Articles | Volume 13, issue 12
https://doi.org/10.5194/essd-13-5689-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-5689-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Dec 2021
Data description paper |
| 08 Dec 2021
| 08 Dec 2021
Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017
Nelson Institute Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, Madison, 53726, USA
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, 53726, USA
Holly K. Gibbs
Nelson Institute Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, Madison, 53726, USA
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, 53726, USA
Department of Geography, University of Wisconsin-Madison, Madison, 53706, USA
Tyler J. Lark
Nelson Institute Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, Madison, 53726, USA
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, 53726, USA
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- Mapping Irrigated Areas Based on Remotely Sensed Crop Phenology and Soil Moisture W. Zuo et al. 10.3390/agronomy13061556
- Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields S. Solgi et al. 10.1016/j.agwat.2023.108226
- Global drivers of local water stresses and global responses to local water policies in the United States I. Haqiqi et al. 10.1088/1748-9326/acd269
- Improving crop-specific groundwater use estimation in the Mississippi Alluvial Plain: Implications for integrated remote sensing and machine learning approaches in data-scarce regions S. Majumdar et al. 10.1016/j.ejrh.2024.101674
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- Cropland abandonment between 1986 and 2018 across the United States: spatiotemporal patterns and current land uses Y. Xie et al. 10.1088/1748-9326/ad2d12
- Irrigation by Crop in the Continental United States From 2008 to 2020 P. Ruess et al. 10.1029/2022WR032804
- An attention-enhanced spatial–temporal high-resolution network for irrigated area mapping using multitemporal Sentinel-2 images W. Li et al. 10.1016/j.jag.2024.104040
- Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products C. Zhang et al. 10.1038/s41597-022-01522-z
- Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model Q. Liu et al. 10.1175/AIES-D-22-0002.1
- Alleviating water scarcity by optimizing crop mixes B. Richter et al. 10.1038/s44221-023-00155-9
- Disentangling contributions to past and future trends in US surface soil moisture L. Vargas Zeppetello et al. 10.1038/s44221-024-00193-x
- Comparative evaluation of the accuracy of mapping irrigated areas using sentinel 1 images in the Bilate and Gumara watersheds, Ethiopia A. Yimer et al. 10.1080/23311916.2024.2357728
- Half of twenty-first century global irrigation expansion has been in water-stressed regions P. Mehta et al. 10.1038/s44221-024-00206-9
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- Influence of Irrigation on Diurnal Mesoscale Circulations: Results From GRAINEX C. Phillips et al. 10.1029/2021GL096822
- The first fine-resolution mapping of contour-levee irrigation using deep Bi-Stream convolutional neural networks L. Liang et al. 10.1016/j.jag.2021.102631
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25 citations as recorded by crossref.
- CIrrMap250: annual maps of China's irrigated cropland from 2000 to 2020 developed through multisource data integration L. Zhang et al. 10.5194/essd-16-5207-2024
- The critical benefits of snowpack insulation and snowmelt for winter wheat productivity P. Zhu et al. 10.1038/s41558-022-01327-3
- High resolution annual irrigation water use maps in China based-on input variables selection and convolutional neural networks J. Zhang et al. 10.1016/j.jclepro.2023.136974
- Mapping of irrigated vineyard areas through the use of machine learning techniques and remote sensing E. López-Pérez et al. 10.1016/j.agwat.2024.108988
- New water accounting reveals why the Colorado River no longer reaches the sea B. Richter et al. 10.1038/s43247-024-01291-0
- Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change A. Talib et al. 10.1016/j.jhydrol.2024.131525
- A review of globally available data sources for modelling the Water-Energy-Food Nexus J. Lodge et al. 10.1016/j.earscirev.2023.104485
- Remote sensing of irrigation: Research trends and the direction to next-generation agriculture through data-driven scientometric analysis V. Manivasagam 10.1016/j.wasec.2023.100161
- A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images H. Chen et al. 10.1016/j.rse.2023.113679
- Mapping Irrigated Areas Based on Remotely Sensed Crop Phenology and Soil Moisture W. Zuo et al. 10.3390/agronomy13061556
- Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields S. Solgi et al. 10.1016/j.agwat.2023.108226
- Global drivers of local water stresses and global responses to local water policies in the United States I. Haqiqi et al. 10.1088/1748-9326/acd269
- Improving crop-specific groundwater use estimation in the Mississippi Alluvial Plain: Implications for integrated remote sensing and machine learning approaches in data-scarce regions S. Majumdar et al. 10.1016/j.ejrh.2024.101674
- IrriMap_CN: Annual irrigation maps across China in 2000–2019 based on satellite observations, environmental variables, and machine learning C. Zhang et al. 10.1016/j.rse.2022.113184
- Cropland abandonment between 1986 and 2018 across the United States: spatiotemporal patterns and current land uses Y. Xie et al. 10.1088/1748-9326/ad2d12
- Irrigation by Crop in the Continental United States From 2008 to 2020 P. Ruess et al. 10.1029/2022WR032804
- An attention-enhanced spatial–temporal high-resolution network for irrigated area mapping using multitemporal Sentinel-2 images W. Li et al. 10.1016/j.jag.2024.104040
- Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products C. Zhang et al. 10.1038/s41597-022-01522-z
- Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model Q. Liu et al. 10.1175/AIES-D-22-0002.1
- Alleviating water scarcity by optimizing crop mixes B. Richter et al. 10.1038/s44221-023-00155-9
- Disentangling contributions to past and future trends in US surface soil moisture L. Vargas Zeppetello et al. 10.1038/s44221-024-00193-x
- Comparative evaluation of the accuracy of mapping irrigated areas using sentinel 1 images in the Bilate and Gumara watersheds, Ethiopia A. Yimer et al. 10.1080/23311916.2024.2357728
- Half of twenty-first century global irrigation expansion has been in water-stressed regions P. Mehta et al. 10.1038/s44221-024-00206-9
- Climate-driven interannual variability in subnational irrigation areas across Europe W. Zhu & S. Siebert 10.1038/s43247-024-01721-z
- Mapping irrigated croplands in China using a synergetic training sample generating method, machine learning classifier, and Google Earth Engine C. Zhang et al. 10.1016/j.jag.2022.102888
3 citations as recorded by crossref.
- Influence of Irrigation on Diurnal Mesoscale Circulations: Results From GRAINEX C. Phillips et al. 10.1029/2021GL096822
- The first fine-resolution mapping of contour-levee irrigation using deep Bi-Stream convolutional neural networks L. Liang et al. 10.1016/j.jag.2021.102631
- Remote sensing monitoring of irrigated area in the non-growth season and of water consumption analysis in a large-scale irrigation district H. Li et al. 10.1016/j.agwat.2024.109020
Latest update: 13 Dec 2024
Short summary
We created 30 m resolution annual irrigation maps covering the conterminous US for the period of 1997–2017, together with derivative products and ground reference data. The products have several improvements over other data, including field-level details of change and frequency, an annual time step, a collection of ~ 10 000 ground reference locations for the eastern US, and improved mapping accuracy of over 90 %, especially in the east compared to others of 50 % to 80 %.
We created 30 m resolution annual irrigation maps covering the conterminous US for the period of...
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