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A Threshold Determination Method for High-Frequency Irrigation Detection Based on Error Propagation Principles

https://doi.org/10.2139/ssrn.6294258
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/2017gl075733openalex-snapshot
Irrigation Signals Detected From SMAP Soil Moisture Retrievals
inferredcites →https://doi.org/10.1002/hyp.5524openalex-snapshot
Water problems and hydrological research in the Yellow River and the Huai and Hai River basins of China
inferredcites →https://doi.org/10.1016/j.advwatres.2021.103910openalex-snapshot
A new dataset of global irrigation areas from 2001 to 2015
inferredcites →https://doi.org/10.1016/j.jhydrol.2009.07.031openalex-snapshot
Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation
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.2011.11.001openalex-snapshot
A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data
inferredcites →https://doi.org/10.1016/j.rse.2013.02.027openalex-snapshot
Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation
inferredcites →https://doi.org/10.1016/j.rse.2014.02.001openalex-snapshot
Landsat-8: Science and product vision for terrestrial global change research
inferredcites →https://doi.org/10.1016/j.rse.2016.02.019openalex-snapshot
Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data
inferredcites →https://doi.org/10.1016/j.rse.2017.10.030openalex-snapshot
Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data
inferredcites →https://doi.org/10.1016/j.rse.2017.10.046openalex-snapshot
Spatio-temporal fusion for daily Sentinel-2 images
inferredcites →https://doi.org/10.1016/j.rse.2020.111968openalex-snapshot
Landsat 9: Empowering open science and applications through continuity
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/10.1029/2020wr028378openalex-snapshot
Satellite‐Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy
inferredcites →https://doi.org/10.1038/s41597-022-01522-zopenalex-snapshot
Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products
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/10.1080/01431160802698919openalex-snapshot
Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium
inferredcites →https://doi.org/10.2166/wp.2017.127openalex-snapshot
Controlling groundwater over abstraction: state policies vs local practices in the Jordan highlands
inferredcites →https://doi.org/10.3390/rs12244102openalex-snapshot
Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California
inferredcites →https://doi.org/10.3390/rs13101960openalex-snapshot
Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images
inferredcites →https://doi.org/10.3390/rs14153801openalex-snapshot
Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images
inferredcites →https://doi.org/10.3390/s16081308openalex-snapshot
Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review
inferredcites →https://doi.org/10.3390/s21134328openalex-snapshot
Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data
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