| High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data M Singha, J Dong, G Zhang, X Xiao Scientific Data 6 (1), 26, 2019 | 186 | 2019 |
| Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine M Singha, J Dong, S Sarmah, N You, Y Zhou, G Zhang, R Doughty, ... ISPRS Journal of Photogrammetry and Remote Sensing 166, 278-293, 2020 | 148 | 2020 |
| An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India M Singha, B Wu, M Zhang Remote Sensing 8 (6), 479, 2016 | 85 | 2016 |
| Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data M Singha, B Wu, M Zhang Sensors 17 (1), 10, 2016 | 45 | 2016 |
| Conflation of expert and crowd reference data to validate global binary thematic maps F Waldner, A Schucknecht, M Lesiv, J Gallego, L See, A Pérez-Hoyos, ... Remote Sensing of Environment 221, 235-246, 2019 | 40 | 2019 |
| Mismatches between vegetation greening and primary productivity trends in South Asia–A satellite evidence S Sarmah, M Singha, J Wang, J Dong, PKD Burman, S Goswami, Y Ge, ... International Journal of Applied Earth Observation and Geoinformation 104 …, 2021 | 32 | 2021 |
| Assessing seasonal trends and variability of vegetation growth from NDVI3g, MODIS NDVI and EVI over South Asia S Sarmah, G Jia, A Zhang, M Singha Remote Sensing Letters 9 (12), 1195-1204, 2018 | 28 | 2018 |
| Satellite evidence on the trade-offs of the food-water–air quality nexus over the breadbasket of India M Singha, J Dong, Q Ge, G Metternicht, S Sarmah, G Zhang, R Doughty, ... Global Environmental Change 71, 102394, 2021 | 20 | 2021 |
| Incorporating crop phenological trajectory and texture for paddy rice detection with time series MODIS, HJ-1A and ALOS PALSAR imagery M Singha, S Sarmah European Journal of Remote Sensing 52 (1), 73-87, 2019 | 14 | 2019 |
| Study the effect of discontinuity adaptive MRF models in fuzzy based classifier M SINGHA University of Twente Faculty of Geo-Information and Earth Observation (ITC …, 2013 | 6 | 2013 |
| Importance of DA-MRF Models in Fuzzy Based Classifier M Singha, A Kumar, A Stein, PNL Raju, YVNK Murthy Journal of the Indian Society of Remote Sensing 43 (1), 27-35, 2015 | 4 | 2015 |
| Spatiotemporal pattern of the dynamics in area, production, and yield of Aus rice in Bangladesh and its response to droughts from 1980 to 2018 A Khondakar, J Dong, Z Li, X Deng, M Singha, M Rahman, Z Jin, S Wang, ... Journal of Geographical Sciences 32 (10), 2069-2084, 2022 | 2 | 2022 |
| Characterizing the 2020 Summer Floods in South China and Effects on Croplands X Chen, J Dong, L Huang, L Chen, Z Li, N You, M Singha, F Tao Available at SSRN 4232452, 0 | 2* | |
| Increasing optimum temperature of vegetation activity over the past four decades Y Wang, S Sarmah, M Singha, W Chen, Y Ge, LL Liang, S Goswami, ... Earth's Future 12 (10), e2024EF004489, 2024 | 1 | 2024 |
| Mapping spatiotemporal pattern of dry-season Boro paddy rice planting areas in Bangladesh from 1987-2018 using Google Earth Engine and Landsat archives M Singha, J Dong, S Sarmah AGU Fall Meeting 2019, 2019 | | 2019 |
| CLIMATE SENSITIVITY OF SEASONAL VEGETATION ANOMALIES OVER ECO-REGIONS OF SOUTH ASIA S Sarmah, S Niu, G Jia, M Singha AGU Fall Meeting 2019, 2019 | | 2019 |
| High Resolution Operational Paddy Rice Mapping using Sentinel-1 SAR, One-Class Support Vector Machine and Google Earth Engine in Cloud-Prone Region M Singha, J Dong AGU Fall Meeting Abstracts, 2018 | | 2018 |
| Understanding the Seasonal Greenness Trends and Controls in South Asia Using Satellite Based Observations S Sarmah, G Jia, A Zhang, M Singha AGU Fall Meeting Abstracts, 2017 | | 2017 |
| Paddy Rice Mapping Based on Phenological and Temporal Features M Singha 中国科学院大学 (中国科学院遥感与数字地球研究所), 2017 | | 2017 |
Miao ZhangAerospace Information Research Institute, Chinese Academy of Sciencesبريد إلكتروني تم التحقق منه على radi.ac.cn
