor], Verón, S.R.[Santiago R.], Koetz, B.[Benjamin], Defourny, P.[Pierre],
Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2,
RS(7), No. 12, 2015, pp. 15815.
DOI Link 1601
BibRef

Costa, H.[Hugo], Foody, G.M.[Giles M.], Jiménez, S.[Sílvia], Silva, L.[Luís],
Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data,
IJGI(4), No. 4, 2015, pp. 2496.
DOI Link 1601
BibRef

Griffith, D.A.[Daniel A.], Chun, Y.W.[Yong-Wan],
Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data,
RS(8), No. 7, 2016, pp. 535.
DOI Link 1608
BibRef

Schima, R.[Robert], Mollenhauer, H.[Hannes], Grenzdörffer, G.J.[Görres J.], Merbach, I.[Ines], Lausch, A.[Angela], Dietrich, P.[Peter], Bumberger, J.[Jan],
Imagine All the Plants: Evaluation of a Light-Field Camera for On-Site Crop Growth Monitoring,
RS(8), No. 10, 2016, pp. 823.
DOI Link 1609
BibRef

Yang, Y.[Yongke], Xiao, P.F.[Peng-Feng], Feng, X.Z.[Xue-Zhi], Li, H.X.[Hai-Xing],
Accuracy assessment of seven global land cover datasets over China,
PandRS(125), No. 1, 2017, pp. 156-173.
Elsevier DOI 1703
Global land cover dataset BibRef

Xia, G.S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., Lu, X.,
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification,
GeoRS(55), No. 7, July 2017, pp. 3965-3981.
IEEE DOI 1706
Benchmark testing, Earth, Google, Performance evaluation, Remote sensing, Rivers, Semantics, Aerial images, benchmark, scene, classification BibRef

Kharbouche, S.[Said], Muller, J.P.[Jan-Peter], Gatebe, C.K.[Charles K.], Scanlon, T.[Tracy], Banks, A.C.[Andrew C.],
Assessment of Satellite-Derived Surface Reflectances by NASA's CAR Airborne Radiometer over Railroad Valley Playa,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Radoux, J.[Julien], Bogaert, P.[Patrick],
Good Practices for Object-Based Accuracy Assessment,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Cheng, G., Han, J., Lu, X.,
Remote Sensing Image Scene Classification: Benchmark and State of the Art,
PIEEE(105), No. 10, October 2017, pp. 1865-1883.
IEEE DOI 1710
data sets, data-driven algorithms, image diversity, image numbers, image variations, learning based methods, BibRef

Phiri, D.[Darius], Morgenroth, J.[Justin],
Developments in Landsat Land Cover Classification Methods: A Review,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Sun, P.J.[Pei-Jun], Congalton, R.G.[Russell G.], Grybas, H.[Heather], Pan, Y.Z.[Yao-Zhong],
The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Li, J.[Jian], Roy, D.P.[David P.],
A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Isidro, C.M.[Celso M.], McIntyre, N.[Neil], Lechner, A.M.[Alex M.], Callow, I.[Ian],
Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Zou, X.C.[Xiao-Chen], Mõttus, M.[Matti],
Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Wang, S.H.[Si-Heng], Yang, D.[Dong], Li, Z.[Zhen], Liu, L.Y.[Liang-Yun], Huang, C.P.[Chang-Ping], Zhang, L.[Lifu],
A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Gao, J.[Jing], Burt, J.E.[James E.],
Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing,
PandRS(134), No. Supplement C, 2017, pp. 110-121.
Elsevier DOI 1712
Model evaluation, Accuracy assessment, Bias-variance decomposition, Absolute error, Squared error BibRef

Rajbhandari, S.[Sachit], Aryal, J.[Jagannath], Osborn, J.[Jon], Musk, R.[Rob], Lucieer, A.[Arko],
Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis,
IJGI(6), No. 12, 2017, pp. xx-yy.
DOI Link 1801
BibRef

Gallo, K.[Kevin], Stensaas, G.[Greg], Dwyer, J.[John], Longhenry, R.[Ryan],
A Land Product Characterization System for Comparative Analysis of Satellite Data and Products,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Berger, K.[Katja], Atzberger, C.[Clement], Danner, M.[Martin], d'Urso, G.[Guido], Mauser, W.[Wolfram], Vuolo, F.[Francesco], Hank, T.[Tobias],
Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Danner, M.[Martin], Berger, K.[Katja], Wocher, M.[Matthias], Mauser, W.[Wolfram], Hank, T.[Tobias],
Efficient RTM-based training of machine learning regression algorithms to quantify biophysical and biochemical traits of agricultural crops,
PandRS(173), 2021, pp. 278-296.
Elsevier DOI 2102
Reflectance modelling, Hyperspectral remote sensing, Radiative transfer model, SPARC, Grid search, Machine learning BibRef

Goldblatt, R.[Ran], Ballesteros, A.R.[Alexis Rivera], Burney, J.[Jennifer],
High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Salk, C.[Carl], Fritz, S.[Steffen], See, L.[Linda], Dresel, C.[Christopher], McCallum, I.[Ian],
An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Ye, S.[Su], Pontius, R.G.[Robert Gilmore], Rakshit, R.[Rahul],
A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches,
PandRS(141), 2018, pp. 137-147.
Elsevier DOI 1806
Accuracy assessment, Object-based image analysis, OBIA, Remote sensing, Per-pixel, Per-polygon BibRef

Sukhova, E.[Ekaterina], Sukhov, V.[Vladimir],
Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Mõisja, K.[Kiira], Uuemaa, E.[Evelyn], Oja, T.[Tõnu],
The Implications of Field Worker Characteristics and Landscape Heterogeneity for Classification Correctness and the Completeness of Topographical Mapping,
IJGI(7), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Colin, B.[Brigitte], Schmidt, M.[Michael], Clifford, S.[Samuel], Woodley, A.[Alan], Mengersen, K.[Kerrie],
Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Deng, L.[Lei], Yan, Y.[Yanan], Gong, H.L.[Hui-Li], Duan, F.Z.[Fu-Zhou], Zhong, R.F.[Ruo-Fei],
The effect of spatial resolution on radiometric and geometric performances of a UAV-mounted hyperspectral 2D imager,
PandRS(144), 2018, pp. 298-314.
Elsevier DOI 1809
Hyperspectral imaging, High spatial resolution, Unmanned aerial vehicles (UAVs), Radiometry, Geometric performance BibRef

Verde, N.[Natalia], Mallinis, G.[Giorgos], Tsakiri-Strati, M.[Maria], Georgiadis, C.[Charalampos], Patias, P.[Petros],
Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
Higher resolution not necessarily reduces errors in analysis. BibRef

Kumar, L.[Lalit], Mutanga, O.[Onisimo],
Google Earth Engine Applications Since Inception: Usage, Trends, and Potential,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Zhang, J.X.[Jing-Xiong], Yang, W.J.[Wen-Jing], Zhang, W.[Wangle], Wang, Y.[Yu], Liu, D.[Di], Xiu, Y.C.[Ying-Chang],
An Explorative Study on Estimating Local Accuracies in Land-Cover Information Using Logistic Regression and Class-Heterogeneity-Stratified Data,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Ernst, S.[Stefan], Lymburner, L.[Leo], Sixsmith, J.[Josh],
Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Sertel, E.[Elif], Topaloglu, R.H.[Raziye Hale], Salli, B.[Betül], Algan, I.Y.[Irmak Yay], Aksu, G.A.[Gül Asli],
Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps,
IJGI(7), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Stachon, Z.[Zdenek], Šašinka, C.[Cenek], Cenek, J.[Jirí], Angsüsser, S.[Stephan], Kubícek, P.[Petr], Šterba, Z.[Zbynek], Bilíková, M.[Martina],
Effect of Size, Shape and Map Background in Cartographic Visualization: Experimental Study on Czech and Chinese Populations,
IJGI(7), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Jian, L.[Ling], Gao, F.[Fuhao], Ren, P.[Peng], Song, Y.Q.[Yun-Quan], Luo, S.H.[Shi-Hua],
A Noise-Resilient Online Learning Algorithm for Scene Classification,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Hua, T.[Ting], Zhao, W.W.[Wen-Wu], Liu, Y.X.[Yan-Xu], Wang, S.[Shuai], Yang, S.Q.[Si-Qi],
Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Carranza-García, M.[Manuel], García-Gutiérrez, J.[Jorge], Riquelme, J.C.[José C.],
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Ma, L.[Lei], Liu, Y.[Yu], Zhang, X.L.[Xue-Liang], Ye, Y.X.[Yuan-Xin], Yin, G.F.[Gao-Fei], Johnson, B.A.[Brian Alan],
Deep learning in remote sensing applications: A meta-analysis and review,
PandRS(152), 2019, pp. 166-177.
Elsevier DOI 1905
Deep learning (DL), Remote sensing, LULC classification, Object detection, Scene classification BibRef

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Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity,
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The t-SNE Algorithm as a Tool to Improve the Quality of Reference Data Used in Accurate Mapping of Heterogeneous Non-Forest Vegetation,
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Yin, G., Ma, L., Zhao, W., Zeng, Y., Xu, B., Wu, S.,
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IEEE DOI 2012
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Ma, X.L.[Xuan-Long], Huete, A.[Alfredo], Tran, N.N.[Ngoc Nguyen], Bi, J.[Jian], Gao, S.[Sicong], Zeng, Y.[Yelu],
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Radosavljevic, M.[Miloš], Brkljac, B.[Branko], Lugonja, P.[Predrag], Crnojevic, V.[Vladimir], Trpovski, Ž.[Željen], Xiong, Z.X.[Zi-Xiang], Vukobratovic, D.[Dejan],
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The T Index: Measuring the Reliability of Accuracy Estimates Obtained from Non-Probability Samples,
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Han, Z.M.[Ze-Min], Dian, Y.Y.[Yuan-Yong], Xia, H.[Hao], Zhou, J.J.[Jing-Jing], Jian, Y.F.[Yong-Feng], Yao, C.H.[Chong-Huai], Wang, X.[Xiong], Li, Y.[Yuan],
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Elsevier DOI 2008
Photogrammetry, Mapping, Aerial, Bundle adjustment, Unmanned aerial vehicle, GPS BibRef

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Berman, E.E.[Ethan E.], Graves, T.A.[Tabitha A.], Mikle, N.L.[Nate L.], Merkle, J.A.[Jerod A.], Johnston, A.N.[Aaron N.], Chong, G.W.[Geneva W.],
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Lu, H.[Han], Fan, T.X.[Tian-Xing], Ghimire, P.[Prakash], Deng, L.[Lei],
Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors,
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Orynbaikyzy, A.[Aiym], Gessner, U.[Ursula], Mack, B.[Benjamin], Conrad, C.[Christopher],
Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies,
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Chaves, M.E.D.[Michel E. D.], Picoli, M.C.A.[Michelle C. A.], Sanches, I.D.[Ieda D.],
Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review,
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Chen, X.R.[Xin-Ran], Zhan, Y.L.[Yu-Lin], Liu, Y.[Yan], Gu, X.F.[Xing-Fa], Yu, T.[Tao], Wang, D.K.[Da-Kang], Liu, Q.X.[Qi-Xin], Zhang, Y.[Yin], Zhang, Y.Z.[Yun-Zhou],
Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices,
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Esteban, J.[Jessica], McRoberts, R.E.[Ronald E.], Fernández-Landa, A.[Alfredo], Tomé, J.L.[José Luis], Marchamalo, M.[Miguel],
A Model-Based Volume Estimator that Accounts for Both Land Cover Misclassification and Model Prediction Uncertainty,
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Chen, Z.[Zhou], Fei, X.Y.[Xian-Yun], Gao, X.W.[Xiang-Wei], Wang, X.X.[Xiao-Xue], Zhao, H.M.[Hui-Min], Wong, K.[Kapo], Tsou, J.Y.[Jin Yeu], Zhang, Y.Z.[Yuan-Zhi],
The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images,
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Gao, Y.[Yuan], Liu, L.Y.[Liang-Yun], Zhang, X.[Xiao], Chen, X.D.[Xi-Dong], Mi, J.[Jun], Xie, S.A.[Shu-Ai],
Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset,
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Deng, X.L.[Xue-Lei], Dong, Y.F.[Yun-Feng], Xie, S.C.[Shu-Cong],
Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster,
RS(12), No. 21, 2020, pp. xx-yy.
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Ogryzek, M.[Marek], Tarantino, E.[Eufemia], Rzasa, K.[Krzysztof],
Infrastructure of the Spatial Information in the European Community (INSPIRE) Based on Examples of Italy and Poland,
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Form Follows Content: An Empirical Study on Symbol-Content (In)Congruences in Thematic Maps,
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Papakonstantinou, A.[Apostolos], Stamati, C.[Chrysa], Topouzelis, K.[Konstantinos],
Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis,
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Ebrahimy, H.[Hamid], Mirbagheri, B.[Babak], Matkan, A.A.[Ali Akbar], Azadbakht, M.[Mohsen],
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Elsevier DOI 2101
Land cover mapping, Support vector machine, Spatial accuracy, Accuracy assessment, Random forest BibRef

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Elsevier DOI 2102
Convolutional Neural Networks (CNN), Deep learning, Vegetation, Plants, Remote sensing, Earth observation BibRef

Rowan, G.S.L.[Gillian S. L.], Kalacska, M.[Margaret],
A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists,
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Prikaziuk, E.[Egor], Yang, P.Q.[Pei-Qi], van der Tol, C.[Christiaan],
Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences,
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DOI Link 2104
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Damgaard, C.[Christian],
Integrating Hierarchical Statistical Models and Machine-Learning Algorithms for Ground-Truthing Drone Images of the Vegetation: Taxonomy, Abundance and Population Ecological Models,
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DOI Link 2104
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Ghayour, L.[Laleh], Neshat, A.[Aminreza], Paryani, S.[Sina], Shahabi, H.[Himan], Shirzadi, A.[Ataollah], Chen, W.[Wei], Al-Ansari, N.[Nadhir], Geertsema, M.[Marten], Amiri, M.P.[Mehdi Pourmehdi], Gholamnia, M.[Mehdi], Dou, J.[Jie], Ahmad, A.[Anuar],
Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms,
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DOI Link 2104
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Selva, M.[Massimo],
The Quality of Remote Sensing Optical Images from Acquisition to Users,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
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Shetty, S.[Shobitha], Gupta, P.K.[Prasun Kumar], Belgiu, M.[Mariana], Srivastav, S.K.,
Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine,
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DOI Link 2104
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Tornatore, V.[Vincenza], Cesaroni, C.[Claudio], Pezzopane, M.[Michael], Alizadeh, M.M.[Mohamad Mahdi], Schuh, H.[Harald],
Performance Evaluation of VTEC GIMs for Regional Applications during Different Solar Activity Periods, Using RING TEC Values,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
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Gu, L.X.[Ling-Xiao], Shuai, Y.M.[Yan-Min], Shao, C.Y.[Cong-Ying], Xie, D.H.[Dong-Hui], Zhang, Q.L.[Qing-Ling], Li, Y.M.[Yao-Ming], Yang, J.[Jian],
Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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de Souza, R.[Romina], Buchhart, C.[Claudia], Heil, K.[Kurt], Plass, J.[Jürgen], Padilla, F.M.[Francisco M.], Schmidhalter, U.[Urs],
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DOI Link 2105
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Rapinel, S.[Sébastien], Hubert-Moy, L.[Laurence],
One-Class Classification of Natural Vegetation Using Remote Sensing: A Review,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
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Sarafian, R.[Ron], Kloog, I.[Itai], Sarafian, E.[Elad], Hough, I.[Ian], Rosenblatt, J.D.[Jonathan D.],
A Domain Adaptation Approach for Performance Estimation of Spatial Predictions,
GeoRS(59), No. 6, June 2021, pp. 5197-5205.
IEEE DOI 2106
Training, Task analysis, Training data, Estimation, Monitoring, Quality assessment, Satellites, Geospatial analysis, remote sensing BibRef

Maxwell, A.E.[Aaron E.], Warner, T.A.[Timothy A.], Guillén, L.A.[Luis Andrés],
Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies: Part 1: Literature Review,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
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Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies: Part 2: Recommendations and Best Practices,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
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Hong, D.F.[Dan-Feng], Hu, J.L.[Jing-Liang], Yao, J.[Jing], Chanussot, J.[Jocelyn], Zhu, X.X.[Xiao Xiang],
Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model,
PandRS(178), 2021, pp. 68-80.
Elsevier DOI 2108
Dataset, Remote Sensing. Benchmark datasets, Classification, Feature learning, Hyperspectral, Land cover mapping, DSM, Multimodal, Specific features BibRef

Dobesova, Z.[Zdena],
Cognition of Graphical Notation for Processing Data in ERDAS IMAGINE,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
Evaluation of the spatial model ediror. BibRef

Ghaffarian, S.[Saman], Valente, J.[João], van der Voort, M.[Mariska], Tekinerdogan, B.[Bedir],
Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review,
RS(13), No. 15, 2021, pp. xx-yy.
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Chen, L.[Li], Xu, Z.W.[Ze-Wei], Li, Q.[Qi], Peng, J.[Jian], Wang, S.W.[Shao-Wen], Li, H.F.[Hai-Feng],
An Empirical Study of Adversarial Examples on Remote Sensing Image Scene Classification,
GeoRS(59), No. 9, September 2021, pp. 7419-7433.
IEEE DOI 2109
Remote sensing, Optical sensors, Optical imaging, Feature extraction, Training, Radar polarimetry, remote sensing image (RSI) scene classification BibRef

Fayad, I.[Ibrahim], Baghdadi, N.[Nicolas], Riedi, J.[Jérôme],
Quality Assessment of Acquired GEDI Waveforms: Case Study over France, Tunisia and French Guiana,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
Global Ecosystem Dynamics Investigation (LiDAR). Over half of data is usable (e.g. no clouds) BibRef

Brown, L.A.[Luke A.], Camacho, F.[Fernando], García-Santos, V.[Vicente], Origo, N.[Niall], Fuster, B.[Beatriz], Morris, H.[Harry], Pastor-Guzman, J.[Julio], Sánchez-Zapero, J.[Jorge], Morrone, R.[Rosalinda], Ryder, J.[James], Nightingale, J.[Joanne], Boccia, V.[Valentina], Dash, J.[Jadunandan],
Fiducial Reference Measurements for Vegetation Bio-Geophysical Variables: An End-to-End Uncertainty Evaluation Framework,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
Assess accuracy and fitness for desired analysis. Crops and forests. Leaf area, Fraction of vegetation, canopy water. BibRef

Tratt, D.M.[David M.], Buckland, K.N.[Kerry N.], Keim, E.R.[Eric R.], Hall, J.L.[Jeffrey L.], Adams, P.M.[Paul M.], Johnson, P.D.[Patrick D.],
On the Utility of Longwave-Infrared Spectral Imaging for Remote Botanical Identification,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
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Wei, S.J.[Shun-Jun], Zhou, Z.C.[Zi-Chen], Wang, M.[Mou], Wei, J.S.[Jin-Shan], Liu, S.[Shan], Shi, J.[Jun], Zhang, X.L.[Xiao-Ling], Fan, F.[Fan],
3DRIED: A High-Resolution 3-D Millimeter-Wave Radar Dataset Dedicated to Imaging and Evaluation,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
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Yu, T.L.[Tian-Lei], Ma, G.[Gang], Lu, F.[Feng], Zhang, X.H.[Xiao-Hu], Zhang, P.[Peng],
Quality Scoring of the Fengyun 4A Clear Sky Radiance Product,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
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Hu, B.[Baoan], Zhang, Z.J.[Zhi-Jie], Han, H.R.[Hai-Rong], Li, Z.Z.[Zu-Zheng], Cheng, X.Q.[Xiao-Qin], Kang, F.F.[Feng-Feng], Wu, H.F.[Hui-Feng],
The Grain for Green Program Intensifies Trade-Offs between Ecosystem Services in Midwestern Shanxi, China,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
Address environmental degradation and enhance human well-being. BibRef

Xu, P.P.[Pan-Pan], Tsendbazar, N.E.[Nandin-Erdene], Herold, M.[Martin], Clevers, J.G.P.W.[Jan G. P. W.],
Assessing a Prototype Database for Comprehensive Global Aquatic Land Cover Mapping,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Li, Z.L.[Zheng-Long], Schmit, T.J.[Timothy J.], Li, J.[Jun], Gunshor, M.M.[Mathew M.], Nagle, F.W.[Frederick W.],
Understanding the Imaging Capability of Tundra Orbits Compared to Other Orbits,
GeoRS(59), No. 11, November 2021, pp. 8944-8956.
IEEE DOI 2111
Satellites, Orbits, Imaging, Low earth orbit satellites, Spatial resolution, Earth, Planetary orbits, Fire detection, weather forecasting BibRef

Duan, W.T.[Wen-Tao], Liu, J.D.[Jian-Dong], Yan, Q.Y.[Qing-Yun], Ruan, H.B.[Hai-Bing], Jin, S.G.[Shuang-Gen],
The Effect of Spatial Resolution and Temporal Sampling Schemes on the Measurement Error for a Moon-Based Earth Radiation Observatory,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Li, C.X.[Chen-Xi], Ma, Z.Y.[Zai-Ying], Wang, L.[Liuyue], Yu, W.J.[Wei-Jian], Tan, D.L.[Dong-Lin], Gao, B.B.[Bing-Bo], Feng, Q.L.[Quan-Long], Guo, H.[Hao], Zhao, Y.Y.[Yuan-Yuan],
Improving the Accuracy of Land Cover Mapping by Distributing Training Samples,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Yang, Y.P.[Yan-Peng], Yang, D.[Dong], Wang, X.F.[Xu-Feng], Zhang, Z.[Zhao], Nawaz, Z.[Zain],
Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
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Doicu, A.[Adrian], Doicu, A.[Alexandru], Efremenko, D.S.[Dmitry S.], Loyola, D.[Diego], Trautmann, T.[Thomas],
An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
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Moncholi-Estornell, A.[Adrián], van Wittenberghe, S.[Shari], Cendrero-Mateo, M.P.[Maria Pilar], Alonso, L.[Luis], Malenovský, Z.[Zbynek], Moreno, J.[José],
Impact of Structural, Photochemical and Instrumental Effects on Leaf and Canopy Reflectance Variability in the 500-600 nm Range,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Zhou, X.C.[Xiao-Cheng], Liu, X.P.[Xue-Ping], Wang, X.Q.[Xiao-Qin], He, G.J.[Guo-Jin], Zhang, Y.S.[You-Shui], Wang, G.Z.[Gui-Zhou], Zhang, Z.M.[Zhao-Ming],
Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Oggioni, L.[Luca], Sanchez del Rio Kandel, D.[David], Pariani, G.[Giorgio],
Earth Observation via Compressive Sensing: The Effect of Satellite Motion,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
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Musial, J.P.[Jan Pawel], Bojanowski, J.S.[Jedrzej Stanislaw],
Comparison of the Novel Probabilistic Self-Optimizing Vectorized Earth Observation Retrieval Classifier with Common Machine Learning Algorithms,
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DOI Link 2201
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Adugna, T.[Tesfaye], Xu, W.B.[Wen-Bo], Fan, J.L.[Jin-Long],
Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Li, R.X.[Ruo-Xi], Tao, Z.[Zui], Zhou, X.[Xiang], Lv, T.T.[Ting-Ting], Wang, J.[Jin], Xie, F.[Futai], Zhai, M.J.[Ming-Jian],
Data-Driven Selection of Land Product Validation Station Based on Machine Learning,
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Tan, Y.M.[Yu-Min], Shi, Y.Z.[Yan-Zhe], Xu, L.[Le], Zhou, K.L.[Kai-Lei], Jing, G.F.[Gui-Fei], Wang, X.L.[Xiao-Lu], Bai, B.X.[Bing-Xin],
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Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021,
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Maxwell, A.E.[Aaron E.], Bester, M.S.[Michelle S.], Ramezan, C.A.[Christopher A.],
Enhancing Reproducibility and Replicability in Remote Sensing Deep Learning Research and Practice,
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Wang, H.[Hao], Hu, Y.F.[Yun-Feng], Feng, Z.M.[Zhi-Ming],
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DOI Link 2212
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IJGI(11), No. 11, 2022, pp. xx-yy.
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Dimitriadou, S.[Stavroula], Nikolakopoulos, K.G.[Konstantinos G.],
Development of the Statistical Errors Raster Toolbox with Six Automated Models for Raster Analysis in GIS Environments,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Jin, Y.[Yan], Guan, X.D.[Xu-Dong], Ge, Y.[Yong], Jia, Y.[Yan], Li, W.[Wenmei],
Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification,
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Ersi, C.[Cha], Bayaer, T.[Tubuxin], Bao, Y.[Yuhai], Bao, Y.L.[Yu-Long], Yong, M.[Mei], Lai, Q.[Quan], Zhang, X.[Xiang], Zhang, Y.[Yusi],
Comparison of Phenological Parameters Extracted from SIF, NDVI and NIRv Data on the Mongolian Plateau,
RS(15), No. 1, 2023, pp. xx-yy.
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Papoutsis, I.[Ioannis], Bountos, N.I.[Nikolaos Ioannis], Zavras, A.[Angelos], Michail, D.[Dimitrios], Tryfonopoulos, C.[Christos],
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PandRS(195), 2023, pp. 250-268.
Elsevier DOI 2301
Benchmark, Land use land cover image classification, BigEarthNet, Wide Residual Networks, EfficientNet, Transfer learning BibRef

Omia, E.[Emmanuel], Bae, H.[Hyungjin], Park, E.[Eunsung], Kim, M.S.[Moon Sung], Baek, I.[Insuck], Kabenge, I.[Isa], Cho, B.K.[Byoung-Kwan],
Remote Sensing in Field Crop Monitoring: A Comprehensive Review of Sensor Systems, Data Analyses and Recent Advances,
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Dimitrovski, I.[Ivica], Kitanovski, I.[Ivan], Kocev, D.[Dragi], Simidjievski, N.[Nikola],
Current trends in deep learning for Earth Observation: An open-source benchmark arena for image classification,
PandRS(197), 2023, pp. 18-35.
Elsevier DOI 2303
Deep learning (DL), Earth observation (EO), Image classification, Benchmark study BibRef

Sousa, D.[Daniel], Small, C.[Christopher],
Which Vegetation Index? Benchmarking Multispectral Metrics to Hyperspectral Mixture Models in Diverse Cropland,
RS(15), No. 4, 2023, pp. xx-yy.
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Yang, L.[Le], Shi, L.[Lei], Sun, W.D.[Wei-Dong], Yang, J.[Jie], Li, P.X.[Ping-Xiang], Li, D.R.[De-Ren], Liu, S.W.[Shan-Wei], Zhao, L.L.[Ling-Li],
Radiometric and Polarimetric Quality Validation of Gaofen-3 over a Five-Year Operation Period,
RS(15), No. 6, 2023, pp. 1605.
DOI Link 2304
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Marelli, D.[Davide], Morelli, L.[Luca], Farella, E.M.[Elisa Mariarosaria], Bianco, S.[Simone], Ciocca, G.[Gianluigi], Remondino, F.[Fabio],
ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry,
PandRS(198), 2023, pp. 84-98.
Elsevier DOI 2304
Dataset, Image matching, Photogrammetry, Local features, 3D reconstruction, Depth estimation, Synthetic images BibRef

Dhillon, M.S.[Maninder Singh], Kübert-Flock, C.[Carina], Dahms, T.[Thorsten], Rummler, T.[Thomas], Arnault, J.[Joel], Steffan-Dewenter, I.[Ingolf], Ullmann, T.[Tobias],
Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany,
RS(15), No. 7, 2023, pp. 1830.
DOI Link 2304
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Aleissaee, A.A.[Abdulaziz Amer], Kumar, A.[Amandeep], Anwer, R.M.[Rao Muhammad], Khan, S.[Salman], Cholakkal, H.[Hisham], Xia, G.S.[Gui-Song], Khan, F.S.[Fahad Shahbaz],
Transformers in Remote Sensing: A Survey,
RS(15), No. 7, 2023, pp. 1860.
DOI Link 2304
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Chen, Y.J.[Yi-Jun], Zhao, S.X.[Shen-Xin], Zhang, L.H.[Li-Hua], Zhou, Q.[Qi],
Quality Assessment of Global Ocean Island Datasets,
IJGI(12), No. 4, 2023, pp. 168.
DOI Link 2305
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Boston, T.[Tony], van Dijk, A.[Albert], Thackway, R.[Richard],
Convolutional Neural Network Shows Greater Spatial and Temporal Stability in Multi-Annual Land Cover Mapping Than Pixel-Based Methods,
RS(15), No. 8, 2023, pp. 2132.
DOI Link 2305
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Joshi, A.[Abhasha], Pradhan, B.[Biswajeet], Gite, S.[Shilpa], Chakraborty, S.[Subrata],
Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review,
RS(15), No. 8, 2023, pp. 2014.
DOI Link 2305
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Pellegrino, A.[Andrea], Fabbretto, A.[Alice], Bresciani, M.[Mariano], de Lima, T.M.A.[Thainara Munhoz Alexandre], Braga, F.[Federica], Pahlevan, N.[Nima], Brando, V.E.[Vittorio Ernesto], Kratzer, S.[Susanne], Gianinetto, M.[Marco], Giardino, C.[Claudia],
Assessing the Accuracy of PRISMA Standard Reflectance Products in Globally Distributed Aquatic Sites,
RS(15), No. 8, 2023, pp. 2163.
DOI Link 2305
hyperspectral. Evaluation. BibRef

Miletic, A.[Andrea], Divjak, A.K.[Ana Kuveždic], Donker, F.W.[Frederika Welle],
Assessment of the Croatian Open Data Portal Using User-Oriented Metrics,
IJGI(12), No. 5, 2023, pp. xx-yy.
DOI Link 2306
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Dondofema, F.[Farai], Nethengwe, N.[Nthaduleni], Taylor, P.[Peter], Ramoelo, A.[Abel],
Comparison of Satellite Platform for Mapping the Distribution of Mauritius Thorn (Caesalpinia decapetala) and River Red Gum (Eucalyptus camaldulensis) in the Vhembe Biosphere Reserve,
RS(15), No. 11, 2023, pp. 2753.
DOI Link 2306
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Mountrakis, G.[Giorgos], Heydari, S.S.[Shahriar S.],
Harvesting the Landsat archive for land cover land use classification using deep neural networks: Comparison with traditional classifiers and multi-sensor benefits,
PandRS(200), 2023, pp. 106-119.
Elsevier DOI 2306
Deep neural networks, Recurrent network, Convolutional network, Long Short-Term Memory, Landsat, Random Forest BibRef

Wang, Z.X.[Zhi-Xin], Mountrakis, G.[Giorgos],
Accuracy Assessment of Eleven Medium Resolution Global and Regional Land Cover Land Use Products: A Case Study over the Conterminous United States,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Rahman, M.M.[Md. Mostafizur], Szabó, G.[György],
Assessing the Status of National Spatial Data Infrastructure (NSDI) of Bangladesh,
IJGI(12), No. 6, 2023, pp. xx-yy.
DOI Link 2307
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Palanisamy, P.A.[Prathiba A.], Jain, K.[Kamal], Bonafoni, S.[Stefania],
Machine Learning Classifier Evaluation for Different Input Combinations: A Case Study with Landsat 9 and Sentinel-2 Data,
RS(15), No. 13, 2023, pp. 3241.
DOI Link 2307
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Bratic, G.[Gorica], Oxoli, D.[Daniele], Brovelli, M.A.[Maria Antonia],
Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
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Dash, P.[Padmanava], Sanders, S.L.[Scott L.], Parajuli, P.[Prem], Ouyang, Y.[Ying],
Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data in an Agricultural Watershed,
RS(15), No. 16, 2023, pp. 4020.
DOI Link 2309
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Lei, M.[Ming], Dong, Y.F.[Yun-Feng],
Multi-Granularity Modeling Method for Effectiveness Evaluation of Remote Sensing Satellites,
RS(15), No. 17, 2023, pp. 4335.
DOI Link 2310
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Jin, Z.C.[Zi-Chun], Long, Z.Y.[Zhi-Yong], Wang, S.F.[Shao-Fei], Liu, Y.M.[Yun-Meng],
Performance of the Atmospheric Radiative Transfer Simulator (ARTS) in the 600-1650 cm-1 Region,
RS(15), No. 19, 2023, pp. 4889.
DOI Link 2310
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Gong, Y.[Yali], Xie, H.[Huan], Liao, S.C.[Shi-Cheng], Lu, Y.[Yao], Jin, Y.M.[Yan-Min], Wei, C.[Chao], Tong, X.H.[Xiao-Hua],
Assessing the Accuracy of Multi-Temporal GlobeLand30 Products in China Using a Spatiotemporal Stratified Sampling Method,
RS(15), No. 18, 2023, pp. 4593.
DOI Link 2310
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Cui, P.P.[Pei-Pei], Chen, T.[Tan], Li, Y.J.[Ying-Jie], Liu, K.[Kai], Zhang, D.P.[Da-Peng], Song, C.Q.[Chun-Qiao],
Comparison and Assessment of Different Land Cover Datasets on the Cropland in Northeast China,
RS(15), No. 21, 2023, pp. 5134.
DOI Link 2311
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Wang, Y.Z.[Yan-Zhao], Sun, Y.H.[Yong-Hua], Cao, X.[Xuyue], Wang, Y.[Yihan], Zhang, W.[Wangkuan], Cheng, X.[Xinglu],
A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing,
PandRS(206), 2023, pp. 311-334.
Elsevier DOI 2312
Land cover, Land use, LULC products, Satellite remote sensing, Challenges and trends BibRef

Liu, J.P.[Jing-Peng], Ren, Y.[Yu], Chen, X.[Xidong],
Regional Accuracy Assessment of 30-Meter GLC_FCS30, GlobeLand30, and CLCD Products: A Case Study in Xinjiang Area,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
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Wang, C.L.[Chen-Liang], Shi, W.J.[Wen-Jiao], Lv, H.C.[Hong-Chen],
Construction of Remote Sensing Indices Knowledge Graph (RSIKG) Based on Semantic Hierarchical Graph,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
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Zhu, R.[Rui], Tan, Y.M.[Yu-Min], Luo, Z.Q.[Zi-Qing], Shi, Y.Z.[Yan-Zhe], Wang, J.[Jiale], Jing, G.[Guifei], Wang, X.L.[Xiao-Lu],
WenSiM: A Relative Accuracy Assessment Method for Land Cover Products Based on Optimal Transportation Theory,
RS(16), No. 2, 2024, pp. 257.
DOI Link 2402
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Zhang, H.Z.[Hong-Zhe], Feng, S.[Shou], Wu, D.[Di], Zhao, C.H.[Chun-Hui], Liu, X.[Xi], Zhou, Y.[Yuan], Wang, S.N.[Sheng-Nan], Deng, H.T.[Hong-Tao], Zheng, S.[Shuang],
Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results,
RS(16), No. 3, 2024, pp. 478.
DOI Link 2402
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Krekovic, D.[Dora], Galic, V.[Vlatko], Tržec, K.[Krunoslav], Žarko, I.P.[Ivana Podnar], Kušek, M.[Mario],
Comparing Remote and Proximal Sensing of Agrometeorological Parameters across Different Agricultural Regions in Croatia: A Case Study Using ERA5-Land, Agri4Cast, and In Situ Stations during the Period 2019-2021,
RS(16), No. 4, 2024, pp. 641.
DOI Link 2402
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Dong, R.[Runbo], Guo, H.D.[Hua-Dong], Liu, G.[Guang],
Comparison Study of Earth Observation Characteristics between Moon-Based Platform and L1 Point of Earth-Moon System,
RS(16), No. 3, 2024, pp. 513.
DOI Link 2402
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Hare, T.M.[Trent M.], Kirk, R.L.[Randolph L.], Bland, M.T.[Michael T.], Galuszka, D.M.[Donna M.], Laura, J.R.[Jason R.], Mayer, D.P.[David P.], Redding, B.L.[Bonnie L.], Wheeler, B.H.[Benjamin H.],
Current Status of the Community Sensor Model Standard for the Generation of Planetary Digital Terrain Models,
RS(16), No. 4, 2024, pp. 648.
DOI Link 2402
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Mountrakis, G.[Giorgos], Heydari, S.S.[Shahriar S.],
Effect of intra-year Landsat scene availability in land cover land use classification in the conterminous United States using deep neural networks,
PandRS(212), 2024, pp. 164-180.
Elsevier DOI 2406
Deep learning, Time-series, Classification, Landsat, United States BibRef

Thoreau, R.[Romain], Risser, L.[Laurent], Achard, V.[Véronique], Berthelot, B.[Béatrice], Briottet, X.[Xavier],
Toulouse Hyperspectral Data Set: A benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques,
PandRS(212), 2024, pp. 323-337.
Elsevier DOI 2406
Hyperspectral imaging, Land cover mapping, Benchmark data set, Semi-supervised learning, Self-supervised learning BibRef

Wijesingha, J.[Jayan], Dzene, I.[Ilze], Wachendorf, M.[Michael],
Evaluating the spatial-temporal transferability of models for agricultural land cover mapping using Landsat archive,
PandRS(213), 2024, pp. 72-86.
Elsevier DOI 2406
Agricultural land cover, Crop types, Landsat, Spatial-temporal transferability, Machine learning BibRef

Liu, M.[Miao], Zhan, Y.L.[Yu-Lin], Li, J.[Juan], Kang, Y.P.[Yu-Peng], Sun, X.[Xiuling], Gu, X.F.[Xing-Fa], Wei, X.Q.[Xiang-Qin], Wang, C.M.[Chun-Mei], Li, L.L.[Ling-Ling], Gao, H.L.[Hai-Liang], Yang, J.[Jian],
Validation of Red-Edge Vegetation Indices in Vegetation Classification in Tropical Monsoon Region: A Case Study in Wenchang, Hainan, China,
RS(16), No. 11, 2024, pp. 1865.
DOI Link 2406
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Ding, A.X.[An-Xin], Jiao, Z.[Ziti], Kokhanovsky, A.[Alexander], Zhang, X.N.[Xiao-Ning], Guo, J.[Jing], Zhao, P.[Ping], Zhang, M.M.[Ming-Ming], Jiang, H.[Hailan], Xu, K.J.[Kai-Jian],
Evaluating the Performance of the Enhanced Ross-Li Models in Characterizing BRDF/Albedo/NBAR Characteristics for Various Land Cover Types in the POLDER Database,
RS(16), No. 12, 2024, pp. 2119.
DOI Link 2406
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Cottrell, B.[Brendan], Kalacska, M.[Margaret], Arroyo-Mora, J.P.[Juan-Pablo], Lucanus, O.[Oliver], Inamdar, D.[Deep], Løke, T.[Trond], Soffer, R.J.[Raymond J.],
Limitations of a Multispectral UAV Sensor for Satellite Validation and Mapping Complex Vegetation,
RS(16), No. 13, 2024, pp. 2463.
DOI Link 2407
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Ceferino-Hernández, L.[Lorenza], Magaña-Hernández, F.[Francisco], Campos-Campos, E.[Enrique], Morosanu, G.A.[Gabriela Adina], Torres-Aguilar, C.E.[Carlos E.], Mora-Ortiz, R.S.[René Sebastián], Díaz, S.A.[Sergio A.],
Assessment of PERSIANN Satellite Products over the Tulijá River Basin, Mexico,
RS(16), No. 14, 2024, pp. 2596.
DOI Link 2408
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Sim, W.D.[Woo-Dam], Yim, J.S.[Jong-Su], Lee, J.S.[Jung-Soo],
Assessing Land Cover Classification Accuracy: Variations in Dataset Combinations and Deep Learning Models,
RS(16), No. 14, 2024, pp. 2623.
DOI Link 2408
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Liu, Y.[Yin], Diao, C.Y.[Chun-Yuan], Mei, W.[Weiye], Zhang, C.[Chishan],
CropSight: Towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and PlanetScope satellite imagery,
PandRS(216), 2024, pp. 66-89.
Elsevier DOI 2408
Crop type ground truth, Street view imagery, PlanetScope, Deep learning, Uncertainty BibRef

Liao, S.C.[Shi-Cheng], Xie, H.[Huan], Gong, Y.[Yali], Jin, Y.M.[Yan-Min], Xu, X.[Xiong], Chen, P.[Peng], Tong, X.H.[Xiao-Hua],
Validation of Multi-Temporal Land-Cover Products Considering Classification Error Propagation,
RS(16), No. 16, 2024, pp. 2968.
DOI Link 2408
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Zhao, J.[Jing], Aziz, F.A.[Faziawati Abdul], Cheng, Z.[Ziyi], Ujang, N.[Norsidah], Zhang, H.[Hui], Xu, J.J.[Jia-Jun], Xiao, Y.[Yi], Shi, L.[Lin],
Post-Occupancy Evaluation of the Improved Old Residential Neighborhood Satisfaction Using Principal Component Analysis: The Case of Wuxi, China,
IJGI(13), No. 9, 2024, pp. 318.
DOI Link 2410
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Li, W.M.[Wei-Ming], Du, Z.Q.[Zhi-Qiang], Wang, L.[Li], Zhou, T.C.[Tian-Cheng],
Evaluation of the Monitoring Capabilities of Remote Sensing Satellites for Maritime Moving Targets,
IJGI(13), No. 9, 2024, pp. 325.
DOI Link 2410
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Ahmad, M.[Munir], Ali, A.[Asmat], Nawaz, M.[Muhammad], Sattar, F.[Farha], Hussain, H.[Hammad],
A Review of Pakistan's National Spatial Data Infrastructure Using Multiple Assessment Frameworks,
IJGI(13), No. 9, 2024, pp. 328.
DOI Link 2410
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Cheon, M.J.[Min-Jong], Mun, C.B.[Chang-Bae],
Combining KAN with CNN: KonvNeXt's Performance in Remote Sensing and Patent Insights,
RS(16), No. 18, 2024, pp. 3417.
DOI Link 2410
Kolmogorov-Arnold Network (KAN). BibRef

Sani, D.[Depanshu], Mahato, S.[Sandeep], Saini, S.[Sourabh], Agarwal, H.K.[Harsh Kumar], Devshali, C.C.[Charu Chandra], Anand, S.[Saket], Arora, G.[Gaurav], Jayaraman, T.[Thiagarajan],
SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters,
WACV24(5983-5992)
IEEE DOI 2404
Satellites, Image resolution, Pipelines, Phenology, Crops, Machine learning, Agriculture, Applications, Agriculture, Algorithms, Remote Sensing BibRef

Ibrahim, M.R.[Mohamed Ramzy], Benavente, R.[Robert], Ponsa, D.[Daniel], Lumbreras, F.[Felipe],
Unveiling the Influence of Image Super-resolution on Aerial Scene Classification,
CIARP23(I:214-228).
Springer DOI 2312
BibRef