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Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-29711-1openalex-snapshot
Remote Sensing Digital Image Analysis: An Introduction
inferredcites →https://doi.org/10.1007/978-3-662-03978-6openalex-snapshot
Remote Sensing Digital Image Analysis
inferredcites →https://doi.org/10.1016/0034-4257(91)90009-uopenalex-snapshot
Potentials and limits of vegetation indices for LAI and APAR assessment
inferredcites →https://doi.org/10.1016/0034-4257(94)90018-3openalex-snapshot
Development of vegetation and soil indices for MODIS-EOS
inferredcites →https://doi.org/10.1016/j.compag.2004.06.003openalex-snapshot
Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia
inferredcites →https://doi.org/10.1016/j.rse.2004.05.002openalex-snapshot
Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Par� State, Brazil
inferredcites →https://doi.org/10.1016/j.rse.2004.08.002openalex-snapshot
Cropland distributions from temporal unmixing of MODIS data
inferredcites →https://doi.org/10.1016/j.rse.2005.08.011openalex-snapshot
On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification
inferredcites →https://doi.org/10.1016/s0034-4257(00)00150-4openalex-snapshot
Optical–Biophysical Relationships of Vegetation Spectra without Background Contamination
inferredcites →https://doi.org/10.1016/s0034-4257(02)00079-2openalex-snapshot
Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data
inferredcites →https://doi.org/10.1016/s0034-4257(02)00081-0openalex-snapshot
Detection of land cover changes using MODIS 250 m data
inferredcites →https://doi.org/10.1016/s0034-4257(02)00083-4openalex-snapshot
Special issue on the moderate resolution imaging spectroradiometer (MODIS): a new generation of land surface monitoring
inferredcites →https://doi.org/10.1016/s0034-4257(02)00085-8openalex-snapshot
Achieving sub-pixel geolocation accuracy in support of MODIS land science
inferredcites →https://doi.org/10.1016/s0034-4257(02)00096-2openalex-snapshot
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
inferredcites →https://doi.org/10.1016/s0034-4257(02)00135-9openalex-snapshot
Monitoring vegetation phenology using MODIS
inferredcites →https://doi.org/10.1016/s0034-4257(03)00054-3openalex-snapshot
Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series
inferredcites →https://doi.org/10.1016/s0034-4257(96)00112-5openalex-snapshot
A comparison of vegetation indices over a global set of TM images for EOS-MODIS
inferredcites →https://doi.org/10.1016/s0308-521x(98)00026-2openalex-snapshot
The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review
inferredcites →https://doi.org/10.1017/s0376892900029854openalex-snapshot
The International Geosphere-Biosphere Programme (IGBP): A Study of Global Change
inferredcites →https://doi.org/10.1073/pnas.0606377103openalex-snapshot
Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon
inferredcites →https://doi.org/10.1080/014311600210083openalex-snapshot
Global and regional land cover characterization from satellite data: an introduction to the Special Issue
inferredcites →https://doi.org/10.1080/014311600210092openalex-snapshot
Land cover mapping of large areas from satellites: Status and research priorities
inferredcites →https://doi.org/10.1080/014311600210191openalex-snapshot
Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data
inferredcites →https://doi.org/10.1080/014311600210209openalex-snapshot
Global land cover classification at 1 km spatial resolution using a classification tree approach
inferredcites →https://doi.org/10.1080/014311600210254openalex-snapshot
The 250 m global land cover change product from the Moderate Resolution Imaging Spectroradiometer of NASA's Earth Observing System
inferredcites →https://doi.org/10.1080/01431168808954847openalex-snapshot
Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations
inferredcites →https://doi.org/10.1080/01431169408954345openalex-snapshot
NDVI-derived land cover classifications at a global scale
inferredcites →https://doi.org/10.1080/014311697217099openalex-snapshot
The IGBP-DIS global 1km land cover data set, DISCover: First results
inferredcites →https://doi.org/10.1080/014311698214235openalex-snapshot
Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers
inferredcites →https://doi.org/10.1080/10106049609354544openalex-snapshot
An approach for using AVHRR data to monitor U.S. Great Plains Grasslands
inferredcites →https://doi.org/10.1111/j.1467-8306.1995.tb01797.x-i1openalex-snapshot
Seasonal Land-Cover Regions of the United States
inferredcites →https://doi.org/10.14358/pers.70.7.829openalex-snapshot
Development of a 2001 National Land-Cover Database for the United States
inferredcites →https://doi.org/10.14358/pers.72.11.1225openalex-snapshot
Using USDA Crop Progress Data for the Evaluation of Greenup Onset Date Calculated from MODIS 250-Meter Data
inferredcites →https://doi.org/10.17226/9975openalex-snapshot
Grand Challenges in Environmental Sciences
inferredcites →https://doi.org/10.2307/3235884openalex-snapshot
Measuring phenological variability from satellite imagery
inferredcites →https://doi.org/10.2307/3628436openalex-snapshot
Mapping Land Cover in a High Plains Agro-Ecosystem Using a Multidate Landsat Thematic Mapper Modeling Approach
inferredcites →https://doi.org/10.5860/choice.27-2701openalex-snapshot
Theory and applications of optical remote sensing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4779347openalex-extracted
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inferred← citeshttps://doi.org/10.1002/9781119297376.ch3openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9781119808565.ch16openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/agj2.20475openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/ajb2.16323openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/ece3.1273openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/eco.1866openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/gdj3.268openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/ldr.3934openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-0-387-36699-9_5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-030-33900-5_9openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-319-00624-6_2openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-319-46010-9_5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00704-025-05447-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s13157-019-01263-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s40003-024-00801-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s40996-018-0157-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41064-018-0050-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41207-016-0006-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41748-019-00106-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41976-018-0002-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41976-025-00229-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s42452-020-2786-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s42990-026-00224-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-12-385954-9.00024-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-12-409548-9.10379-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-12-815826-5.00024-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-12-815998-9.00008-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-323-95616-1.00022-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-443-13220-9.00041-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-443-13220-9.60007-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agee.2012.09.005openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agee.2017.07.028openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agee.2023.108603openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agrformet.2010.11.012openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agrformet.2014.06.007openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agrformet.2015.03.016openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agrformet.2020.108143openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agrformet.2023.109797openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agwat.2010.01.027openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agwat.2015.05.009openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agwat.2019.06.010openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agwat.2021.107249openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.apgeog.2011.06.018openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.apgeog.2014.08.003openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.apgeog.2017.06.016openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.apgeog.2017.09.003openalex-snapshot
A phenology based geo-informatics approach to map land use and land cover (2003–2013) by spatial segregation of large heterogenic river basins
inferred← citeshttps://doi.org/10.1016/j.asr.2009.11.013openalex-snapshot
Study on extraction of crop information using time-series MODIS data in the Chao Phraya Basin of Thailand
inferred← citeshttps://doi.org/10.1016/j.asr.2020.01.028openalex-snapshot
Classification of multitemporal Sentinel-2 data for field-level monitoring of rice cropping practices in Taiwan
inferred← citeshttps://doi.org/10.1016/j.asr.2021.06.053openalex-snapshot
Mapping and forecasting of rice cropping systems in central China using multiple data sources and phenology-based time-series similarity measurement
inferred← citeshttps://doi.org/10.1016/j.asr.2025.04.028openalex-snapshot
Identification of phenological characteristics and mapping lavender using Sentinel-2 time series data
inferred← citeshttps://doi.org/10.1016/j.atech.2026.102272openalex-snapshot
A novel and transferable Chlorophyll and Water Content Composite Index for cotton mapping using Sentinel-2 multispectral images on Google Earth Engine
inferred← citeshttps://doi.org/10.1016/j.atmosres.2025.108084openalex-snapshot
Increasing frequency of warm-dry climate-year type in Northeast China: A major contributor to maize yield shocks
inferred← citeshttps://doi.org/10.1016/j.catena.2024.107846openalex-snapshot
Constructing a eucalyptus identification model based on NDVI time-series remote sensing images
inferred← citeshttps://doi.org/10.1016/j.cj.2022.07.005openalex-snapshot
Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series
inferred← citeshttps://doi.org/10.1016/j.comnet.2020.107147openalex-snapshot
Towards smart farming: Systems, frameworks and exploitation of multiple sources
inferred← citeshttps://doi.org/10.1016/j.compag.2012.07.015openalex-snapshot
Crop type mapping using spectral–temporal profiles and phenological information
inferred← citeshttps://doi.org/10.1016/j.compag.2014.01.010openalex-snapshot
Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France
inferred← citeshttps://doi.org/10.1016/j.compag.2014.02.003openalex-snapshot
Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images
inferred← citeshttps://doi.org/10.1016/j.compag.2016.03.008openalex-snapshot
Mapping interannual variability of maize cover in a large irrigation district using a vegetation index – phenological index classifier
inferred← citeshttps://doi.org/10.1016/j.compag.2016.04.015openalex-snapshot
Automated cropping intensity extraction from isolines of wavelet spectra
inferred← citeshttps://doi.org/10.1016/j.compag.2016.12.006openalex-snapshot
Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques
inferred← citeshttps://doi.org/10.1016/j.compag.2017.08.024openalex-snapshot
Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning
inferred← citeshttps://doi.org/10.1016/j.compag.2017.09.037openalex-snapshot
A review on the practice of big data analysis in agriculture
inferred← citeshttps://doi.org/10.1016/j.compag.2018.03.007openalex-snapshot
QPhenoMetrics: An open source software application to assess vegetation phenology metrics
inferred← citeshttps://doi.org/10.1016/j.compag.2018.05.035openalex-snapshot
Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics
inferred← citeshttps://doi.org/10.1016/j.compag.2018.07.039openalex-snapshot
Mapping spatiotemporal dynamics of maize in China from 2005 to 2017 through designing leaf moisture based indicator from Normalized Multi-band Drought Index
inferred← citeshttps://doi.org/10.1016/j.compag.2019.02.009openalex-snapshot
A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery
inferred← citeshttps://doi.org/10.1016/j.compag.2019.04.026openalex-snapshot
Using NDVI percentiles to monitor real-time crop growth
inferred← citeshttps://doi.org/10.1016/j.compag.2019.05.035openalex-snapshot
Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length
inferred← citeshttps://doi.org/10.1016/j.compag.2020.105595openalex-snapshot
Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices
inferred← citeshttps://doi.org/10.1016/j.compag.2020.105962openalex-snapshot
An automated early-season method to map winter wheat using time-series Sentinel-2 data: A case study of Shandong, China
inferred← citeshttps://doi.org/10.1016/j.compag.2021.106049openalex-snapshot
Winter wheat planted area monitoring and yield modeling using MODIS data in the Huang-Huai-Hai Plain, China
inferred← citeshttps://doi.org/10.1016/j.compag.2021.106079openalex-snapshot
A study on the use of UAV images to improve the separation accuracy of agricultural land areas
inferred← citeshttps://doi.org/10.1016/j.compag.2021.106222openalex-snapshot
Tracking the spatio-temporal change of planting area of winter wheat-summer maize cropping system in the North China Plain during 2001–2018
inferred← citeshttps://doi.org/10.1016/j.compag.2022.106731openalex-snapshot
A new method for pixel classification for rice variety identification using spectral and time series data from Sentinel-2 satellite imagery
inferred← citeshttps://doi.org/10.1016/j.compag.2022.107066openalex-snapshot
A robust but straightforward phenology-based ginger mapping algorithm by using unique phenology features, and time-series Sentinel-2 images
inferred← citeshttps://doi.org/10.1016/j.compag.2022.107217openalex-snapshot
Technological revolutions in smart farming: Current trends, challenges & future directions
inferred← citeshttps://doi.org/10.1016/j.compag.2023.108015openalex-snapshot
Comparisons between temporal statistical metrics, time series stacks and phenological features derived from NASA Harmonized Landsat Sentinel-2 data for crop type mapping
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109536openalex-snapshot
Winter wheat mapping without ground labels via automated knowledge transfer across regions and years
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110184openalex-snapshot
The role of spectro-temporal remote sensing in vegetation classification: A comprehensive review integrating machine learning and bibliometric analysis
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110255openalex-snapshot
Integrating ecological niche and epidemiological models to predict wheat fusarium head blight using remote sensing and meteorological data
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110676openalex-snapshot
Impact of sampling techniques on crop type mapping using multi-temporal composites from Harmonized Landsat-Sentinel images
inferred← citeshttps://doi.org/10.1016/j.compag.2026.111451openalex-snapshot
Deriving crop calendars from satellite phenology and unsupervised learning: an application to Andalusia (Spain)
inferred← citeshttps://doi.org/10.1016/j.cropro.2016.06.014openalex-snapshot
Developing the framework for a risk map for mite vectored viruses in wheat resulting from pre-harvest hail damage
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2013.07.002openalex-snapshot
Moist deciduous forest identification using temporal MODIS data — A comparative study using fuzzy based classifiers
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2014.06.007openalex-snapshot
Dynamic monitoring of wetland cover changes using time-series remote sensing imagery
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2017.07.007openalex-snapshot
Future changes in summer MODIS-based enhanced vegetation index for the South-Central United States
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2018.05.006openalex-snapshot
CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2022.101678openalex-snapshot
A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications
inferred← citeshttps://doi.org/10.1016/j.ecolind.2015.03.039openalex-snapshot
Mapping paddy rice areas based on vegetation phenology and surface moisture conditions
inferred← citeshttps://doi.org/10.1016/j.ecolind.2015.09.012openalex-snapshot
Phenologic metrics derived from MODIS NDVI as indicators for Plant Available Water-holding Capacity
inferred← citeshttps://doi.org/10.1016/j.ecolind.2021.108029openalex-snapshot
Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data
inferred← citeshttps://doi.org/10.1016/j.ecolind.2023.110157openalex-snapshot
Mapping evergreen forests using new phenology index, time series Sentinel-1/2 and Google Earth Engine
inferred← citeshttps://doi.org/10.1016/j.ecolind.2024.112395openalex-snapshot
A novel index for exposed coal mapping using Landsat imagery
inferred← citeshttps://doi.org/10.1016/j.ejrs.2021.12.008openalex-snapshot
Paddy lands detection using Landsat-8 satellite images and object-based classification in Rasht city, Iran
inferred← citeshttps://doi.org/10.1016/j.envadv.2024.100572openalex-snapshot
Unveiling seasonal nitrate contamination dynamics in cropland sub-watersheds: A geo-morphological analysis of the bilate agricultural watershed
inferred← citeshttps://doi.org/10.1016/j.gloenvcha.2020.102131openalex-snapshot
Multiple cropping systems of the world and the potential for increasing cropping intensity
inferred← citeshttps://doi.org/10.1016/j.heliyon.2023.e14393openalex-snapshot
The spatial distribution and expansion of Eucalyptus in its hotspots: Implications on agricultural landscapes
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e26717openalex-snapshot
Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed
inferred← citeshttps://doi.org/10.1016/j.ijleo.2015.10.107openalex-snapshot
Major crops classification using time series MODIS EVI with adjacent years of ground reference data in the US state of Kansas
inferred← citeshttps://doi.org/10.1016/j.indic.2025.100927openalex-snapshot
Vulnerability assessment of smallholder farmers to climate change in the Awash Basin, Ethiopia
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2009.08.004openalex-snapshot
Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2011.04.001openalex-snapshot
Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2012.04.001openalex-snapshot
Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2013.03.005openalex-snapshot
Estimating crop net primary production using national inventory data and MODIS-derived parameters
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2013.07.008openalex-snapshot
A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2013.08.007openalex-snapshot
Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2013.09.008openalex-snapshot
Satellite-based investigation of flood-affected rice cultivation areas in Chao Phraya River Delta, Thailand
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2015.03.004openalex-snapshot
Analysis of uncertainty in multi-temporal object-based classification
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2015.07.001openalex-snapshot
Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2015.09.013openalex-snapshot
Automated annual cropland mapping using knowledge-based temporal features
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2016.05.010openalex-snapshot
Evolution of regional to global paddy rice mapping methods: A review
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2016.05.014openalex-snapshot
Automated mapping of soybean and corn using phenology
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2016.09.016openalex-snapshot
Winter wheat mapping combining variations before and after estimated heading dates
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2016.10.009openalex-snapshot
Characterizing major agricultural land change trends in the Western Corn Belt
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2017.10.003openalex-snapshot
Assessing the Three-North Shelter Forest Program in China by a novel framework for characterizing vegetation changes
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2020.03.012openalex-snapshot
Fractional evergreen forest cover mapping by MODIS time-series FEVC-CV methods at sub-pixel scales
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2020.04.017openalex-snapshot
Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2020.11.022openalex-snapshot
Exploring Google Street View with deep learning for crop type mapping
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2021.02.003openalex-snapshot
Zanthoxylum bungeanum Maxim mapping with multi-temporal Sentinel-2 images: The importance of different features and consistency of results
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2023.07.004openalex-snapshot
Automated in-season mapping of winter wheat in China with training data generation and model transfer
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2025.04.031openalex-snapshot
Annual winter wheat mapping for unveiling spatiotemporal patterns in China with a knowledge-guided approach and multi-source datasets
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2026.02.001openalex-snapshot
Knowledge-data-model-driven multimodal few-shot learning for hyperspectral fine classification: Generalization across sensor, category and scene
inferred← citeshttps://doi.org/10.1016/j.iswcr.2022.03.004openalex-snapshot
A spatial frequency/spectral indicator-driven model for estimating cultivated land quality using the gradient boosting decision tree and genetic algorithm-back propagation neural network
inferred← citeshttps://doi.org/10.1016/j.jag.2007.11.003openalex-snapshot
Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China
inferred← citeshttps://doi.org/10.1016/j.jag.2009.11.005openalex-snapshot
Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data
inferred← citeshttps://doi.org/10.1016/j.jag.2011.03.008openalex-snapshot
Efficient collection of training data for sub-pixel land cover classification using neural networks
inferred← citeshttps://doi.org/10.1016/j.jag.2011.06.010openalex-snapshot
Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products
inferred← citeshttps://doi.org/10.1016/j.jag.2012.03.014openalex-snapshot
Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data
inferred← citeshttps://doi.org/10.1016/j.jag.2012.10.001openalex-snapshot
Mapping land cover gradients through analysis of hyper-temporal NDVI imagery
inferred← citeshttps://doi.org/10.1016/j.jag.2013.02.001openalex-snapshot
Detecting long-duration cloud contamination in hyper-temporal NDVI imagery
inferred← citeshttps://doi.org/10.1016/j.jag.2013.03.006openalex-snapshot
Developing a gross primary production model for coniferous forests of northeastern USA from MODIS data
inferred← citeshttps://doi.org/10.1016/j.jag.2013.05.016openalex-snapshot
A new methodology to map double-cropping croplands based on continuous wavelet transform
inferred← citeshttps://doi.org/10.1016/j.jag.2014.03.021openalex-snapshot
Impact of the construction of a large dam on riparian vegetation cover at different elevation zones as observed from remotely sensed data
inferred← citeshttps://doi.org/10.1016/j.jag.2014.07.002openalex-snapshot
A support vector machine to identify irrigated crop types using time-series Landsat NDVI data
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