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

https://doi.org/10.2139/ssrn.6184138
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/2017gl074071openalex-snapshot
Annual Irrigation Dynamics in the U.S. Northern High Plains Derived from Landsat Satellite Data
inferredcites →https://doi.org/10.1016/j.advwatres.2022.104130openalex-snapshot
Double-scale analysis on the detectability of irrigation signals from remote sensing soil moisture over an area with complex topography in central Italy
inferredcites →https://doi.org/10.1016/j.agwat.2022.107975openalex-snapshot
A stand-alone remote sensing approach based on the use of the optical trapezoid model for detecting the irrigated areas
inferredcites →https://doi.org/10.1016/j.agwat.2024.108988openalex-snapshot
Mapping of irrigated vineyard areas through the use of machine learning techniques and remote sensing
inferredcites →https://doi.org/10.1016/j.compag.2024.109239openalex-snapshot
Understanding the potentials of early-season crop type mapping by using Landsat-8, Sentinel-1/2, and GF-1/6 data
inferredcites →https://doi.org/10.1016/j.jag.2015.01.014openalex-snapshot
Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data
inferredcites →https://doi.org/10.1016/j.jhydrol.2021.126129openalex-snapshot
Detecting and mapping irrigated areas in a Mediterranean environment by using remote sensing soil moisture and a land surface model
inferredcites →https://doi.org/10.1016/j.rse.2008.04.010openalex-snapshot
A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US
inferredcites →https://doi.org/10.1016/j.rse.2011.05.028openalex-snapshot
GMES Sentinel-1 mission
inferredcites →https://doi.org/10.1016/j.rse.2014.04.008openalex-snapshot
Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI
inferredcites →https://doi.org/10.1016/j.rse.2014.08.016openalex-snapshot
Dynamic identification of summer cropping irrigated areas in a large basin experiencing extreme climatic variability
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.04.365openalex-snapshot
Detecting global irrigated areas by using satellite and reanalysis products
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.135984openalex-snapshot
Irrigation cooling effect on land surface temperature across China based on satellite observations
inferredcites →https://doi.org/10.1016/s0034-4257(01)00191-2openalex-snapshot
Detecting vegetation leaf water content using reflectance in the optical domain
inferredcites →https://doi.org/10.1038/s44221-024-00206-9openalex-snapshot
Half of twenty-first century global irrigation expansion has been in water-stressed regions
inferredcites →https://doi.org/10.1038/sdata.2016.118openalex-snapshot
Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015
inferredcites →https://doi.org/10.1073/pnas.1116437108openalex-snapshot
Global food demand and the sustainable intensification of agriculture
inferredcites →https://doi.org/10.1080/01431161.2018.1433343openalex-snapshot
Implementation of machine-learning classification in remote sensing: an applied review
inferredcites →https://doi.org/10.14358/pers.78.8.815openalex-snapshot
Mapping Crop Types, Irrigated Areas, and Cropping Intensities in Heterogeneous Landscapes of Southern India Using Multi-Temporal Medium-Resolution Imagery
inferredcites →https://doi.org/10.2134/agronj2015.0602openalex-snapshot
Profit‐Maximizing Irrigation for Cotton on High Available Water Holding Capacity Soils
inferredcites →https://doi.org/10.3390/rs12050854openalex-snapshot
Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
inferredcites →https://doi.org/10.3390/rs15245647openalex-snapshot
Mapping Small-Scale Irrigation Areas Using Expert Decision Rules and the Random Forest Classifier in Northern Ethiopia
inferredcites →https://doi.org/10.5194/hess-22-1119-2018openalex-snapshot
A global approach to estimate irrigated areas – a comparison between different data and statistics
inferredcites →https://doi.org/10.5194/hess-29-1483-2025openalex-snapshot
Identifying irrigated areas using land surface temperature and hydrological modelling: application to the Rhine basin
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