| A review of assessing the accuracy of classifications of remotely sensed data RG Congalton Remote sensing of environment 37 (1), 35-46, 1991 | 10278 | 1991 |
| Assessing the accuracy of remotely sensed data: principles and practices RG Congalton, K Green CRC press, 2019 | 9032 | 2019 |
| Accuracy assessment: a user’s perspective M Story, RG Congalton Photogrammetric Engineering and remote sensing 52 (3), 397-399, 1986 | 2192 | 1986 |
| Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques RG Congalton, RG Oderwald, RA Mead Photogrammetric engineering and remote sensing 49 (12), 1671-1678, 1983 | 1158 | 1983 |
| A quantitative method to test for consistency and correctness in photointerpretation RG Congalton, RA Mead Photogrammetric Engineering and Remote Sensing 49 (1), 69-74, 1983 | 810 | 1983 |
| Application of remote sensing and geographic information systems to forest fire hazard mapping E Chuvieco, RG Congalton Remote sensing of Environment 29 (2), 147-159, 1989 | 749 | 1989 |
| A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data RD Macleod, RG Congalton Photogrammetric engineering and remote sensing 64 (3), 207-216, 1998 | 742 | 1998 |
| Accuracy assessment and validation of remotely sensed and other spatial information RG Congalton International journal of wildland fire 10 (4), 321-328, 2001 | 559 | 2001 |
| Automated cropland mapping of continental Africa using Google Earth Engine cloud computing J Xiong, PS Thenkabail, MK Gumma, P Teluguntla, J Poehnelt, ... ISPRS Journal of Photogrammetry and Remote Sensing 126, 225-244, 2017 | 534 | 2017 |
| Determining forest species composition using high spectral resolution remote sensing data ME Martin, SD Newman, JD Aber, RG Congalton Remote sensing of environment 65 (3), 249-254, 1998 | 520 | 1998 |
| A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data. RG Congalton | 506 | 1988 |
| Remote sensing and geographic information system data integration- Error sources and research issues R Lunetta, R Congalton, L Fenstermaker, J Jensen, K Mcgwire, LR Tinney Photogrammetric engineering and remote sensing 57 (6), 677-687, 1991 | 502 | 1991 |
| A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform P Teluguntla, PS Thenkabail, A Oliphant, J Xiong, MK Gumma, ... ISPRS journal of photogrammetry and remote sensing 144, 325-340, 2018 | 465 | 2018 |
| A comparison of urban mapping methods using high-resolution digital imagery N Thomas, C Hendrix, RG Congalton Photogrammetric Engineering & Remote Sensing 69 (9), 963-972, 2003 | 420 | 2003 |
| Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine J Xiong, PS Thenkabail, JC Tilton, MK Gumma, P Teluguntla, A Oliphant, ... Remote Sensing 9 (10), 1065, 2017 | 418 | 2017 |
| Evaluating the potential for measuring river discharge from space DM Bjerklie, SL Dingman, CJ Vorosmarty, CH Bolster, RG Congalton Journal of hydrology 278 (1-4), 17-38, 2003 | 404 | 2003 |
| Global land cover mapping: A review and uncertainty analysis RG Congalton, J Gu, K Yadav, P Thenkabail, M Ozdogan Remote Sensing 6 (12), 12070-12093, 2014 | 373 | 2014 |
| A practical look at the sources of confusion in error matrix generation. RG Congalton, K Green | 337 | 1993 |
| Using spatial autocorrelation analysis to explore the errors in maps generated from remotely sensed data. RG Congalton | 296 | 1988 |
| Effects of landscape characteristics on amphibian distribution in a forest-dominated landscape HL Herrmann, KJ Babbitt, MJ Baber, RG Congalton Biological Conservation 123 (2), 139-149, 2005 | 246 | 2005 |
Murali Krishna GummaPrincipal Scientist &Cluster Leader – Geospatial Sciences and Big Data at ICRISAT (IRRI, IWMI, JNTU)Verified email at cgiar.org
