23.2.8.5.3 Barley

Chapter Contents (Back)
Barley Classification. Barley Yield.
See also Wheat Crop Analysis, Detection, Change.

Borzuchowski, J., Schulz, K.,
Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet,
RS(2), No. 7, July 2010, pp. 1702-1721.
DOI Link 1203
BibRef

Yu, K., Lenz-Wiedemann, V.I.S., Leufen, G., Hunsche, M., Noga, G., Chen, X.P., Bareth, G.,
Assessing Hyperspectral Vegetation Indices for Estimating Leaf Chlorophyll Concentration of Summer Barley,
AnnalsPRS(I-7), No. 2012, pp. 89-94.
DOI Link 1209
BibRef

Yu, K.[Kang], Leufen, G.[Georg], Hunsche, M.[Mauricio], Noga, G.[Georg], Chen, X.P.[Xin-Ping], Bareth, G.[Georg],
Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices,
RS(6), No. 1, 2013, pp. 64-86.
DOI Link 1402
BibRef

Yu, K.[Kang], Lenz-Wiedemann, V.[Victoria], Chen, X.P.[Xin-Ping], Bareth, G.[Georg],
Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects,
PandRS(97), No. 1, 2014, pp. 58-77.
Elsevier DOI 1410
Leaf chlorophyll BibRef

Bendig, J.[Juliane], Bolten, A.[Andreas], Bennertz, S.[Simon], Broscheit, J.[Janis], Eichfuss, S.[Silas], Bareth, G.[Georg],
Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging,
RS(6), No. 11, 2014, pp. 10395-10412.
DOI Link 1412
BibRef

Brocks, S.[Sebastian], Bareth, G.[Georg],
Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Tilly, N.[Nora], Aasen, H.[Helge], Bareth, G.[George],
Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass,
RS(7), No. 9, 2015, pp. 11449.
DOI Link 1511
BibRef
And: Correction: RS(7), No. 12, 2015, pp. 15878.
DOI Link 1601
BibRef

Näsi, R.[Roope], Viljanen, N.[Niko], Kaivosoja, J.[Jere], Alhonoja, K.[Katja], Hakala, T.[Teemu], Markelin, L.[Lauri], Honkavaara, E.[Eija],
Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Cicuéndez, V.[Víctor], Rodríguez-Rastrero, M.[Manuel], Recuero, L.[Laura], Huesca, M.[Margarita], Schmid, T.[Thomas], Inclán, R.[Rosa], Litago, J.[Javier], Sánchez-Girón, V.[Víctor], Palacios-Orueta, A.[Alicia],
First Insights on Soil Respiration Prediction across the Growth Stages of Rainfed Barley Based on Simulated MODIS and Sentinel-2 Spectral Indices,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Shawon, A.R.[Ashifur Rahman], Ko, J.[Jonghan], Jeong, S.[Seungtaek], Shin, T.[Taehwan], Lee, K.D.[Kyung Do], Shim, S.I.[Sang In],
Two-Dimensional Simulation of Barley Growth and Yield Using a Model Integrated with Remote-Controlled Aerial Imagery,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Liu, Y.[Yu], Hatou, K.[Kenji], Aihara, T.[Takanori], Kurose, S.[Sakuya], Akiyama, T.[Tsutomu], Kohno, Y.S.[Yasu-Shi], Lu, S.[Shan], Omasa, K.[Kenji],
A Robust Vegetation Index Based on Different UAV RGB Images to Estimate SPAD Values of Naked Barley Leaves,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Wengert, M.[Matthias], Piepho, H.P.[Hans-Peter], Astor, T.[Thomas], Graß, R.[Rüdiger], Wijesingha, J.[Jayan], Wachendorf, M.[Michael],
Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Herzig, P.[Paul], Borrmann, P.[Peter], Knauer, U.[Uwe], Klück, H.C.[Hans-Christian], Kilias, D.[David], Seiffert, U.[Udo], Pillen, K.[Klaus], Maurer, A.[Andreas],
Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Harfenmeister, K.[Katharina], Itzerott, S.[Sibylle], Weltzien, C.[Cornelia], Spengler, D.[Daniel],
Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
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Chen, J.L.[Ji-Long], Tan, H.Y.[Hai-Yun], Ji, Y.Y.[Yong-Yue], Tang, Q.Q.[Qing-Qing], Yan, L.Y.[Ling-Yun], Chen, Q.[Qiao], Tan, D.M.[Da-Ming],
Evapotranspiration Components Dynamic of Highland Barley Using PML ET Product in Tibet,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Sipols, A.E.[Ana E.], Valcarce-Diñeiro, R.[Rubén], Santos-Martín, M.T.[Maria Teresa], Sánchez, N.[Nilda], de Blas, C.S.[Clara Simón],
Time Series of Quad-Pol C-Band Synthetic Aperture Radar for the Forecasting of Crop Biophysical Variables of Barley Fields Using Statistical Techniques,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Duffková, R.[Renata], Poláková, L.[Lucie], Lukas, V.[Vojtech], Fucík, P.[Petr],
The Effect of Controlled Tile Drainage on Growth and Grain Yield of Spring Barley as Detected by UAV Images, Yield Map and Soil Moisture Content,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Shin, T.[Taehwan], Ko, J.[Jonghan], Jeong, S.[Seungtaek], Kang, J.[Jiwoo], Lee, K.[Kyungdo], Shim, S.[Sangin],
Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Khoramshahi, E.[Ehsan], Näsi, R.[Roope], Rua, S.[Stefan], Oliveira, R.A.[Raquel A.], Päivänsalo, A.[Axel], Niemeläinen, O.[Oiva], Niskanen, M.[Markku], Honkavaara, E.[Eija],
A Novel Deep Multi-Image Object Detection Approach for Detecting Alien Barleys in Oat Fields Using RGB UAV Images,
RS(15), No. 14, 2023, pp. 3582.
DOI Link 2307
BibRef

Thomas, L.F.[Leon-Friedrich], Änäkkälä, M.[Mikael], Lajunen, A.[Antti],
Weakly Supervised Perennial Weed Detection in a Barley Field,
RS(15), No. 11, 2023, pp. 2877.
DOI Link 2306
BibRef


Vlachopoulos, O., Leblon, B., Wang, J., Haddadi, A., LaRocque, A., Patterson, G.,
Mapping Barley Lodging with UAS Multispectral Imagery and Machine Learning,
ISPRS21(B1-2021: 203-208).
DOI Link 2201
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Potato Crop Analysis, Production, Detection, Health, Change .


Last update:May 6, 2024 at 15:50:14