| A review of assessing the accuracy of classifications of remotely sensed data RG Congalton Remote sensing of environment 37 (1), 35-46, 1991 | 11005 | 1991 |
| Assessing the accuracy of remotely sensed data: principles and practices RG Congalton, K Green CRC press, 2019 | 9861 | 2019 |
| Accuracy assessment: a user’s perspective M Story, RG Congalton Photogrammetric Engineering and remote sensing 52 (3), 397-399, 1986 | 2354 | 1986 |
| Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. RG Congalton, RG Oderwald, RA Mead | 1220 | 1983 |
| A quantitative method to test for consistency and correctness in photointerpretation RG Congalton, RA Mead PHOTOCRAMMETRIC ENGNEERING AND REMOTE SENSI, 1983 | 836 | 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 | 823 | 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 | 764 | 1998 |
| Accuracy assessment and validation of remotely sensed and other spatial information RG Congalton International journal of wildland fire 10 (4), 321-328, 2001 | 645 | 2001 |
| 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 | 606 | 2018 |
| 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 | 603 | 2017 |
| A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data. RG Congalton | 536 | 1988 |
| 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 | 535 | 1998 |
| Remote sensing and geographic information system data integration: Error sources and research issues. RS Lunetta, RG Congalton, LK Fenstermaker, JR Jensen, KC McGwire, ... Photogrammetric engineering and remote sensing, 1991 | 523 | 1991 |
| 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 | 478 | 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 | 440 | 2003 |
| 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 | 426 | 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 | 420 | 2014 |
| A practical look at the sources of confusion in error matrix generation. RG Congalton, K Green | 345 | 1993 |
| Using spatial autocorrelation analysis to explore the errors in maps generated from remotely sensed data. RG Congalton | 313 | 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 | 260 | 2005 |
Murali Krishna GummaTheme Leader & Principal Scientist–Digtal Agri.,&Geospatial Sciences at ICRISAT (IRRI, IWMI, JNTU)Verified email at cgiar.org
