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Annual Irrigation Dynamics in the U.S. Northern High Plains Derived from Landsat Satellite Data

https://doi.org/10.1002/2017gl074071

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inferred← citeshttps://doi.org/10.1109/access.2021.3049852openalex-snapshot

Study on the Detection of Soil Water Content Based on the Pulsed Acoustic Wave (PAW) Method

inferred← citeshttps://doi.org/10.1109/i-smac58438.2023.10290343openalex-snapshot

Groundwater Level Modeling Artificial Intelligence Approach of Features and Abilities

inferred← citeshttps://doi.org/10.1109/jstars.2020.3021052openalex-snapshot

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

inferred← citeshttps://doi.org/10.1109/jstars.2020.3029434openalex-snapshot

Monitoring Crop Growth During the Period of the Rapid Spread of COVID-19 in China by Remote Sensing

inferred← citeshttps://doi.org/10.1109/jstars.2022.3222884openalex-snapshot

Classification of Different Irrigation Systems at Field Scale Using Time-Series of Remote Sensing Data

inferred← citeshttps://doi.org/10.1109/tgrs.2022.3175635openalex-snapshot

A Deep Learning Approach to Mapping Irrigation Using Landsat: IrrMapper U-Net

inferred← citeshttps://doi.org/10.1111/1752-1688.12722openalex-snapshot

Transition Pathways to Sustainable Agricultural Water Management: A Review of Integrated Modeling Approaches

inferred← citeshttps://doi.org/10.1111/1752-1688.70076openalex-snapshot

Using Remote Sensing and Machine Learning to Determine Past, Current and Future Crop Water Use From the Nubian Sandstone Aquifer

inferred← citeshttps://doi.org/10.1111/gcb.14632openalex-snapshot

Dissecting the nonlinear response of maize yield to high temperature stress with model‐data integration

inferred← citeshttps://doi.org/10.1130/abs/2020am-355709openalex-snapshot

INFLUENCE OF IRRIGATION DRIVERS USING BOOSTED REGRESSION TREES: KANSAS HIGH PLAINS

inferred← citeshttps://doi.org/10.1175/bams-d-19-0316.1openalex-snapshot

Closing the Water Cycle from Observations across Scales: Where Do We Stand?

inferred← citeshttps://doi.org/10.13031/aea.14693openalex-snapshot

A Geographical Survey of Center Pivot Irrigation Systems in the Central and Southern High Plains Aquifer Region of the United States

inferred← citeshttps://doi.org/10.14358/pers.25-00081r3openalex-snapshot

Landsat-Derived Rainfed and Irrigated-Area Product for Conterminous United States for the Year 2020 (LRIP30 CONUS 2020) Using Supervised and Unsupervised Machine Learning on the Cloud

inferred← citeshttps://doi.org/10.21203/rs.3.rs-1708393/v1openalex-snapshot

Regional groundwater sustainability assessment through enhancing conveyance and application efficiencies of irrigation water

inferred← citeshttps://doi.org/10.2139/ssrn.4821978openalex-snapshot

A Geospatial Analysis of Vegetation Cover Change and Watershed-Scale Biophysical Impacts in a Low-Income Country Context

inferred← citeshttps://doi.org/10.2139/ssrn.5352243openalex-snapshot

From National to Global: Mapping Global Center-Pivot Irrigated Cropland with Knowledge Guided Data Augmentation

inferred← citeshttps://doi.org/10.2139/ssrn.6175590openalex-snapshot

From national to global: Mapping global center-pivot irrigated cropland with knowledge-guided data augmentation

inferred← citeshttps://doi.org/10.2139/ssrn.6184138openalex-snapshot

Crop-specific differentiation between irrigated and rainfed fields using HLS optical and Landsat thermal time series

inferred← citeshttps://doi.org/10.2139/ssrn.6210413openalex-snapshot

From national to global: Mapping global center-pivot irrigated cropland with knowledge-guided data augmentation

inferred← citeshttps://doi.org/10.31223/osf.io/hqrmuopenalex-snapshot

Streamflow depletion estimation for conjunctive water management in a heavily-stressed aquifer using analytical depletion functions

inferred← citeshttps://doi.org/10.31223/x5c932openalex-snapshot

Majority of 21st century global irrigation expansion has been in water stressed regions

inferred← citeshttps://doi.org/10.3133/fs20223056openalex-snapshot

Nebraska and Landsat

inferred← citeshttps://doi.org/10.3133/ofr20231037openalex-snapshot

Grassland management priorities for the North Central Region

inferred← citeshttps://doi.org/10.33448/rsd-v10i4.13858openalex-snapshot

Outorgas consuntivas: análise hídrica no Município de Unaí - MG

inferred← citeshttps://doi.org/10.3389/frwa.2021.786016openalex-snapshot

Center Pivot Irrigation Systems and Where to Find Them: A Deep Learning Approach to Provide Inputs to Hydrologic and Economic Models

inferred← citeshttps://doi.org/10.3389/fsufs.2022.1053921openalex-snapshot

Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions

inferred← citeshttps://doi.org/10.3390/agriculture14101693openalex-snapshot

Identification and Monitoring of Irrigated Areas in Arid Areas Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm

inferred← citeshttps://doi.org/10.3390/land10040394openalex-snapshot

Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017

inferred← citeshttps://doi.org/10.3390/rs10050760openalex-snapshot

An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale

inferred← citeshttps://doi.org/10.3390/rs11030232openalex-snapshot

Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning

inferred← citeshttps://doi.org/10.3390/rs11030370openalex-snapshot

Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data

inferred← citeshttps://doi.org/10.3390/rs12142328openalex-snapshot

IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.

inferred← citeshttps://doi.org/10.3390/rs13030403openalex-snapshot

Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine

inferred← citeshttps://doi.org/10.3390/rs13091659openalex-snapshot

Climate and Socioeconomic Factors Drive Irrigated Agriculture Dynamics in the Lower Colorado River Basin

inferred← citeshttps://doi.org/10.3390/rs13091727openalex-snapshot

Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture

inferred← citeshttps://doi.org/10.3390/rs13142667openalex-snapshot

Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region

inferred← citeshttps://doi.org/10.3390/rs13204103openalex-snapshot

Proximal Gamma-Ray Spectroscopy: An Effective Tool to Discern Rain from Irrigation

inferred← citeshttps://doi.org/10.3390/rs13204112openalex-snapshot

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

inferred← citeshttps://doi.org/10.3390/rs14040997openalex-snapshot

Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery

inferred← citeshttps://doi.org/10.3390/rs14133004openalex-snapshot

Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains

inferred← citeshttps://doi.org/10.3390/rs14133056openalex-snapshot

Detection of Irrigated Permanent Grasslands with Sentinel-2 Based on Temporal Patterns of the Leaf Area Index (LAI)

inferred← citeshttps://doi.org/10.3390/rs14143253openalex-snapshot

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

inferred← citeshttps://doi.org/10.3390/rs15164071openalex-snapshot

Mapping Irrigated Croplands from Sentinel-2 Images Using Deep Convolutional Neural Networks

inferred← citeshttps://doi.org/10.3390/rs15164081openalex-snapshot

Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model

inferred← citeshttps://doi.org/10.3390/rs9101072openalex-snapshot

The Geometry of Large Tundra Lakes Observed in Historical Maps and Satellite Images

inferred← citeshttps://doi.org/10.3390/su18178771openalex-snapshot

A Soil Moisture Profile Response-Driven Framework for Estimating Irrigation Water Use Under Spatial Allocation Constraints

inferred← citeshttps://doi.org/10.3390/w11071458openalex-snapshot

Increased Dependence on Irrigated Crop Production Across the CONUS (1945–2015)

inferred← citeshttps://doi.org/10.3390/w14172628openalex-snapshot

A New Method to Map Spring Irrigated Areas Using MODIS LST Products and Ancillary Data in an Agricultural District of Northwest China

inferred← citeshttps://doi.org/10.3390/w18101233openalex-snapshot

Machine Learning-Based Mapping of Irrigated Farmland Dynamics in the Lower Yellow River Basin

inferred← citeshttps://doi.org/10.34133/remotesensing.0403openalex-snapshot

Advancing Sustainable Development Goals through Earth Observation Satellite Data: Current Insights and Future Directions

inferred← citeshttps://doi.org/10.4081/jae.2021.1265openalex-snapshot

Comparing Sentinel-1, Sentinel-2, and Landsat-8 data in the early recognition of irrigated areas in central Italy

inferred← citeshttps://doi.org/10.48550/arxiv.2103.03278openalex-snapshot

A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net

inferred← citeshttps://doi.org/10.5194/essd-13-5689-2021openalex-snapshot

Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017

inferred← citeshttps://doi.org/10.5194/essd-16-5207-2024openalex-snapshot

CIrrMap250: annual maps of China's irrigated cropland from 2000 to 2020 developed through multisource data integration

inferred← citeshttps://doi.org/10.5194/essd-2021-207-ac1openalex-snapshot

Reply on RC1

inferred← citeshttps://doi.org/10.5194/essd-2021-207-ac2openalex-snapshot

Reply on RC2

inferred← citeshttps://doi.org/10.5194/essd-2024-2-ac1openalex-snapshot

Reply on RC1

inferred← citeshttps://doi.org/10.5194/essd-2024-2-rc1openalex-snapshot

Comment on essd-2024-2

inferred← citeshttps://doi.org/10.5194/essd-2024-2-rc2openalex-snapshot

Comment on essd-2024-2

inferred← citeshttps://doi.org/10.5194/hess-2020-627-ac1openalex-snapshot

Reply on RC1

inferred← citeshttps://doi.org/10.5194/hess-2020-627-ac2openalex-snapshot

Reply on RC2

inferred← citeshttps://doi.org/10.5194/hess-2020-627-ac3openalex-snapshot

Reply on RC2

inferred← citeshttps://doi.org/10.5194/hess-2020-627-ac4openalex-snapshot

Reply on RC2

inferred← citeshttps://doi.org/10.5194/hess-2020-627-ac5openalex-snapshot

Reply on RC3

inferred← citeshttps://doi.org/10.5194/hess-2020-627-ac6openalex-snapshot

Reply on CC1

inferred← citeshttps://doi.org/10.5194/hess-2020-627-rc1openalex-snapshot

Comment on hess-2020-627

inferred← citeshttps://doi.org/10.5194/hess-2020-627-rc2openalex-snapshot

Comment on hess-2020-627

inferred← citeshttps://doi.org/10.5194/hess-2020-627-rc3openalex-snapshot

Comment on hess-2020-627

inferred← citeshttps://doi.org/10.5194/hess-23-897-2019openalex-snapshot

Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data

inferred← citeshttps://doi.org/10.5194/hess-25-1827-2021openalex-snapshot

Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

inferred← citeshttps://doi.org/10.5194/hess-26-827-2022openalex-snapshot

Untangling irrigation effects on maize water and heat stress alleviation using satellite data

inferred← citeshttps://doi.org/10.5194/isprs-archives-xliii-b2-2020-697-2020openalex-snapshot

MAPPING IRRIGATED AREAS USING RANDOM FOREST BASED ON GF-1 MULTI-SPECTRAL DATA

inferred← citeshttps://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-33-2023openalex-snapshot

IRRIGATED AGRICULTURE MAPPING IN A SEMI-ARID REGION IN BRAZIL BASED ON THE USE OF SENTINEL-2 DATA AND RANDOM FOREST ALGORITHM

inferred← citeshttps://doi.org/10.70675/2e30046dz752az407az8aa6zec52217c3e1eopenalex-snapshot

Approche transdisciplinaire du diagnostic des pathogènes anciens

inferred← citeshttps://doi.org/10.70675/62826a92z584bz4433z8bc1z3e0174cc82efopenalex-snapshot

Apports des données radar à haute répétitivité et à haute résolution du capteur Sentinel-1 pour la caractérisation de l'état hydrique des surfaces agricoles dans les régions sud-méditerranéennes

inferred← citeshttps://doi.org/10.70675/84efc384zb70bz479bzb32dz558796f23662openalex-snapshot

Characterization and mapping of irrigated crop classes at territory level

inferred← citeshttps://doi.org/10.70675/f36bf8dbzfb3dz4b7az8dfez7343c0b5b9baopenalex-snapshot

Mapping Irrigated Areas using Radar and Optical Remote Sensing Data

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