APA (7th ed.) Citation

Gumma, M. K., Thenkabail, P. S., Panjala, P., Teluguntla, P., Yamano, T., & Mohammed, I. (2022). Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security. GIScience and remote sensing, 59(1), 1048-1077. https://doi.org/10.1080/15481603.2022.2088651

Chicago Style (17th ed.) Citation

Gumma, Murali Krishna, Prasad S. Thenkabail, Pranay Panjala, Pardhasaradhi Teluguntla, Takashi Yamano, and Ismail Mohammed. "Multiple Agricultural Cropland Products of South Asia Developed Using Landsat-8 30 M and MODIS 250 M Data Using Machine Learning on the Google Earth Engine (GEE) Cloud and Spectral Matching Techniques (SMTs) in Support of Food and Water Security." GIScience and Remote Sensing 59, no. 1 (2022): 1048-1077. https://doi.org/10.1080/15481603.2022.2088651.

MLA (9th ed.) Citation

Gumma, Murali Krishna, et al. "Multiple Agricultural Cropland Products of South Asia Developed Using Landsat-8 30 M and MODIS 250 M Data Using Machine Learning on the Google Earth Engine (GEE) Cloud and Spectral Matching Techniques (SMTs) in Support of Food and Water Security." GIScience and Remote Sensing, vol. 59, no. 1, 2022, pp. 1048-1077, https://doi.org/10.1080/15481603.2022.2088651.