| Smallholder maize area and yield mapping at national scales with Google Earth Engine Z Jin, G Azzari, C You, S Di Tommaso, S Aston, M Burke, DB Lobell Remote sensing of environment 228, 115-128, 2019 | 354 | 2019 |
| Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation KB Byrd, JL O'Connell, S Di Tommaso, M Kelly Remote Sensing of Environment 149, 166-180, 2014 | 139 | 2014 |
| Changes in the drought sensitivity of US maize yields DB Lobell, JM Deines, SD Tommaso Nature Food 1 (11), 729-735, 2020 | 115 | 2020 |
| Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive S Wang, S Di Tommaso, JM Deines, DB Lobell Scientific Data 7 (1), 307, 2020 | 110 | 2020 |
| Mapping crop types in southeast India with smartphone crowdsourcing and deep learning S Wang, S Di Tommaso, J Faulkner, T Friedel, A Kennepohl, R Strey, ... Remote Sensing 12 (18), 2957, 2020 | 88 | 2020 |
| Mapping forests with Lidar provides flexible, accurate data with many uses M Kelly, SD Tommaso California Agriculture 69 (1), 2015 | 73 | 2015 |
| Globally ubiquitous negative effects of nitrogen dioxide on crop growth DB Lobell, S Di Tommaso, JA Burney Science advances 8 (22), eabm9909, 2022 | 61 | 2022 |
| Sight for sorghums: Comparisons of satellite-and ground-based sorghum yield estimates in Mali DB Lobell, S Di Tommaso, C You, IY Djima, M Burke, T Kilic Remote Sensing 12 (1), 100, 2019 | 54 | 2019 |
| Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops S Di Tommaso, S Wang, DB Lobell Environmental Research Letters 16 (12), 125002, 2021 | 37 | 2021 |
| Impact of error in LiDAR-derived canopy height and canopy base height on modeled wildfire behavior in the Sierra Nevada, California, USA M Kelly, Y Su, S Di Tommaso, DL Fry, BM Collins, SL Stephens, Q Guo Remote Sensing 10 (1), 10, 2017 | 31 | 2017 |
| Twice is nice: the benefits of two ground measures for evaluating the accuracy of satellite-based sustainability estimates DB Lobell, S Di Tommaso, M Burke, T Kilic Remote Sensing 13 (16), 3160, 2021 | 14 | 2021 |
| Annual field-scale maps of tall and short crops at the global scale using GEDI and Sentinel-2 S Di Tommaso, S Wang, V Vajipey, N Gorelick, R Strey, DB Lobell Remote Sensing 15 (17), 4123, 2023 | 10 | 2023 |
| Changes in the drought sensitivity of US maize yields. Nature Food, 1, 729–735 D Lobell, J Deines, S Tommaso | 5 | 2020 |
| Mapping sugarcane globally at 10 m resolution using Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 S Di Tommaso, S Wang, R Strey, DB Lobell Earth System Science Data 16 (10), 4931-4947, 2024 | 1 | 2024 |
| Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2 S Di Tommaso, S Wang, R Strey, DB Lobell Earth System Science Data Discussions 2024, 1-26, 2024 | 1 | 2024 |
| Mapping Crop Types in India with Crowdsourced Data and Deep Learning S Wang, S Di Tommaso, J Faulkner, T Friedel, A Kennepohl, R Strey, ... AGU Fall Meeting Abstracts 2019, IN42A-03, 2019 | 1 | 2019 |
| Running for Cover: Understanding the Causes of Short-and Long-Term Yield Effects of Cover Crop Adoption in the US S Di Tommaso, K Guan, Q Zhou, J Specht, JM Deines, DB Lobell AGU Fall Meeting Abstracts 2023, GC51B-06, 2023 | | 2023 |
| Scaling up in Earth Engine JM Deines, S Di Tommaso, N Clinton, N Gorelick Cloud-Based Remote Sensing with Google Earth Engine: Fundamentals and …, 2023 | | 2023 |
| Global mapping of tall crops in regions without field labels using GEDI and Sentinel-2 S Wang, S Di Tommaso, V Vajipey, DB Lobell AGU Fall Meeting Abstracts 2022, GC22A-05, 2022 | | 2022 |
| Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops S Wang, S Di Tommaso, D Lobell AGU Fall Meeting Abstracts 2021, GC34B-03, 2021 | | 2021 |