Global irrigated croplands cover approximately 20% of the total but consume about 70% of worldwide freshwater withdrawals. Despite this importance, large uncertainties on the effects of irrigation on water use and the environment remain. In this research focus, we are using big geospatial data, data-driven models, and machine learning to know the following information for improved water management from field to global scales:

(1) where irrigated croplands are and how they change over time

(2) what are the techniques used for irrigation and their efficiencies

(3) how much water is extracted and where the water is from

Related publications:

Zhang, L., Xie, Y., Zhu, X., Ma, Q., & Brocca, L., 2024. CIrrMap250: Annual maps of China’s irrigated cropland from 2000 to 2020 developed through multisource data integration. Earth System Science Data Discussions, 2024, 1-31. https://doi.org/10.5194/essd-16-5207-2024. 

Xie Y., Gibbs H., and Lark T.J., 2021. "LANID-US: annual 30-m resolution irrigation maps for the conterminous United States, 1997-2017." Earth System Science Data, 13(12), 5689-5710. https://doi.org/10.5194/essd-13-5689-2021. 

Xie Y. and Lark T.J., 2021. " Mapping annual irrigation from Landsat imagery and environmental variables across the conterminous United States." Remote Sensing of Environment, 260, 1-17. https://doi.org/10.1016/j.rse.2021.112445. 

Ren J., Shao Y., Wan H., and Xie Y., 2021. "A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat Analysis Ready Data." ISPRS Journal of Photogrammetry and Remote Sensing, 176, 69-82. https://doi.org/10.1016/j.isprsjprs.2021.04.007. 

Xie Y., Lark T. J., Brown J. F., & Gibbs H. K., 2019. "Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine." ISPRS Journal of Photogrammetry and Remote Sensing, 155, 136-149. https://doi.org/10.1016/j.isprsjprs.2019.07.005. 

The earth has undergone unprecedented urbanization in recent decades and the global development of land consumes some of our planet’s most productive agricultural lands. This conversion is usually irreversible—once urbanized, agricultural lands are very unlikely to be recultivated. Such losses of agricultural lands already pose a substantial threat to local and regional food security. It is projected that the trends of urbanization-induced cropland loss will continue if no actions are taken. Given an increasing population and its attendant growing need for food and fuel, protecting existing agricultural lands is critical, especially under the compounding stresses of climate change, extreme weather, armed conflicts, and public health crises such as the COVID-19 pandemic.

In this research focus, we are integrating multi-source geospatial data, land use science, and machine learning to understand complex agri-urban land use competitions with the aim of saving limited lands for both food security and sustainable urban development. More specifically, our research covers:

(1) map, monitor, and project urbanization and cropland dynamics

(2) understand the urbanization process of agriculture lands

(3) predict and protect croplands under threat of urbanization

(4) translate our scientific findings into actionable changes

Related publications:

Xie, Y., Spawn-Lee, S. A., Radeloff, V. C., Yin, H., Robertson, G. P., & Lark, T. J., 2024. Cropland abandonment between 1986 and 2018 across the United States: spatiotemporal patterns and current land uses. Environmental Research Letters, 19(4), 044009. https://doi.org/10.1088/1748-9326/ad2d12. 

Xie, Y., Hunter, M., Sorensen, A., Nogeire-McRae, T., Murphy, R., Suraci, J.P., Lischka, S., Lark, T.J., 2023. U.S. Farmland under Threat of Urbanization: Future Development Scenarios to 2040. Land, 12, 574. https://doi.org/10.3390/land12030574. 

Xie Y., Weng Q., and Fu P., 2019. "Temporal variations of artificial nighttime lights and their implications for urbanization in the conterminous United States, 2013–2017." Remote Sensing of Environment, 225: 160-174. https://doi.org/10.1016/j.rse.2019.03.008. 

Xie Y. and Weng Q., 2017. "Spatiotemporally enhancing time-series DMSP/OLS nighttime lights for mapping large-scale urban dynamics." ISPRS Journal of Photogrammetry and Remote Sensing 128: 1-15. https://doi.org/10.1016/j.isprsjprs.2017.03.003. 

Xie Y. and Weng Q., 2016. "Updating urban extents with nighttime light imagery by using an object-based thresholding method." Remote Sensing of Environment 187: 1-13. https://doi.org/10.1016/j.rse.2016.10.002.