Reference health
Quantification and Attribution of Spectral Variation in Irrigated Perennial Tree Crops
31/31 checkable references clean · checked 2026-10-06
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
26 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 31 checked references that resolve
resolves10.3390/rs15092420
Delineation of Orchard, Vineyard, and Olive Trees Based on Phenology Metrics Derived from Time Series of Sentinel-2
resolves10.1016/j.jrurstud.2017.12.026
Limits to adaptation: Reducing irrigation water in the Murray-Darling Basin dairy communities
resolves10.3390/atmos14101593
Adapting Almond Production to Climate Change through Deficit Irrigation and Foliar Kaolin Application in a Mediterranean Climate
resolves10.1007/s10661-022-10685-6
Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020
resolves10.1371/journal.pone.0287731
Building a community-based open harmonised reference data repository for global crop mapping
resolves10.3390/rs12010096
Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data
resolves10.3390/rs14112621
Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes
resolves10.3390/rs16030458
Irrigation Detection Using Sentinel-1 and Sentinel-2 Time Series on Fruit Tree Orchards
resolves10.1016/j.isprsjprs.2019.03.012
Automatic mapping of planting year for tree crops with Landsat satellite time series stacks
resolves10.1080/10106049.2015.1027292
An assessment of chlorophyll-<i>a</i>concentration spatio-temporal variation using Landsat satellite data, in a small tropical reservoir
resolves10.1002/2017GL074071
Annual Irrigation Dynamics in the U.S. Northern High Plains Derived from Landsat Satellite Data
resolves10.1007/s10668-021-01885-0
Assessing the spatial variation of cropping intensity using multi-temporal Sentinel-2 data by rule-based classification
resolves10.1016/j.rse.2017.06.031
Google Earth Engine: Planetary-scale geospatial analysis for everyone
resolves10.1080/15481603.2022.2088651
Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security
resolves10.1016/j.landusepol.2009.01.002
Policy directions for agricultural land use in Australia and other post-industrial economies
resolves10.1007/s11625-023-01298-0
Water trading as a tool to combat economic losses in agriculture under climate change
resolves10.1109/IGARSS.2005.1526319
Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+
resolves10.14445/22315381/IJETT-V69I8P217
Separability Analysis of The Band Combinations For Land Cover Classification of Satellite Images
resolves10.1016/j.eap.2020.01.001
Water allocation in Australia’s Murray–Darling Basin: Managing change under heightened uncertainty
resolves10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2
FITTING MULTIVARIATE MODELS TO COMMUNITY DATA: A COMMENT ON DISTANCE-BASED REDUNDANCY ANALYSIS
resolves10.1111/j.1469-8137.2010.03536.x
Sources of variability in canopy reflectance and the convergent properties of plants
resolves10.1111/gcb.12143
Evidence of current impact of climate change on life: a walk from genes to the biosphere
resolves10.1016/j.rse.2018.06.046
Multi-decade, multi-sensor time-series modelling—based on geostatistical concepts—to predict broad groups of crops
resolves10.3390/rs14235942
Potential of Time-Series Sentinel 2 Data for Monitoring Avocado Crop Phenology
resolves10.1080/00167223.1988.10649264
Applications of multivariate statistical analysis in remote sensing of agriculture
resolves10.1016/j.gloplacha.2012.06.004
Current and future irrigation water requirements in pan-Europe: An integrated analysis of socio-economic and climate scenarios
resolves10.1016/j.isprsjprs.2020.10.005
Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data
resolves10.5194/hess-26-827-2022
Untangling irrigation effects on maize water and heat stress alleviation using satellite data
The 26 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedRemotely-sensed surface temperature and vegetation status for the assessment of decadal change in the irrigated land cover of north-central Victoria
no DOI — not checkedAustralian Water Markets Report: 2021-2022 review and 2022-23 outlook
no DOI — not checkedPermutational Multivariate Analysis of Variance (PERMANOVA)
no DOI — not checkedref7
no DOI — not checkedref12
no DOI — not checkedScalable approach for high-resolution land cover: A case study in the Mediterranean Basin
no DOI — not checkedOn the application of remote sensing time series analysis for land cover mapping: Spectral indices for crops classification. The International Archives of the Photogrammetry
no DOI — not checkedref20
no DOI — not checkedEnhancing the accuracy and temporal transferability of irrigated cropping field classification using optical remote sensing imagery
no DOI — not checkedWater planning and hydro-climatic change in the Murray-Darling Basin
no DOI — not checkedMulti-crop classification using feature selectioncoupled machine learning classifiers based on spectral, textural and environmental features
no DOI — not checkedref32
no DOI — not checkedA pixel-based spectral matching method for mapping high-resolution irrigated areas using EVI time series
no DOI — not checkedref36
no DOI — not checkedDetecting the annual areal extent of sugarcane crops in Queensland
no DOI — not checkedUse of Sentinel-2 for forest classification in Mediterranean environments
no DOI — not checkedMulti-year time series transfer learning: Application of early crop classification
no DOI — not checkedIrrigated crop area data for the Lower Murray-Darling 2003 to 2021-Including analysis of the Edward/Kolety and Wakool river system
no DOI — not checkedFrom frequency to intensity -A new index for annual large-scale cropping intensity mapping
no DOI — not checked) data
no DOI — not checkedref51
no DOI — not checkedContinuous water monitoring network
no DOI — not checkedgeemap: A Python package for interactive mapping with Google Earth Engine
no DOI — not checkedref56
no DOI — not checkedref57
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. If Crossref’s record marks the work itself
retracted, withdrawn, or removed, the badge says so instead of showing clean. It says nothing
about the quality, findings, or importance of the work itself, and nothing about references
deposited without a DOI.
checked 2026-10-06 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2139/ssrn.5071980"><img src="https://citestamp.com/citestamped/10.2139/ssrn.5071980/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2139/ssrn.5071980)