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Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes

https://doi.org/10.3390/rs14112621
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inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/978-3-030-04663-7_4openalex-snapshot
Learning from Imbalanced Data
inferredcites →https://doi.org/10.1007/s11676-017-0530-4openalex-snapshot
Forest type identification by random forest classification combined with SPOT and multitemporal SAR data
inferredcites →https://doi.org/10.1007/s11769-019-1060-0openalex-snapshot
Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data
inferredcites →https://doi.org/10.1016/j.agsy.2017.01.023openalex-snapshot
Big Data in Smart Farming – A review
inferredcites →https://doi.org/10.1016/j.asr.2019.08.042openalex-snapshot
Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data
inferredcites →https://doi.org/10.1016/j.biosystemseng.2016.10.012openalex-snapshot
LiDAR and thermal images fusion for ground-based 3D characterisation of fruit trees
inferredcites →https://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
inferredcites →https://doi.org/10.1016/j.compag.2015.05.001openalex-snapshot
Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data
inferredcites →https://doi.org/10.1016/j.compag.2016.07.023openalex-snapshot
Automatic green fruit counting in orange trees using digital images
inferredcites →https://doi.org/10.1016/j.compag.2016.07.028openalex-snapshot
Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging
inferredcites →https://doi.org/10.1016/j.compag.2016.08.021openalex-snapshot
A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding
inferredcites →https://doi.org/10.1016/j.compag.2017.08.024openalex-snapshot
Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning
inferredcites →https://doi.org/10.1016/j.compag.2019.105121openalex-snapshot
Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow
inferredcites →https://doi.org/10.1016/j.compag.2020.105581openalex-snapshot
Parcel-level mapping of crops in a smallholder agricultural area: A case of central China using single-temporal VHSR imagery
inferredcites →https://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
inferredcites →https://doi.org/10.1016/j.compag.2020.105618openalex-snapshot
Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China
inferredcites →https://doi.org/10.1016/j.compag.2020.105864openalex-snapshot
Agricultural crop discrimination in a heterogeneous low-mountain range region based on multi-temporal and multi-sensor satellite data
inferredcites →https://doi.org/10.1016/j.inpa.2020.01.001openalex-snapshot
Paradigm change in Indian agricultural practices using Big Data: Challenges and opportunities from field to plate
inferredcites →https://doi.org/10.1016/j.inpa.2021.01.006openalex-snapshot
High resolution aerial photogrammetry based 3D mapping of fruit crop canopies for precision inputs management
inferredcites →https://doi.org/10.1016/j.isprsjprs.2014.07.013openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.03.009openalex-snapshot
Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages
inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.04.017openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.isprsjprs.2021.06.018openalex-snapshot
An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine
inferredcites →https://doi.org/10.1016/j.jag.2012.10.008openalex-snapshot
Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3
inferredcites →https://doi.org/10.1016/j.jag.2014.07.002openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jag.2018.06.007openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jag.2018.08.011openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jag.2019.01.002openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jag.2020.102114openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.landusepol.2019.104168openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.landusepol.2019.104190openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rsase.2017.07.006openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rsase.2018.05.002openalex-snapshot
Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system
inferredcites →https://doi.org/10.1016/j.rsase.2020.100290openalex-snapshot
Mapping rice crop using sentinels (1 SAR and 2 MSI) images in tropical area: A case study in Fogera wereda, Ethiopia
inferredcites →https://doi.org/10.1016/j.rse.2006.11.021openalex-snapshot
Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains
inferredcites →https://doi.org/10.1016/j.rse.2008.02.011openalex-snapshot
Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
inferredcites →https://doi.org/10.1016/j.rse.2011.07.023openalex-snapshot
ESA's sentinel missions in support of Earth system science
inferredcites →https://doi.org/10.1016/j.rse.2012.12.026openalex-snapshot
Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection
inferredcites →https://doi.org/10.1016/j.rse.2014.02.015openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2017.06.022openalex-snapshot
A new method for crop classification combining time series of radar images and crop phenology information
inferredcites →https://doi.org/10.1016/j.rse.2017.07.015openalex-snapshot
Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications
inferredcites →https://doi.org/10.1016/j.rse.2018.04.025openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2018.09.028openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2019.111599openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2020.111673openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2020.111951openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2020.111952openalex-snapshot
Deeply synergistic optical and SAR time series for crop dynamic monitoring
inferredcites →https://doi.org/10.1016/j.rse.2020.112232openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2021.112394openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2021.112795openalex-snapshot
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inferredcites →https://doi.org/10.1016/s2095-3119(15)61321-1openalex-snapshot
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inferredcites →https://doi.org/10.1016/s2095-3119(20)63208-7openalex-snapshot
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inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
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inferredcites →https://doi.org/10.1038/s41597-021-00867-1openalex-snapshot
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inferredcites →https://doi.org/10.1080/01431160701874603openalex-snapshot
The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature
inferredcites →https://doi.org/10.1080/01431161.2010.543182openalex-snapshot
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inferredcites →https://doi.org/10.1080/01431161.2016.1182663openalex-snapshot
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inferredcites →https://doi.org/10.1080/01431161.2019.1569791openalex-snapshot
Crop type classification using a combination of optical and radar remote sensing data: a review
inferredcites →https://doi.org/10.1080/10095020.2020.1782776openalex-snapshot
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inferredcites →https://doi.org/10.1080/10106049.2019.1700556openalex-snapshot
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inferredcites →https://doi.org/10.1080/17445647.2017.1372316openalex-snapshot
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inferredcites →https://doi.org/10.1109/agro-geoinformatics.2019.8820699openalex-snapshot
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inferredcites →https://doi.org/10.1109/igarss.2017.8127795openalex-snapshot
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inferredcites →https://doi.org/10.1111/ejss.12893openalex-snapshot
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inferredcites →https://doi.org/10.1155/2021/8810279openalex-snapshot
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inferredcites →https://doi.org/10.1186/s13717-021-00285-6openalex-snapshot
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inferredcites →https://doi.org/10.25165/j.ijabe.20201301.5285openalex-snapshot
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inferredcites →https://doi.org/10.32614/rj-2015-018openalex-snapshot
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inferredcites →https://doi.org/10.3390/agronomy10050641openalex-snapshot
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inferredcites →https://doi.org/10.3390/rs10020202openalex-snapshot
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inferred← citeshttps://doi.org/10.2139/ssrn.5071980openalex-extracted
inferred← citeshttps://doi.org/10.1007/s10530-024-03361-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12145-023-01154-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12145-024-01506-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12518-026-00780-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41976-025-00247-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.asr.2025.07.008openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.cj.2026.07.025openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2024.108993openalex-snapshot
Precision agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and adoption
inferred← citeshttps://doi.org/10.1016/j.ecolind.2025.113820openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ejrs.2025.04.005openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.engappai.2026.115637openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.inpa.2025.11.005openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jag.2024.104204openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jenvman.2023.118790openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.rsase.2024.101261openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.rsase.2025.101524openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.rsase.2025.101527openalex-snapshot
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inferred← citeshttps://doi.org/10.1051/e3sconf/202459003010openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/01431161.2023.2205984openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/01431161.2026.2676261openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/01431161.2026.2742385openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/2150704x.2026.2691914openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/22797254.2025.2490787openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cises66934.2025.11264906openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cvmi61877.2024.10782261openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icaecis58353.2023.10170503openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ictacs62700.2024.10840995openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/igarss55030.2025.11242442openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s43170-023-00193-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pone.0304450openalex-snapshot
Novel artificial intelligence assisted Landsat-8 imagery analysis for mango orchard detection and area mapping
inferred← citeshttps://doi.org/10.14719/pst.5165openalex-snapshot
Object-based image analysis and machine learning for mapping cashew plantations in Ariyalur district, Tamil Nadu
inferred← citeshttps://doi.org/10.1590/1981-6723.812025openalex-snapshot
Gabiroba (Campomanesia pubescens): an analysis of the evolution of bioactive, proximate, and mineral compounds during development
inferred← citeshttps://doi.org/10.18359/rcin.6996openalex-snapshot
Evaluación comparativa de los algoritmos de aprendizaje automático Support Vector Machine y Random Forest
inferred← citeshttps://doi.org/10.21203/rs.3.rs-8960136/v1openalex-snapshot
FusionAttNet: Hierarchical Attention-Driven Sentinel- 1/Sentinel-2 Fusion for Semi-Arid Land Cover Classification in Far North Cameroon
inferred← citeshttps://doi.org/10.2139/ssrn.5249847openalex-snapshot
Thirty Years' of Crop Composition Change: Analysing Multi-Decade Trends in Australia's Southern Murray-Darling Basin
inferred← citeshttps://doi.org/10.2139/ssrn.5370592openalex-snapshot
Optimized Horticulture Species Identification and Crown Segmentation Using Multi-Modal Vision Transformers and Uav Imagery
inferred← citeshttps://doi.org/10.2139/ssrn.5884802openalex-snapshot
Deep Learning-Based Grassland Classification Using Multi-Modal Sentinel-1, Sentinel-2, and Street-Level Imagery
inferred← citeshttps://doi.org/10.3389/fagro.2024.1482893openalex-snapshot
Dynamic perspectives into tropical fruit production: a review of modeling techniques
inferred← citeshttps://doi.org/10.3389/frsen.2023.1148328openalex-snapshot
Automatic wide area land cover mapping using Sentinel-1 multitemporal data
inferred← citeshttps://doi.org/10.3390/agriculture15131427openalex-snapshot
UniHSFormer X for Hyperspectral Crop Classification with Prototype-Routed Semantic Structuring
inferred← citeshttps://doi.org/10.3390/agriculture16141518openalex-snapshot
Technology-Driven or Farmer-Centred? A Systematic Review of Artificial Intelligence Research for Smallholder Agriculture
inferred← citeshttps://doi.org/10.3390/agronomy15092122openalex-snapshot
Data-Driven Integration of Remote Sensing, Agro-Meteorology, and Wireless Sensor Networks for Crop Water Demand Estimation: Tools Towards Sustainable Irrigation in High-Value Fruit Crops
inferred← citeshttps://doi.org/10.3390/drones7010061openalex-snapshot
The Effects of Spatial Resolution and Resampling on the Classification Accuracy of Wetland Vegetation Species and Ground Objects: A Study Based on High Spatial Resolution UAV Images
inferred← citeshttps://doi.org/10.3390/geomatics3010004openalex-snapshot
Exploring the Effect of Balanced and Imbalanced Multi-Class Distribution Data and Sampling Techniques on Fruit-Tree Crop Classification Using Different Machine Learning Classifiers
inferred← citeshttps://doi.org/10.3390/land13030335openalex-snapshot
Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia
inferred← citeshttps://doi.org/10.3390/land13122184openalex-snapshot
Mapping of Forest Species Using Sentinel-2A Images in the Alentejo and Algarve Regions, Portugal
inferred← citeshttps://doi.org/10.3390/land14010171openalex-snapshot
A Review: Potential of Earth Observation (EO) for Mapping Small-Scale Agriculture and Cropping Systems in West Africa
inferred← citeshttps://doi.org/10.3390/rs14133104openalex-snapshot
Inter-Annual Climate Variability Impact on Oil Palm Mapping
inferred← citeshttps://doi.org/10.3390/rs14195013openalex-snapshot
Optimized Software Tools to Generate Large Spatio-Temporal Data Using the Datacubes Concept: Application to Crop Classification in Cap Bon, Tunisia
inferred← citeshttps://doi.org/10.3390/rs14205206openalex-snapshot
Banana Mapping in Heterogenous Smallholder Farming Systems Using High-Resolution Remote Sensing Imagery and Machine Learning Models with Implications for Banana Bunchy Top Disease Surveillance
inferred← citeshttps://doi.org/10.3390/rs14225739openalex-snapshot
Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
inferred← citeshttps://doi.org/10.3390/rs15030658openalex-snapshot
A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China
inferred← citeshttps://doi.org/10.3390/rs15112727openalex-snapshot
Synergy of Sentinel-1 and Sentinel-2 Imagery for Crop Classification Based on DC-CNN
inferred← citeshttps://doi.org/10.3390/rs15174140openalex-snapshot
Optimized Extraction Method of Fruit Planting Distribution Based on Spectral and Radar Data Fusion of Key Time Phase
inferred← citeshttps://doi.org/10.3390/rs16091579openalex-snapshot
Innovative Decision Fusion for Accurate Crop/Vegetation Classification with Multiple Classifiers and Multisource Remote Sensing Data
inferred← citeshttps://doi.org/10.3390/rs17244011openalex-snapshot
A Region-Adaptive Phenology-Aware Network for Perennial Cash Crop Mapping Using Multi-Source Time-Series Remote Sensing
inferred← citeshttps://doi.org/10.3390/su18136843openalex-snapshot
Spatial Coupling of Vegetation Frontline Migration and Vegetation-Cover Change on the Eastern Bank of the Liaohe Estuary Based on Multi-Source Remote Sensing (2000–2025)
inferred← citeshttps://doi.org/10.4314/sajg.v12i.2.10openalex-snapshot
Identifying the optimal phenological period for discriminating subtropical fruit tree crops using multi-temporal Sentinel-2 data and Google Earth Engine
inferred← citeshttps://doi.org/10.5194/essd-16-3213-2024openalex-snapshot
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
inferred← citeshttps://doi.org/10.5194/essd-2023-467-ac3openalex-snapshot
Reply on RC3
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.