|
28 Apr 2015
Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine
A. Kolotii, N. Kussul, A. Shelestov, S. Skakun, B. Yailymov, R. Basarab, M. Lavreniuk, T. Oliinyk, and V. Ostapenko
Viewed
Total article views: 1,095 (including HTML, PDF, and XML)
HTML |
PDF |
XML |
Total |
BibTeX |
EndNote |
529 |
534 |
32 |
1,095 |
37 |
39 |
- HTML: 529
- PDF: 534
- XML: 32
- Total: 1,095
- BibTeX: 37
- EndNote: 39
Views and downloads (calculated since 28 Apr 2015)
Cumulative views and downloads
(calculated since 28 Apr 2015)
Cited
32 citations as recorded by crossref.
-
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
N. Kussul et al.
10.1109/LGRS.2017.2681128
-
Estimates of Crop Yield Anomalies for 2022 in Ukraine Based on Copernicus Sentinel-1, Sentinel-3 Satellite Data, and ERA-5 Agrometeorological Indicators
E. Panek-Chwastyk et al.
10.3390/s24072257
-
Cloud Approach to Automated Crop Classification Using Sentinel-1 Imagery
A. Shelestov et al.
10.1109/TBDATA.2019.2940237
-
Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images
E. Saralioglu & O. Gungor
10.1080/10106049.2021.1917006
-
Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors
A. Ahmed et al.
10.3390/rs14051136
-
ВИКОРИСТАННЯ СТРАТЕГІЧНИХ МЕТОДІВ КОСМІЧНОГО ЗНІМАННЯ ДЛЯ МОНІТОРИНГУ ЗЕМЕЛЬ СІЛЬСЬКОГОСПОДАРСЬКОГО ПРИЗНАЧЕННЯ
О. Гулько
10.31734/architecture2023.24.209
-
In-Season Crop Type Detection by Combing Sentinel-1A and Sentinel-2 Imagery Based on the CNN Model
M. Mao et al.
10.3390/agronomy13071723
-
Is Soil Bonitet an Adequate Indicator for Agricultural Land Appraisal in Ukraine?
L. Shumilo et al.
10.3390/su132112096
-
Biophysical parameters mapping within the SPOT-5 Take 5 initiative
A. Shelestov et al.
10.1080/22797254.2017.1324743
-
Crop Classification Using MSCDN Classifier and Sparse Auto-Encoders with Non-Negativity Constraints for Multi-Temporal, Quad-Pol SAR Data
W. Zhang et al.
10.3390/rs13142749
-
Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries
E. Panek & D. Gozdowski
10.3390/agronomy11020340
-
Land cover classification and urbanization monitoring using Landsat data: A case study in Changsha city, Hunan province, China
M. Kutia et al.
10.31548/forest/1.2023.72
-
Preprocessed Sentinel-1 Data via a Web Service Focused on Agricultural Field Monitoring
M. Christiansen et al.
10.1109/ACCESS.2019.2917063
-
Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control
V. Makarichev et al.
10.3390/rs14010125
-
Methods of essential variables determination for the Earth’s surface state assessing
B. YAILYMOV et al.
10.15407/knit2018.04.026
-
Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network
E. Saralioglu & O. Gungor
10.1080/10106049.2020.1734871
-
Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model
S. Skakun et al.
10.1016/j.rse.2017.04.026
-
Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region
C. de Oliveira Santos et al.
10.3390/rs11030334
-
Relationship Between Ambient Particulate Matter and Leaf Area Index: A Panel Data Study in Delhi, India
K. Chowdhury et al.
10.1007/s10666-022-09872-z
-
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
H. Zhao et al.
10.3390/rs11222673
-
Mapping spatial-temporal nationwide soybean planting area in Argentina using Google Earth Engine
Y. Shangguan et al.
10.1080/01431161.2022.2049913
-
Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data
N. Kussul et al.
10.1109/JSTARS.2016.2560141
-
Automatic generation of land use maps using aerial orthoimages and building floor data with a Conv-Depth Block (CDB) ResU-Net architecture
S. Yoo et al.
10.1016/j.jag.2022.102678
-
High-resolution multispectral imagery and LiDAR point cloud fusion for the discrimination and biophysical characterisation of vegetable crops at different levels of nitrogen
R. Nidamanuri et al.
10.1016/j.biosystemseng.2022.08.005
-
Reducing the influence of solar illumination angle when using active optical sensor derived NDVIAOS to infer fAPAR for spring wheat (Triticum aestivum L.)
M. Rahman et al.
10.1016/j.compag.2018.11.007
-
Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models
S. Skakun et al.
10.3390/rs11151768
-
Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information
H. Zhao et al.
10.3390/rs13142790
-
SATELLITE AGROMONITORING IN UKRAINE: RESULTS OF SENTINEL-2 FOR AGRICULTURE PROJECT AND FURTHER PROSPECTS
N. Kussul et al.
10.15407/visn2016.12.099
-
Satellite agromonitoring in Ukraine
N. Kussul et al.
10.15407/visn2016.02.096
-
Using MODIS Data to Predict Regional Corn Yields
H. Ban et al.
10.3390/rs9010016
-
Crop classification based on multi-temporal PolSAR images with a single tensor network
W. Zhang et al.
10.1016/j.patcog.2023.109773
-
Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping
A. Shelestov et al.
10.3389/feart.2017.00017
Latest update: 17 Apr 2024