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The T Index: Measuring the Reliability of Accuracy Estimates Obtained from Non-Probability Samples.. Remote. Sens., 12 (15): 2483 (2020)Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps., , , and . Remote Sensing, 7 (6): 7959-7986 (2015)To Blend or Not to Blend? A Framework for Nationwide Landsat-MODIS Data Selection for Crop Yield Prediction., , , , , , , and . Remote. Sens., 12 (10): 1653 (2020)A Unified Cropland Layer at 250 m for Global Agriculture Monitoring., , , , , , , , , and 6 other author(s). Data, 1 (1): 3 (2016)A multi-disciplinary perspective on emergent and future innovations in peer review., , , , , , , , , and 23 other author(s). F1000Research, (2017)Global Cropland Yield Monitoring with Gaussian Processes., , , , , , and . IGARSS, page 180-183. IEEE, (2021)Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images., , , , , , , and . Remote. Sens., 13 (11): 2197 (2021)Where can pixel counting area estimates meet user-defined accuracy requirements?, and . Int. J. Appl. Earth Obs. Geoinformation, (2017)Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network., and . CoRR, (2019)Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model., , , , and . Remote Sensing, 7 (12): 16204-16225 (2015)