23.2.8 Classification for Crops, Analysis of Production, Specific Crops, Specific Plants

Badhwar, G.D., Austin, W.W., Carnes, J.G.,
A semi-automatic technique for multitemporal classification of a given crop within a landsat scene,
PR(15), No. 3, 1982, pp. 217-230.
Elsevier DOI 0309
BibRef

Davis, L.S.[Larry S.], Wang, C.Y.[Cheng-Ye], Xie, H.C.[Hu-Chen],
An Experiment in Multispectral, Multitemporal Crop Classification Using Relaxation Techniques,
CVGIP(23), No. 2, August 1983, pp. 227-235.
Elsevier DOI BibRef 8308

Sun, W.X.[Wan-Xiao], Heidt, V., Gong, P.[Peng], Xu, G.[Gang],
Information fusion for rural land-use classification with high-resolution satellite imagery,
GeoRS(41), No. 4, April 2003, pp. 883-890.
IEEE Abstract. 0307
BibRef

Aplin, P.[Paul], Atkinson, P.M.[Peter M.],
Predicting Missing Field Boundaries to Increase Per-Field Classification Accuracy,
PhEngRS(70), No. 1, January 2004, pp. 141-150.
WWW Link. 0403
Missing field boundaries were predicted by comparing the within-field modal land-cover proportion and local variance to increase the accuracy of per-field classification.
See also Super-resolution target identification from remotely sensed images using a Hopfield neural network. BibRef

Somers, B.[Ben], Delalieux, S.[Stephanie], Verstraeten, W.W.[Willem W.], Coppin, P.[Pol],
A Conceptual Framework for the Simultaneous Extraction of Sub-pixel Spatial Extent and Spectral Characteristics of Crops,
PhEngRS(75), No. 1, January 2009, pp. 57-68.
WWW Link. 0902
BibRef

Lucas, R.[Richard], Rowlands, A.[Aled], Brown, A.[Alan], Keyworth, S.[Steve], Bunting, P.[Peter],
Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping,
PandRS(62), No. 3, August 2007, pp. 165-185.
Elsevier DOI 0709
Time-series imagery; Landsat; Segmentation; Decision rules; Fuzzy membership BibRef

Johnson, D.M.[David M.],
A Comparison of Coincident Landsat-5 TM and Resourcesat-1 AWiFS Imagery for Classifying Croplands,
PhEngRS(74), No. 11, November 2008, pp. 1413-1424.
WWW Link. 0804
Testing the suitability of AWiFS imagery with TM as a benchmark for deriving row crop focused cover type maps over highly cultivated regions of the central U.S. BibRef

Leite, P.B.C.[Paula Beatriz Cerqueira], Feitosa, R.Q.[Raul Queiroz], Formaggio, A.R.[Antonio Roberto], da Costa, G.A.O.P.[Gilson Alexandre Ostwald Pedro], Pakzad, K.[Kian], Sanches, I.D.[Ieda Del'Arco],
Hidden Markov Models for crop recognition in remote sensing image sequences,
PRL(31), No. 1, January 2010, pp. 19-26.
Elsevier DOI 1011
Hidden Markov Models; Crop recognition; Remote sensing BibRef

Zheng, L.Y.[Li-Ying], Shi, D.M.[Da-Ming], Zhang, J.T.[Jing-Tao],
Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminant,
PRL(31), No. 9, 1 July 2010, pp. 920-925.
Elsevier DOI 1004
Mean shift; Fisher linear discriminant; Point-line distance; Crop image; Segmentation BibRef

Potgieter, A.B., Apan, A., Hammer, G., Dunn, P.,
Early-season crop area estimates for winter crops in NE Australia using MODIS satellite imagery,
PandRS(65), No. 4, July 2010, pp. 380-387.
Elsevier DOI 1003
Early-season; Crop area estimates; Simple metric; Multi-temporal; Shire-scale BibRef

Knoth, C.[Christian], Nüst, D.[Daniel],
Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
Replacing proprietary software with open source. BibRef

Biliouris, D., van der Zande, D., Verstraeten, W., Stuckens, J., Muys, B., Dutré, P., Coppin, P.,
RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurements of Fagus sylvatica L. Leaves.,
RS(1), No. 2, June 2009, pp. 92-106.
DOI Link 1203
BibRef

Hunt, E., Hively, W., Fujikawa, S., Linden, D., Daughtry, C., McCarty, G.,
Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring,
RS(2), No. 1, January 2010, pp. 290-305.
DOI Link 1203
Award, Remote Sensing. 2014. See:
DOI Link BibRef

Panda, S., Ames, D., Panigrahi, S.,
Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques,
RS(2), No. 3, March 2010, pp. 673-696.
DOI Link 1203
BibRef

Martinez, B., Cassiraga, E., Camacho, F., Garcia-Haro, J.,
Geostatistics for Mapping Leaf Area Index over a Cropland Landscape: Efficiency Sampling Assessment,
RS(2), No. 11, November 2010, pp. 2584-2606.
DOI Link 1203
BibRef

Fletcher, R.S., Everitt, J.H., Elder, H.,
Evaluating Airborne Multispectral Digital Video to Differentiate Giant Salvinia from Other Features in Northeast Texas,
RS(2), No. 10, October 2010, pp. 2413-2423.
DOI Link 1203
BibRef

Wu, J.D.[Jin-Dong], Bauer, M.E.[Marvin E.],
Evaluating the Effects of Shadow Detection on QuickBird Image Classification and Spectroradiometric Restoration,
RS(5), No. 9, 2013, pp. 4450-4469.
DOI Link 1310
Shadow Detection. BibRef

Knox, N.M.[Nichola M.], Skidmore, A.K.[Andrew K.], Prins, H.H.T.[Herbert H.T.], Heitkönig, I.M.A.[Ignas M.A.], Slotow, R.[Rob], van der Waal, C.[Cornelis], de Boer, W.F.[William F.],
Remote sensing of forage nutrients: Combining ecological and spectral absorption feature data,
PandRS(72), No. 1, August 2012, pp. 27-35.
Elsevier DOI 1209
Landscape; Modelling; Monitoring; Ecology; Resources; Hyperspectral BibRef

Holland, J.[Jennie], Aplin, P.[Paul],
Super-resolution image analysis as a means of monitoring bracken (Pteridium aquilinum) distributions,
PandRS(75), No. 1, January 2013, pp. 48-63.
Elsevier DOI 1301
Bracken; Super-resolution; Classification; Monitoring; Vegetation; Scale BibRef

Zhao, Y., Pu, R., Bell, S.S., Meyer, C., Baggett, L.P., Geng, X.,
Hyperion Image Optimization in Coastal Waters,
GeoRS(51), No. 2, February 2013, pp. 1025-1036.
IEEE DOI 1302
Costal vegetation of Florida. BibRef

Turker, M.[Mustafa], Kok, E.H.[Emre Hamit],
Field-based sub-boundary extraction from remote sensing imagery using perceptual grouping,
PandRS(79), No. 1, May 2013, pp. 106-121.
Elsevier DOI 1305
BibRef
Earlier:
Developing an integrated system for the extraction of sub-fields within agricultural parcels from remote sensing images,
OBIA06(xx-yy).
PDF File. 0607
Field-based; Boundary detection; Perceptual-grouping; Agriculture; SPOT imagery BibRef

Ozdarici, A., Turker, M.,
Field-based classification of agricultural crops using multi-scale images,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Ordonez, C., Rodriguez-Perez, J.R., Moreira, J.J., Sanz, E.,
Using Hyperspectral Spectrometry and Functional Models to Characterize Vine-Leaf Composition,
GeoRS(51), No. 5, May 2013, pp. 2610-2618.
IEEE DOI 1305
BibRef

Rembold, F.[Felix], Atzberger, C.[Clement], Savin, I.[Igor], Rojas, O.[Oscar],
Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection,
RS(5), No. 4, April 2013, pp. 1704-1733.
DOI Link 1305
BibRef
And: Correction: RS(5), No. 11, 2013, pp. 5572-5573.
DOI Link 1312
BibRef

Kamble, B., Kilic, A., Hubbard, K.,
Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index,
RS(5), No. 4, April 2013, pp. 1588-1602.
DOI Link 1305
BibRef

Mishra, V.[Vikalp], Cruise, J.F.[James F.], Mecikalski, J.R.[John R.], Hain, C.R.[Christopher R.], Anderson, M.C.[Martha C.],
A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields,
RS(5), No. 7, 2013, pp. 3331-3356.
DOI Link 1308
BibRef

Peng, D.L.[Dai-Liang], Jiang, Z.Y.[Zhang-Yan], Huete, A.R.[Alfredo R.], Ponce-Campos, G.E.[Guillermo E.], Nguyen, U.[Uyen], Luvall, J.C.[Jeffrey C.],
Response of Spectral Reflectances and Vegetation Indices on Varying Juniper Cone Densities,
RS(5), No. 10, 2013, pp. 5330-5345.
DOI Link 1311
BibRef

Roelofsen, H.D.[Hans D.], van Bodegom, P.M.[Peter M.], Kooistra, L.[Lammert], Witte, J.P.M.[Jan-Philip M.],
Trait Estimation in Herbaceous Plant Assemblages from in situ Canopy Spectra,
RS(5), No. 12, 2013, pp. 6323-6345.
DOI Link 1402
BibRef

Beamish, D.[David],
Peat Mapping Associations of Airborne Radiometric Survey Data,
RS(6), No. 1, 2014, pp. 521-539.
DOI Link 1402
BibRef

Wilson, J.H.[Jeffrey H.], Zhang, C.H.[Chun-Hua], Kovacs, J.M.[John M.],
Separating Crop Species in Northeastern Ontario Using Hyperspectral Data,
RS(6), No. 2, 2014, pp. 925-945.
DOI Link 1403
BibRef

Li, P.[Peng], Feng, Z.M.[Zhi-Ming], Jiang, L.[Luguang], Liao, C.[Chenhua], Zhang, J.H.[Jing-Hua],
A Review of Swidden Agriculture in Southeast Asia,
RS(6), No. 2, 2014, pp. 1654-1683.
DOI Link 1403
BibRef

Chen, J.[Jun], Cui, T.W.[Ting-Wei], Qiu, Z.F.[Zhong-Feng], Lin, C.S.[Chang-Song],
A simple two-band semi-analytical model for retrieval of specific absorption coefficients in coastal waters,
PandRS(91), No. 1, 2014, pp. 85-97.
Elsevier DOI 1404
Remote sensing BibRef

Jiang, Z.W.[Zhi-Wei], Chen, Z.X.[Zhong-Xin], Chen, J.[Jin], Ren, J.Q.[Jian-Qiang], Li, Z.N.[Zong-Nan], Sun, L.[Liang],
The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation,
RS(6), No. 4, 2014, pp. 2664-2681.
DOI Link 1405
BibRef

Lewis, S.M.[Sarah M.], Kelly, M.[Maggi],
Mapping the Potential for Biofuel Production on Marginal Lands: Differences in Definitions, Data and Models across Scales,
IJGI(3), No. 2, 2014, pp. 430-459.
DOI Link 1405
BibRef

Peña, J.M.[José M.], Gutiérrez, P.A.[Pedro A.], Hervás-Martínez, C.[César], Six, J.[Johan], Plant, R.E.[Richard E.], López-Granados, F.[Francisca],
Object-Based Image Classification of Summer Crops with Machine Learning Methods,
RS(6), No. 6, 2014, pp. 5019-5041.
DOI Link 1407
BibRef

Zhang, M.[Miao], Wu, B.F.[Bing-Fang], Yu, M.Z.[Ming-Zhao], Zou, W.T.[Wen-Tao], Zheng, Y.[Yang],
Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio,
RS(6), No. 6, 2014, pp. 5774-5794.
DOI Link 1407
BibRef

Prilepova, O.[Olga], Hart, Q.[Quinn], Merz, J.[Justin], Parker, N.[Nathan], Bandaru, V.[Varaprasad], Jenkins, B.[Bryan],
Design of a GIS-Based Web Application for Simulating Biofuel Feedstock Yields,
IJGI(3), No. 3, 2014, pp. 929-941.
DOI Link 1407
BibRef

Leroux, L.[Louise], Jolivot, A.[Audrey], Bégué, A.[Agnès], Lo Seen, D.[Danny], Zoungrana, B.[Bernardin],
How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?,
RS(6), No. 9, 2014, pp. 8541-8564.
DOI Link 1410
BibRef

Cai, W.W.[Wen-Wen], Yuan, W.P.[Wen-Ping], Liang, S.L.[Shun-Lin], Liu, S.G.[Shu-Guang], Dong, W.J.[Wen-Jie], Chen, Y.[Yang], Liu, D.[Dan], Zhang, H.C.[Hai-Cheng],
Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models,
RS(6), No. 9, 2014, pp. 8945-8965.
DOI Link 1410
BibRef

Hoberg, T.[Thorsten], Rottensteiner, F.[Franz], Feitosa, R.Q.[Raul Queiroz], Heipke, C.[Christian],
Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery,
GeoRS(53), No. 2, February 2015, pp. 659-673.
IEEE DOI 1411
BibRef
Earlier: A1, A2, A4, Only:
Context Models for CRF-Based Classification of Multitemporal Remote Sensing Data,
AnnalsPRS(I-7), No. 2012, pp. 129-134.
DOI Link 1209
BibRef
Earlier: A1, A2, A4, Only:
Classification of multitemporal remote sensing data of different resolution using Conditional Random Fields,
CVRSE11(235-242).
IEEE DOI 1201
artificial satellites BibRef

Hoberg, T., Müller, S.,
Multitemporal Crop Type Classification using Conditional Random Fields and RapidEye Data,
HighRes11(xx-yy).
PDF File. 1106
BibRef

Albert, L.[Lena], Rottensteiner, F.[Franz], Heipke, C.[Christian],
A higher order conditional random field model for simultaneous classification of land cover and land use,
PandRS(130), No. 1, 2017, pp. 63-80.
Elsevier DOI 1708
BibRef
Earlier:
Contextual Land Use Classification: How Detailed Can The Class Structure Be?,
ISPRS16(B4: 11-18).
DOI Link 1610
BibRef
Earlier:
A two-layer Conditional Random Field model for simultaneous classification of land cover and land use,
PCV14(17-24).
DOI Link 1404
Contextual, classification.
See also Sequential Gaussian Mixture Models for Two-Level Conditional Random Fields. BibRef

Kouadio, L.[Louis], Newlands, N.K.[Nathaniel K.], Davidson, A.[Andrew], Zhang, Y.[Yinsuo], Chipanshi, A.[Aston],
Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale,
RS(6), No. 10, 2014, pp. 10193-10214.
DOI Link 1411
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Mattiuzzi, M.[Matteo], Bussink, C.[Coen], Bauer, T.[Thomas],
Analysing Phenological Characteristics Extracted from Landsat NDVI Time Series to Identify Suitable Image Acquisition Dates for Cannabis Mapping in Afghanistan,
PFG(2014), No. 5, 2014, pp. 383-392.
DOI Link 1411
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Li, X.X.[Xiao-Xiao], Shao, G.F.[Guo-Fan],
Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA,
RS(6), No. 11, 2014, pp. 11372-11390.
DOI Link 1412
BibRef

Doktor, D.[Daniel], Lausch, A.[Angela], Spengler, D.[Daniel], Thurner, M.[Martin],
Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods,
RS(6), No. 12, 2014, pp. 12247-12274.
DOI Link 1412
BibRef

Marshall, M.[Michael], Thenkabail, P.[Prasad],
Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing,
RS(7), No. 1, 2015, pp. 808-835.
DOI Link 1502
BibRef

Bareth, G.[Georg], Aasen, H.[Helge], Bendig, J.[Juliane], Gnyp, M.L.[Martin Leon], Bolten, A.[Andreas], Jung, A.[András], Michels, R.[René], Soukkamäki, J.[Jussi],
Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements,
PFG(2015), No. 1, 2015, pp. 69-79.
DOI Link 1503
BibRef

Pôças, I.[Isabel], Paço, T.A.[Teresa A.], Paredes, P.[Paula], Cunha, M.[Mário], Pereira, L.S.[Luís S.],
Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data,
RS(7), No. 3, 2015, pp. 2373-2400.
DOI Link 1504
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Miao, R.H.[Rong-Hui], Tang, J.L.[Jing-Lei], Chen, X.Q.[Xiao-Qian],
Classification of farmland images based on color features,
JVCIR(29), No. 1, 2015, pp. 138-146.
Elsevier DOI 1504
Color features BibRef

Hao, P.Y.[Peng-Yu], Zhan, Y.L.[Yu-Lin], Wang, L.[Li], Niu, Z.[Zheng], Shakir, M.[Muhammad],
Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA,
RS(7), No. 5, 2015, pp. 5347-5369.
DOI Link 1506
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Ozdarici-Ok, A.[Asli], Ok, A.O.[Ali Ozgun], Schindler, K.[Konrad],
Mapping of Agricultural Crops from Single High-Resolution Multispectral Images: Data-Driven Smoothing vs. Parcel-Based Smoothing,
RS(7), No. 5, 2015, pp. 5611-5638.
DOI Link 1506
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Lanaras, C., Bioucas-Dias, J., Baltsavias, E., Schindler, K.[Konrad],
Super-Resolution of Multispectral Multiresolution Images from a Single Sensor,
EarthVision17(1505-1513)
IEEE DOI 1709
Estimation, MODIS, Monitoring, Remote sensing, Signal resolution, Spatial, resolution BibRef

Schmedtmann, J.[Jonas], Campagnolo, M.L.[Manuel L.],
Reliable Crop Identification with Satellite Imagery in the Context of Common Agriculture Policy Subsidy Control,
RS(7), No. 7, 2015, pp. 9325.
DOI Link 1506
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Waldner, F.[François], Lambert, M.J.[Marie-Julie], Li, W.J.[Wen-Juan], Weiss, M.[Marie], Demarez, V.[Valérie], Morin, D.[David], Marais-Sicre, C.[Claire], Hagolle, O.[Olivier], Baret, F.[Frédéric], Defourny, P.[Pierre],
Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series,
RS(7), No. 8, 2015, pp. 10400.
DOI Link 1509
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Hamada, Y.[Yuki], Ssegane, H.[Herbert], Negri, M.C.[Maria Cristina],
Mapping Intra-Field Yield Variation Using High Resolution Satellite Imagery to Integrate Bioenergy and Environmental Stewardship in an Agricultural Watershed,
RS(7), No. 8, 2015, pp. 9753.
DOI Link 1509
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Kou, X.K.[Xiao-Kang], Chai, L.[Linna], Jiang, L.M.[Ling-Mei], Zhao, S.J.[Shao-Jie], Yan, S.[Shuang],
Modeling of the Permittivity of Holly Leaves in Frozen Environments,
GeoRS(53), No. 11, November 2015, pp. 6048-6057.
IEEE DOI 1509
vegetation BibRef

Marshall, M.[Michael], Thenkabail, P.[Prasad],
Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation,
PandRS(108), No. 1, 2015, pp. 205-218.
Elsevier DOI 1511
Earth observation BibRef

Villa, P.[Paolo], Stroppiana, D.[Daniela], Fontanelli, G.[Giacomo], Azar, R.[Ramin], Brivio, P.A.[Pietro Alessandro],
In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features,
RS(7), No. 10, 2015, pp. 12859.
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Liu, X.L.[Xiao-Long], Bo, Y.C.[Yan-Chen], Zhang, J.[Jian], He, Y.Q.[Ya-Qian],
Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data,
RS(7), No. 11, 2015, pp. 15244.
DOI Link 1512
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Wu, M.Q.[Ming-Quan], Zhang, X.Y.[Xiao-Yang], Huang, W.J.[Wen-Jiang], Niu, Z.[Zheng], Wang, C.Y.[Chang-Yao], Li, W.[Wang], Hao, P.Y.[Peng-Yu],
Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring,
RS(7), No. 12, 2015, pp. 15826.
DOI Link 1601
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Heim, R.H.J.[René Hans-Jürgen], Jürgens, N.[Norbert], Große-Stoltenberg, A.[André], Oldeland, J.[Jens],
The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae),
RS(7), No. 12, 2015, pp. 15862.
DOI Link 1601
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Meroni, M., Fasbender, D., Balaghi, R., Dali, M., Haffani, M., Haythem, I., Hooker, J., Lahlou, M., Lopez-Lozano, R., Mahyou, H., Ben Moussa, M., Sghaier, N., Wafa, T., Leo, O.,
Evaluating NDVI Data Continuity Between SPOT-VEGETATION and PROBA-V Missions for Operational Yield Forecasting in North African Countries,
GeoRS(54), No. 2, February 2016, pp. 795-804.
IEEE DOI 1601
Agriculture BibRef

Li, J.Z.[Jing-Zhong], Liu, Y.M.[Yong-Mei], Mo, C.H.[Chong-Hui], Wang, L.[Lei], Pang, G.[Guowei], Cao, M.M.[Ming-Ming],
IKONOS Image-Based Extraction of the Distribution Area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China,
RS(8), No. 2, 2016, pp. 148.
DOI Link 1603
A flower. BibRef

Danielson, P.[Patrick], Yang, L.M.[Li-Min], Jin, S.[Suming], Homer, C.[Collin], Napton, D.[Darrell],
An Assessment of the Cultivated Cropland Class of NLCD 2006 Using a Multi-Source and Multi-Criteria Approach,
RS(8), No. 2, 2016, pp. 101.
DOI Link 1603
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Zhang, X.Y.[Xiao-Yang], Zhang, Q.Y.[Qing-Yuan],
Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations,
PandRS(114), No. 1, 2016, pp. 191-205.
Elsevier DOI 1604
Global crop yield BibRef

Lambert, M.J.[Marie-Julie], Waldner, F.[François], Defourny, P.[Pierre],
Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m,
RS(8), No. 3, 2016, pp. 232.
DOI Link 1604
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Zhang, J.[Jian], Yang, C.H.[Cheng-Hai], Song, H.B.[Huai-Bo], Hoffmann, W.C.[Wesley Clint], Zhang, D.Y.[Dong-Yan], Zhang, G.Z.[Guo-Zhong],
Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification,
RS(8), No. 3, 2016, pp. 257.
DOI Link 1604
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Zhang, J.[Jian], Yang, C.H.[Cheng-Hai], Zhao, B.Q.[Bi-Quan], Song, H.B.[Huai-Bo], Hoffmann, W.C.[Wesley Clint], Shi, Y.Y.[Ye-Yin], Zhang, D.Y.[Dong-Yan], Zhang, G.Z.[Guo-Zhong],
Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
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Bareth, G.[Georg], Bendig, J.[Juliane], Tilly, N.[Nora], Hoffmeister, D.[Dirk], Aasen, H.[Helge], Bolten, A.[Andreas],
A Comparison of UAV- and TLS-derived Plant Height for Crop Monitoring: Using Polygon Grids for the Analysis of Crop Surface Models (CSMs),
PFG(2016), No. 2, 2016, pp. 85-94.
DOI Link 1606
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Inglada, J.[Jordi], Vincent, A.[Arthur], Arias, M.[Marcela], Marais-Sicre, C.[Claire],
Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series,
RS(8), No. 5, 2016, pp. 362.
DOI Link 1606
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Uhl, F.[Florian], Bartsch, I.[Inka], Oppelt, N.[Natascha],
Submerged Kelp Detection with Hyperspectral Data,
RS(8), No. 6, 2016, pp. 487.
DOI Link 1608
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Hu, Q., Wu, W., Song, Q., Yu, Q., Lu, M., Yang, P., Tang, H., Long, Y.,
Extending the Pairwise Separability Index for Multicrop Identification Using Time-Series MODIS Images,
GeoRS(54), No. 11, November 2016, pp. 6349-6361.
IEEE DOI 1610
Agriculture BibRef

Bose, P.[Pritam], Kasabov, N.K.[Nikola K.], Bruzzone, L.[Lorenzo], Hartono, R.N.[Reggio N.],
Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series,
GeoRS(54), No. 11, November 2016, pp. 6563-6573.
IEEE DOI 1610
Agriculture BibRef

Alemu, W.G.[Woubet G.], Henebry, G.M.[Geoffrey M.],
Characterizing Cropland Phenology in Major Grain Production Areas of Russia, Ukraine, and Kazakhstan by the Synergistic Use of Passive Microwave and Visible to Near Infrared Data,
RS(8), No. 12, 2016, pp. 1016.
DOI Link 1612
BibRef

Alemu, W.G.[Woubet G.], Henebry, G.M.[Geoffrey M.],
Comparing Passive Microwave with Visible-To-Near-Infrared Phenometrics in Croplands of Northern Eurasia,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Alemu, W.G.[Woubet G.], Henebry, G.M.[Geoffrey M.],
Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Xue, Z.H.[Zhao-Hui], Du, P.J.[Pei-Jun], Li, J.[Jun], Su, H.J.[Hong-Jun],
Sparse graph regularization for robust crop mapping using hyperspectral remotely sensed imagery with very few in situ data,
PandRS(124), No. 1, 2017, pp. 1-15.
Elsevier DOI 1702
Hyperspectral remote sensing BibRef

Silva, J.[Joel], Bacao, F.[Fernando], Caetano, M.[Mario],
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Iqbal, F.[Faheem], Lucieer, A.[Arko], Barry, K.[Karen], Wells, R.[Reuben],
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Danner, M.[Martin], Berger, K.[Katja], Wocher, M.[Matthias], Mauser, W.[Wolfram], Hank, T.[Tobias],
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Verrelst, J.[Jochem], Sabater, N.[Neus], Rivera, J.P.[Juan Pablo], Muñoz-Marí, J.[Jordi], Vicent, J.[Jorge], Camps-Valls, G.[Gustau], Moreno, J.[José],
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Carl, C.[Christin], Landgraf, D.[Dirk], van der Maaten-Theunissen, M.[Marieke], Biber, P.[Peter], Pretzsch, H.[Hans],
Robinia pseudoacacia L. Flower Analyzed by Using An Unmanned Aerial Vehicle (UAV),
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Yang, Y.P.[Ying-Pin], Huang, Q.T.[Qi-Ting], Wu, W.[Wei], Luo, J.C.[Jian-Cheng], Gao, L.J.[Li-Jing], Dong, W.[Wen], Wu, T.J.[Tian-Jun], Hu, X.D.[Xiao-Dong],
Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data,
RS(9), No. 12, 2017, pp. xx-yy.
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Leduc, M.B.[Marie-Bé], Knudby, A.J.[Anders J.],
Mapping Wild Leek through the Forest Canopy Using a UAV,
RS(10), No. 1, 2018, pp. xx-yy.
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Hunt, E.R.[E. Raymond], Li, L.[Li], Friedman, J.M.[Jennifer M.], Gaiser, P.W.[Peter W.], Twarog, E.[Elizabeth], Cosh, M.H.[Michael H.],
Incorporation of Stem Water Content into Vegetation Optical Depth for Crops and Woodlands,
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Pascucci, S.[Simone], Carfora, M.F.[Maria Francesca], Palombo, A.[Angelo], Pignatti, S.[Stefano], Casa, R.[Raffaele], Pepe, M.[Monica], Castaldi, F.[Fabio],
A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment,
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Xu, W.[Wei], Wang, Q.[Qili], Chen, R.[Runyu],
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Moeckel, T.[Thomas], Dayananda, S.[Supriya], Nidamanuri, R.R.[Rama Rao], Nautiyal, S.I.[Sun-Il], Hanumaiah, N.[Nagaraju], Buerkert, A.[Andreas], Wachendorf, M.[Michael],
Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images,
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Zhou, G.H.[Guan-Hua], Ma, Z.Q.[Zhong-Qi], Sathyendranath, S.[Shubha], Platt, T.[Trevor], Jiang, C.[Cheng], Sun, K.[Kang],
Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis,
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Torbick, N.[Nathan], Huang, X.D.[Xiao-Dong], Ziniti, B.[Beth], Johnson, D.[David], Masek, J.[Jeff], Reba, M.[Michele],
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Rotta, L.H.S.[Luiz Henrique S.], Mishra, D.R.[Deepak R.], Watanabe, F.S.Y.[Fernanda S.Y.], Rodrigues, T.W.P.[Thanan W.P], Alcântara, E.H.[Enner H.], Imai, N.N.[Nilton N.],
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Elsevier DOI 1809
Radiative transfer models, Reflectance, Attenuation, Water column correction, Bottom albedo, Hydroacoustic data, Reservoir management BibRef

Liu, L.L.[Ling-Ling], Zhang, X.Y.[Xiao-Yang], Yu, Y.Y.[Yun-Yue], Gao, F.[Feng], Yang, Z.W.[Zheng-Wei],
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Petersen, L.K.[Lillian Kay],
Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa,
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Liu, X.Y.[Xiang-Yu], Tian, Y.C.[Yi-Chen], Yuan, C.[Chao], Zhang, F.F.[Fei-Fei], Yang, G.[Guang],
Opium Poppy Detection Using Deep Learning,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
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Aneece, I.[Itiya], Thenkabail, P.[Prasad],
Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
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Berger, K.[Katja], Atzberger, C.[Clement], Danner, M.[Martin], Wocher, M.[Matthias], Mauser, W.[Wolfram], Hank, T.[Tobias],
Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
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Tao, J.B.[Jian-Bin], Wu, W.B.[Wen-Bin], Xu, M.[Meng],
Using the Bayesian Network to Map Large-Scale Cropping Intensity by Fusing Multi-Source Data,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
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Malambo, L.[Lonesome], Popescu, S.C.[Sorin C.], Horne, D.W., Pugh, N.A., Rooney, W.L.,
Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data,
PandRS(149), 2019, pp. 1-13.
Elsevier DOI 1903
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Malambo, L.[Lonesome], Popescu, S.C.[Sorin C.], Ku, N.W.[Nian-Wei], Rooney, W.[William], Zhou, T.[Tan], Moore, S.[Samuel],
A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting,
RS(11), No. 24, 2019, pp. xx-yy.
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Liu, P.[Ping], Chen, X.[Xi],
Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link 1903
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Wang, L.M.[Li-Min], Dong, Q.H.[Qing-Han], Yang, L.B.[Ling-Bo], Gao, J.M.[Jian-Meng], Liu, J.[Jia],
Crop Classification Based on a Novel Feature Filtering and Enhancement Method,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
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Chauhan, S.[Sugandh], Darvishzadeh, R.[Roshanak], Boschetti, M.[Mirco], Pepe, M.[Monica], Nelson, A.[Andrew],
Remote sensing-based crop lodging assessment: Current status and perspectives,
PandRS(151), 2019, pp. 124-140.
Elsevier DOI 1904
Crop lodging, Remote sensing, Airborne, Satellite, Risk mapping, Lodging detection BibRef

Kim, N.[Nari], Ha, K.J.[Kyung-Ja], Park, N.W.[No-Wook], Cho, J.[Jaeil], Hong, S.[Sungwook], Lee, Y.W.[Yang-Won],
A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006-2015,
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Chen, Y.[Yang], Lee, W.S.[Won Suk], Gan, H.[Hao], Peres, N.[Natalia], Fraisse, C.[Clyde], Zhang, Y.C.[Yan-Chao], He, Y.[Yong],
Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
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Siddiqui, M.D.[Muhammad Danish], Zaidi, A.Z.[Arjumand Z.], Abdullah, M.[Muhammad],
Performance Evaluation of Newly Proposed Seaweed Enhancing Index (SEI),
RS(11), No. 12, 2019, pp. xx-yy.
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Huang, Q.[Qing], Qiu, F.[Feng], Fan, W.L.[Wei-Liang], Liu, Y.[Yibo], Zhang, Q.[Qian],
Evaluation of Different Methods for Estimating the Fraction of Sunlit Leaves and Its Contribution for Photochemical Reflectance Index Utilization in a Coniferous Forest,
RS(11), No. 14, 2019, pp. xx-yy.
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LUE: Light Use Efficiency. Simulate gross productivity. BibRef

Dayananda, S.[Supriya], Astor, T.[Thomas], Wijesingha, J.[Jayan], Thimappa, S.C.[Subbarayappa Chickadibburahalli], Chowdappa, H.D.[Hanumanthappa Dimba], Mudalagiriyappa, Nidamanuri, R.R.[Rama Rao], Nautiyal, S.I.[Sun-Il], Wachendorf, M.[Michael],
Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging,
RS(11), No. 15, 2019, pp. xx-yy.
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Easterday, K.[Kelly], Kislik, C.[Chippie], Dawson, T.E.[Todd E.], Hogan, S.[Sean], Kelly, M.[Maggi],
Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs),
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
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Liu, L.C.[Li-Cong], Cao, R.[Ruyin], Shen, M.G.[Miao-Gen], Chen, J.[Jin], Wang, J.M.[Jian-Min], Zhang, X.Y.[Xiao-Yang],
How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes?,
RS(11), No. 18, 2019, pp. xx-yy.
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Nguyen, V.C.[Van Cuong], Jeong, S.[Seungtaek], Ko, J.H.[Jong-Han], Ng, C.T.[Chi Tim], Yeom, J.[Jongmin],
Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity,
RS(11), No. 18, 2019, pp. xx-yy.
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Qian, Y.L.[Yong-Lan], Yang, Z.W.[Zheng-Wei], Di, L.P.[Li-Ping], Rahman, M.S.[Md. Shahinoor], Tan, Z.Y.[Zhen-Yu], Xue, L.[Lei], Gao, F.[Feng], Yu, E.G.[Eugene Genong], Zhang, X.Y.[Xiao-Yang],
Crop Growth Condition Assessment at County Scale Based on Heat-Aligned Growth Stages,
RS(11), No. 20, 2019, pp. xx-yy.
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Dong, Y.D.[Ya-Dong], Jiao, Z.[Ziti], Cui, L.[Lei], Zhang, H.[Hu], Zhang, X.N.[Xiao-Ning], Yin, S.Y.[Si-Yang], Ding, A.X.[An-Xin], Chang, Y.X.[Ya-Xuan], Xie, R.[Rui], Guo, J.[Jing],
Assessment of the Hotspot Effect for the PROSAIL Model With POLDER Hotspot Observations Based on the Hotspot-Enhanced Kernel-Driven BRDF Model,
GeoRS(57), No. 10, October 2019, pp. 8048-8064.
IEEE DOI 1910
Hotspot effect is a typical angular reflectance signature of vegetation canopies. geophysical techniques, reflectivity, remote sensing, terrain mapping, vegetation, vegetation mapping, hotspot effect, PROSAIL model BibRef

Abdelbaki, A.[Asmaa], Schlerf, M.[Martin], Verhoef, W.[Wout], Udelhoven, T.[Thomas],
Introduction of Variable Correlation for the Improved Retrieval of Crop Traits Using Canopy Reflectance Model Inversion,
RS(11), No. 22, 2019, pp. xx-yy.
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Johansen, K., Morton, M.J.L., Malbeteau, Y., Aragon, B., Al-Mashharawi, S., Ziliani, M., Angel, Y., Fiene, G., Negrao, S., Mousa, M.A.A., Tester, M.A., McCabe, M.F.,
Predicting Biomass and Yield At Harvest of Salt-stressed Tomato Plants Using UAV Imagery,
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Lobell, D.B.[David B.], di Tommaso, S.[Stefania], You, C.[Calum], Djima, I.Y.[Ismael Yacoubou], Burke, M.[Marshall], Kilic, T.[Talip],
Sight for Sorghums: Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali,
RS(12), No. 1, 2019, pp. xx-yy.
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ten Harkel, J.[Jelle], Bartholomeus, H.[Harm], Kooistra, L.[Lammert],
Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
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Yin, L.[Leikun], You, N.S.[Nan-Shan], Zhang, G.[Geli], Huang, J.X.[Jian-Xi], Dong, J.[Jinwei],
Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping,
RS(12), No. 1, 2020, pp. xx-yy.
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Momm, H.G.[Henrique G.], El Kadiri, R.[Racha], Porter, W.[Wesley],
Crop-Type Classification for Long-Term Modeling: An Integrated Remote Sensing and Machine Learning Approach,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
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Mohammed, I.[Issamaldin], Marshall, M.[Michael], de Bie, K.[Kees], Estes, L.[Lyndon], Nelson, A.[Andy],
A blended census and multiscale remote sensing approach to probabilistic cropland mapping in complex landscapes,
PandRS(161), 2020, pp. 233-245.
Elsevier DOI 2002
Agricultural production, Landscape stratification, GAMs, NDVI, Proba-V, Landsat BibRef

Garcia-Millan, V.E.[Virginia E.], Rankine, C.[Cassidy], Sanchez-Azofeifa, G.A.[G. Arturo],
Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data,
RS(12), No. 6, 2020, pp. xx-yy.
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Sukhova, E.[Ekaterina], Sukhov, V.[Vladimir],
Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants,
RS(12), No. 8, 2020, pp. xx-yy.
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Böhler, J.E.[Jonas E.], Schaepman, M.E.[Michael E.], Kneubühler, M.[Mathias],
Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data,
RS(12), No. 8, 2020, pp. xx-yy.
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Ma, X.[Xu], Wang, T.J.[Tie-Jun], Lu, L.[Lei],
A Refined Four-Stream Radiative Transfer Model for Row-Planted Crops,
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Guan, Z.[Zhen], Abd-Elrahman, A.[Amr], Fan, Z.[Zhen], Whitaker, V.M.[Vance M.], Wilkinson, B.[Benjamin],
Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images,
PandRS(163), 2020, pp. 171-186.
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And: Corrigendum: PandRS(167), 2020, pp. 189.
Elsevier DOI 2008
Precision agriculture, Phenotyping, close-range Remote Sensing, Biomass modeling, Leaf area modeling, Smoothness metric, Object-based image analysis BibRef

Chen, Y.[Yang], McVicar, T.R.[Tim R.], Donohue, R.J.[Randall J.], Garg, N.[Nikhil], Waldner, F.[François], Ota, N.[Noboru], Li, L.T.[Ling-Tao], Lawes, R.[Roger],
To Blend or Not to Blend? A Framework for Nationwide Landsat-MODIS Data Selection for Crop Yield Prediction,
RS(12), No. 10, 2020, pp. xx-yy.
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Wellington, M.J.[Michael J.], Kuhnert, P.[Petra], Renzullo, L.J.[Luigi J.], Lawes, R.[Roger],
Modelling Within-Season Variation in Light Use Efficiency Enhances Productivity Estimates for Cropland,
RS(14), No. 6, 2022, pp. xx-yy.
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Dhillon, M.S.[Maninder Singh], Dahms, T.[Thorsten], Kuebert-Flock, C.[Carina], Borg, E.[Erik], Conrad, C.[Christopher], Ullmann, T.[Tobias],
Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany,
RS(12), No. 11, 2020, pp. xx-yy.
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Karst, I.G.[Isabel G.], Mank, I.[Isabel], Traoré, I.[Issouf], Sorgho, R.[Raissa], Stückemann, K.J.[Kim-Jana], Simboro, S.[Séraphin], Sié, A.[Ali], Franke, J.[Jonas], Sauerborn, R.[Rainer],
Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
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Alvar-Beltrán, J.[Jorge], Fabbri, C.[Carolina], Verdi, L.[Leonardo], Truschi, S.[Stefania], Marta, A.D.[Anna Dalla], Orlandini, S.[Simone],
Testing Proximal Optical Sensors on Quinoa Growth and Development,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
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Zhao, H.Q.[Heng-Qian], Yang, C.H.[Cheng-Hai], Guo, W.[Wei], Zhang, L.[Lifu], Zhang, D.Y.[Dong-Yan],
Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
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And: Correction: RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
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Beeson, P.C.[Peter C.], Daughtry, C.S.T.[Craig S.T.], Wallander, S.A.[Steven A.],
Estimates of Conservation Tillage Practices Using Landsat Archive,
RS(12), No. 16, 2020, pp. xx-yy.
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Evaluation of tillage practice. BibRef

Melkas, T.[Timo], Riekki, K.[Kirsi], Sorsa, J.A.[Juha-Antti],
Automated Method for Delineating Harvested Stands Based on Harvester Location Data,
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Sánchez-Virosta, Á.[Álvaro], Sánchez-Gómez, D.[David],
Thermography as a Tool to Assess Inter-Cultivar Variability in Garlic Performance along Variations of Soil Water Availability,
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Halubok, M.[Maryia], Yang, Z.L.[Zong-Liang],
Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data,
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Qiu, T.[Tong], Song, C.H.[Cong-He], Li, J.X.[Jun-Xiang],
Deriving Annual Double-Season Cropland Phenology Using Landsat Imagery,
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Messina, G.[Gaetano], Peña, J.M.[Jose M.], Vizzari, M.[Marco], Modica, G.[Giuseppe],
A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the 'Cipolla Rossa di Tropea' (Italy),
RS(12), No. 20, 2020, pp. xx-yy.
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Masjedi, A.[Ali], Crawford, M.M.[Melba M.], Carpenter, N.R.[Neal R.], Tuinstra, M.R.[Mitchell R.],
Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data,
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Pinto, J.[José], Powell, S.[Scott], Peterson, R.[Robert], Rosalen, D.[David], Fernandes, O.[Odair],
Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing,
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Trépos, R.[Ronan], Champolivier, L.[Luc], Dejoux, J.F.[Jean-François], Bitar, A. .A.[Ahmad Al], Casadebaig, P.[Pierre], Debaeke, P.[Philippe],
Forecasting Sunflower Grain Yield by Assimilating Leaf Area Index into a Crop Model,
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Ma, Z.[Zhe], Liu, Z.[Zhe], Zhao, Y.Y.[Yuan-Yuan], Zhang, L.[Lin], Liu, D.[Diyou], Ren, T.W.[Tian-Wei], Zhang, X.D.[Xiao-Dong], Li, S.M.[Shao-Ming],
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Nevavuori, P.[Petteri], Narra, N.[Nathaniel], Linna, P.[Petri], Lipping, T.[Tarmo],
Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models,
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Wang, R.R.[Rui-Rui], Shi, W.[Wei], Dong, P.L.[Pin-Liang],
Mapping Dragon Fruit Croplands from Space Using Remote Sensing of Artificial Light at Night,
RS(12), No. 24, 2020, pp. xx-yy.
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Chamorro Martinez, J.A.[Jorge Andres], Feitosa, R.Q.[Raul Queiroz], Happ, P.N.[Patrick Nigri], Bermudez, J.D.,
Towards Lifelong Crop Recognition Using Fully Convolutional Recurrent Networks and SAR Image Sequences,
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Chamorro Martinez, J.A.[Jorge Andres], Cué La Rosa, L.E.[Laura Elena], Feitosa, R.Q.[Raul Queiroz], Sanches, I.D.[Ieda Del'Arco], Happ, P.N.[Patrick Nigri],
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PandRS(171), 2021, pp. 188-201.
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Convolutional recurrent networks, Fully convolutional networks, Recurrent networks, Remote sensing BibRef

Vuorinne, I.[Ilja], Heiskanen, J.[Janne], Pellikka, P.K.E.[Petri K. E.],
Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices,
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Chang, A.[Anjin], Jung, J.H.[Jin-Ha], Yeom, J.[Junho], Landivar, J.[Juan],
3D Characterization of Sorghum Panicles Using a 3D Point Cloud Derived from UAV Imagery,
RS(13), No. 2, 2021, pp. xx-yy.
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Zheng, C.[Caiwang], Abd-Elrahman, A.[Amr], Whitaker, V.[Vance],
Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming,
RS(13), No. 3, 2021, pp. xx-yy.
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Sagan, V.[Vasit], Maimaitijiang, M.[Maitiniyazi], Bhadra, S.[Sourav], Maimaitiyiming, M.[Matthew], Brown, D.R.[Davis R.], Sidike, P.[Paheding], Fritschi, F.B.[Felix B.],
Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning,
PandRS(174), 2021, pp. 265-281.
Elsevier DOI 2103
PlanetScope, WorldView-3, Deep learning, Convolutionneural network, ResNet, Artificial intelligence, Food security BibRef

Jaafar, H.[Hadi], Mourad, R.[Roya],
GYMEE: A Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine Based on Landsat, Weather, and Soil Data,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
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Adams, T.[Tyler], Bruton, R.[Richard], Ruiz, H.[Henry], Barrios-Perez, I.[Ilse], Selvaraj, M.G.[Michael G.], Hays, D.B.[Dirk B.],
Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Abd-Elrahman, A.[Amr], Wu, F.[Feng], Agehara, S.[Shinsuke], Britt, K.[Katie],
Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches,
IJGI(10), No. 4, 2021, pp. xx-yy.
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Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning,
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DOI Link 2105
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Kwak, G.H.[Geun-Ho], Park, C.W.[Chan-Won], Lee, K.D.[Kyung-Do], Na, S.I.[Sang-Il], Ahn, H.Y.[Ho-Yong], Park, N.W.[No-Wook],
Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data,
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Abdelbaki, A.[Asmaa], Schlerf, M.[Martin], Retzlaff, R.[Rebecca], Machwitz, M.[Miriam], Verrelst, J.[Jochem], Udelhoven, T.[Thomas],
Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging,
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Janoušek, J.[Jirí], Jambor, V.[Václav], Marcon, P.[Petr], Dohnal, P.[Premysl], Synková, H.[Hana], Fiala, P.[Pavel],
Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops,
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Zhu, X.F.[Xiu-Fang], Guo, R.[Rui], Liu, T.T.[Ting-Ting], Xu, K.[Kun],
Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data,
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Chancia, R.[Robert], van Aardt, J.[Jan], Pethybridge, S.[Sarah], Cross, D.[Daniel], Henderson, J.[John],
Predicting Table Beet Root Yield with Multispectral UAS Imagery,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Saif, M.S.[Mohammad S.], Chancia, R.[Robert], Pethybridge, S.[Sarah], Murphy, S.P.[Sean P.], Hassanzadeh, A.[Amirhossein], van Aardt, J.[Jan],
Forecasting Table Beet Root Yield Using Spectral and Textural Features from Hyperspectral UAS Imagery,
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Quemada, C.[Carlos], Pérez-Escudero, J.M.[José M.], Gonzalo, R.[Ramón], Ederra, I.[Iñigo], Santesteban, L.G.[Luis G.], Torres, N.[Nazareth], Iriarte, J.C.[Juan Carlos],
Remote Sensing for Plant Water Content Monitoring: A Review,
RS(13), No. 11, 2021, pp. xx-yy.
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Wang, C.S.[Chun-Shan], Wang, Q.[Qian], Wu, H.[Huarui], Zhao, C.J.[Chun-Jiang], Teng, G.[Guifa], Li, J.X.[Jiu-Xi],
Low-Altitude Remote Sensing Opium Poppy Image Detection Based on Modified YOLOv3,
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DOI Link 2106
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Evans, F.H.[Fiona H.], Shen, J.X.[Jian-Xiu],
Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Lawal, A.F.[Afolarin Fahd], Kerner, H.[Hannah], Becker-Reshef, I.[Inbal], Meyer, S.[Seth],
Mapping the Location and Extent of 2019 Prevent Planting Acres in South Dakota Using Remote Sensing Techniques,
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DOI Link 2107
Prevented planting due to weather. BibRef

Li, G.[Guang], Han, W.T.[Wen-Ting], Huang, S.J.[Shen-Jin], Ma, W.T.[Wei-Tong], Ma, Q.[Qian], Cui, X.[Xin],
Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Fang, P.[Peng], Yan, N.[Nana], Wei, P.P.[Pan-Pan], Zhao, Y.F.[Yi-Fan], Zhang, X.[Xiwang],
Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery,
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DOI Link 2107
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KC, K.[Kushal], Zhao, K.[Kaiguang], Romanko, M.[Matthew], Khanal, S.[Sami],
Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing,
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DOI Link 2107
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Padial-Iglesias, M.[Mario], Serra, P.[Pere], Ninyerola, M.[Miquel], Pons, X.[Xavier],
A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps,
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DOI Link 2107
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Huang, S.J.[Shen-Jin], Han, W.T.[Wen-Ting], Chen, H.P.[Hai-Peng], Li, G.[Guang], Tang, J.D.[Jian-Dong],
Recognizing Zucchinis Intercropped with Sunflowers in UAV Visible Images Using an Improved Method Based on OCRNet,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Zhao, H.W.[Hong-Wei], Duan, S.[Sibo], Liu, J.[Jia], Sun, L.[Liang], Reymondin, L.[Louis],
Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Liu, Y.D.[Ya-Dong], Kim, J.[Junhwan], Fleisher, D.H.[David H.], Kim, K.S.[Kwang-Soo],
Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
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Diao, C.Y.[Chun-Yuan], Yang, Z.J.[Zi-Jun], Gao, F.[Feng], Zhang, X.Y.[Xiao-Yang], Yang, Z.W.[Zheng-Wei],
Hybrid phenology matching model for robust crop phenological retrieval,
PandRS(181), 2021, pp. 308-326.
Elsevier DOI 2110
Phenology, Remote sensing, Agriculture, Crop progress, Planting date BibRef

Teodoro, P.E.[Paulo Eduardo], Ribeiro Teodoro, L.P.[Larissa Pereira], Rojo Baio, F.H.[Fábio Henrique], da Silva Junior, C.A.[Carlos Antonio], dos Santos, R.G.[Regimar Garcia], Marques Ramos, A.P.[Ana Paula], Faita Pinheiro, M.M.[Mayara Maezano], Osco, L.P.[Lucas Prado], Gonçalves, W.N.[Wesley Nunes], Carneiro, A.M.[Alexsandro Monteiro], Junior, J.M.[José Marcato], Pistori, H.[Hemerson], Shiratsuchi, L.S.[Luciano Shozo],
Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data,
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DOI Link 2112
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Fan, J.L.[Jin-Long], Defourny, P.[Pierre], Zhang, X.Y.[Xiao-Yu], Dong, Q.H.[Qing-Han], Wang, L.M.[Li-Min], Qin, Z.H.[Zhi-Hao], de Vroey, M.[Mathilde], Zhao, C.L.[Chun-Liang],
Crop Mapping with Combined Use of European and Chinese Satellite Data,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Quinton, F.[Félix], Landrieu, L.[Loic],
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Johnson, D.M.[David M.], Rosales, A.[Arthur], Mueller, R.[Richard], Reynolds, C.[Curt], Frantz, R.[Ronald], Anyamba, A.[Assaf], Pak, E.[Ed], Tucker, C.[Compton],
USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Aneece, I.[Itiya], Thenkabail, P.S.[Prasad S.],
Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Chen, X.[Xin], Zhang, G.L.[Guo-Liang], Jin, Y.L.[Yu-Ling], Mao, S.C.[Si-Cheng], Laakso, K.[Kati], Sanchez-Azofeifa, A.[Arturo], Jiang, L.[Li], Zhou, Y.[Yi], Zhao, H.[Haile], Yu, L.[Le], Jiang, R.[Rui], Pan, Z.H.[Zhi-Hua], An, P.[Pingli],
Evaluating the Farmland Use Intensity and Its Patterns in a Farming: Pastoral Ecotone of Northern China,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Worrall, G.[George], Rangarajan, A.[Anand], Judge, J.[Jasmeet],
Domain-Guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Azzari, G.[George], Jain, S.[Shruti], Jeffries, G.[Graham], Kilic, T.[Talip], Murray, S.[Siobhan],
Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Haufler, A.F.[Alex F.], Booske, J.H.[John H.], Hagness, S.C.[Susan C.],
Microwave Sensing for Estimating Cranberry Crop Yield: A Pilot Study Using Simulated Canopies and Field Measurement Testbeds,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Microwave theory and techniques, Agriculture, Machine learning, Sensors, Permittivity, Yield estimation, Soil moisture, microwave sensing BibRef

Yu, Y.[Ying], Yang, X.G.[Xi-Guang], Fan, W.Y.[Wen-Yi],
Remote Sensing Inversion of Leaf Maximum Carboxylation Rate Based on a Mechanistic Photosynthetic Model,
GeoRS(60), 2022, pp. 1-12.
IEEE DOI 2112
Mathematical model, Forestry, Vegetation mapping, Nitrogen, Indexes, Biological system modeling, Vegetation, photochemical reflectance index (PRI) BibRef

Shi, Y.[Yue], Han, L.X.[Liang-Xiu], Huang, W.J.[Wen-Jiang], Chang, S.[Sheng], Dong, Y.Y.[Ying-Ying], Dancey, D.[Darren], Han, L.H.[Liang-Hao],
A Biologically Interpretable Two-Stage Deep Neural Network (BIT-DNN) for Vegetation Recognition From Hyperspectral Imagery,
GeoRS(60), 2022, pp. 1-20.
IEEE DOI 2112
Feature extraction, Biological system modeling, Deep learning, Vegetation mapping, Data models, Biology, Neural networks, interpretability BibRef

Hu, X.[Xin], Wang, X.Y.[Xin-Yu], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
S3ANet: Spectral-spatial-scale attention network for end-to-end precise crop classification based on UAV-borne H2 imagery,
PandRS(183), 2022, pp. 147-163.
Elsevier DOI 2201
Precise crop classification, Spectral attention, Spatial attention, Scale attention, WHU-Hi dataset BibRef

Santos, A.F.[Adão F.], Lacerda, L.N.[Lorena N.], Rossi, C.[Chiara], de A. Moreno, L.[Leticia], Oliveira, M.F.[Mailson F.], Pilon, C.[Cristiane], Silva, R.P.[Rouverson P.], Vellidis, G.[George],
Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Li, M.Y.[Meng-Yao], Zhang, R.[Rui], Luo, H.X.[Hong-Xia], Gu, S.W.[Song-Wei], Qin, Z.L.[Zi-Li],
Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Li, K.Y.[Kai-Yun], Sampaio de Lima, R.[Raul], Burnside, N.G.[Niall G.], Vahtmäe, E.[Ele], Kutser, T.[Tiit], Sepp, K.[Karli], Pinheiro, V.H.C.[Victor Henrique Cabral], Yang, M.D.[Ming-Der], Vain, A.[Ants], Sepp, K.[Kalev],
Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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Bojanowski, J.S.[Jedrzej S.], Sikora, S.[Sylwia], Musial, J.P.[Jan P.], Wozniak, E.[Edyta], Dabrowska-Zielinska, K.[Katarzyna], Slesinski, P.[Przemyslaw], Milewski, T.[Tomasz], Laczynski, A.[Artur],
Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-Meteorological Indicators for Operational Crop Yield Forecasting,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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Wang, Q.[Qian], Wang, C.S.[Chun-Shan], Wu, H.R.[Hua-Rui], Zhao, C.J.[Chun-Jiang], Teng, G.F.[Gui-Fa], Yu, Y.J.[Ya-Jie], Zhu, H.[Huaji],
A Two-Stage Low-Altitude Remote Sensing Papaver Somniferum Image Detection System Based on YOLOv5s+DenseNet121,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
opium poppy detection. BibRef

Timmer, B.[Brian], Reshitnyk, L.Y.[Luba Y.], Hessing-Lewis, M.[Margot], Juanes, F.[Francis], Costa, M.[Maycira],
Comparing the Use of Red-Edge and Near-Infrared Wavelength Ranges for Detecting Submerged Kelp Canopy,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Chen, J.[Jianqu], Li, X.[Xunmeng], Wang, K.[Kai], Zhang, S.[Shouyu], Li, J.[Jun],
Estimation of Seaweed Biomass Based on Multispectral UAV in the Intertidal Zone of Gouqi Island,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Muruganantham, P.[Priyanga], Wibowo, S.[Santoso], Grandhi, S.[Srimannarayana], Samrat, N.H.[Nahidul Hoque], Islam, N.[Nahina],
A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Pascual-Venteo, A.B.[Ana B.], Portalés, E.[Enrique], Berger, K.[Katja], Tagliabue, G.[Giulia], Garcia, J.L.[Jose L.], Pérez-Suay, A.[Adrián], Rivera-Caicedo, J.P.[Juan Pablo], Verrelst, J.[Jochem],
Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
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Yao, J.X.[Jin-Xi], Wu, J.[Ji], Xiao, C.Z.[Cheng-Zhi], Zhang, Z.[Zhi], Li, J.Z.[Jian-Zhong],
The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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He, S.[Shan], Peng, P.[Peng], Chen, Y.Y.[Yi-Yun], Wang, X.[Xiaomi],
Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Tian, S.[Shuang], Lu, Q.K.[Qi-Kai], Wei, L.F.[Li-Fei],
Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Manned Hyperspectral Imagery,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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And: Correction: RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Abdelbaki, A.[Asmaa], Udelhoven, T.[Thomas],
A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Revenga, J.C.[Jaime C.], Trepekli, K.[Katerina], Oehmcke, S.[Stefan], Jensen, R.[Rasmus], Li, L.[Lei], Igel, C.[Christian], Gieseke, F.C.[Fabian Cristian], Friborg, T.[Thomas],
Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV-LiDAR and Machine Learning Methods,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
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Sosa-Herrera, J.A.[Jesús A.], Alvarez-Jarquin, N.[Nohemi], Cid-Garcia, N.M.[Nestor M.], López-Araujo, D.J.[Daniela J.], Vallejo-Pérez, M.R.[Moisés R.],
Automated Health Estimation of Capsicum annuum L. Crops by Means of Deep Learning and RGB Aerial Images,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Änäkkälä, M.[Mikael], Lajunen, A.[Antti], Hakojärvi, M.[Mikko], Alakukku, L.[Laura],
Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Wei, S.C.[Si-Cheng], Yang, Y.T.[Yue-Ting], Li, K.W.[Kai-Wei], Guo, Y.[Ying], Zhang, J.[Jiquan],
Three-Dimensional Vulnerability Assessment of Peanut (Arachis hypogaea) Based on Comprehensive Drought Index and Vulnerability Surface: A Case Study of Shandong Province, China,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Bahrami, H.[Hazhir], McNairn, H.[Heather], Mahdianpari, M.[Masoud], Homayouni, S.[Saeid],
A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization,
RS(14), No. 22, 2022, pp. xx-yy.
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Zhao, L.C.[Long-Cai], Li, Q.Z.[Qiang-Zi], Chang, Q.R.[Qing-Rui], Shang, J.L.[Jia-Li], Du, X.[Xin], Liu, J.G.[Jian-Gui], Dong, T.F.[Tai-Feng],
In-season crop type identification using optimal feature knowledge graph,
PandRS(194), 2022, pp. 250-266.
Elsevier DOI 2212
Crop type, Automatic identification, Optimal identification feature, Knowledge graph, Remote sensing BibRef

Dokoohaki, H.[Hamze], Rai, T.[Teerath], Kivi, M.[Marissa], Lewis, P.[Philip], Gómez-Dans, J.L.[Jose L.], Yin, F.[Feng],
Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Wang, Y.X.[Yu-Xian], Fang, Y.[Yuan], Zhong, W.L.[Wen-Long], Zhuo, R.M.[Rong-Ming], Peng, J.H.[Jun-Huan], Xu, L.L.[Lin-Lin],
A Spatial-Temporal Depth-Wise Residual Network for Crop Sub-Pixel Mapping from MODIS Images,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Zhou, W.[Wu], Zhao, L.[Li], Hu, Y.M.[Yue-Ming], Liu, Z.H.[Zhen-Hua], Wang, L.[Lu], Ye, C.[Changdong], Mao, X.Y.[Xiao-Yun], Xie, X.[Xia],
Cultivated Land Quality Evaluated Using the RNN Algorithm Based on Multisource Data,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Zhao, H.[Hailan], Meng, J.[Jihua], Shi, T.T.[Ting-Ting], Zhang, X.B.[Xiao-Bo], Wang, Y.[Yanan], Luo, X.J.[Xiang-Jiang], Lin, Z.X.[Zhen-Xin], You, X.Y.[Xin-Yan],
Validating the Crop Identification Capability of the Spectral Variance at Key Stages (SVKS) Computed via an Object Self-Reference Combined Algorithm,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
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Zhang, Z.Y.[Zhuo-Yao], Liu, X.N.[Xiang-Nan], Zhu, L.H.[Li-Hong], Li, J.J.[Jun-Ji], Zhang, Y.[Yue],
Remote Sensing Extraction Method of Illicium verum Based on Functional Characteristics of Vegetation Canopy,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
Star anise BibRef

Wu, Y.C.[Yong-Chuang], Wu, Y.L.[Yan-Lan], Wang, B.[Biao], Yang, H.[Hui],
A Remote Sensing Method for Crop Mapping Based on Multiscale Neighborhood Feature Extraction,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Zhang, W.T.[Wei-Tao], Zheng, S.D.[Sheng-Di], Li, Y.B.[Yi-Bang], Guo, J.[Jiao], Wang, H.[Hui],
A Full Tensor Decomposition Network for Crop Classification with Polarization Extension,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Jiang, D.P.[Da-Peng], Du, J.[Jia], Song, K.[Kaishan], Zhao, B.[Boyu], Zhang, Y.W.[Yi-Wei], Zhang, W.J.[Wei-Jian],
Classification of Conservation Tillage Using Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Zhang, W.[Wangle], Wang, J.W.[Ji-Wen], Lin, H.[Hate], Cong, M.[Ming], Wan, Y.[Yue], Zhang, J.X.[Jing-Xiong],
Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Wei, M.F.[Meng-Fan], Wang, H.Y.[Hong-Yan], Zhang, Y.[Yuan], Li, Q.Z.[Qiang-Zi], Du, X.[Xin], Shi, G.W.[Guan-Wei], Ren, Y.T.[Yi-Ting],
Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Weilandt, F.[Frank], Behling, R.[Robert], Goncalves, R.[Romulo], Madadi, A.[Arash], Richter, L.[Lorenz], Sanona, T.[Tiago], Spengler, D.[Daniel], Welsch, J.[Jona],
Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Wu, Y.C.[Yong-Chuang], Wu, P.H.[Peng-Hai], Wu, Y.[Yanlan], Yang, H.[Hui], Wang, B.[Biao],
Remote Sensing Crop Recognition by Coupling Phenological Features and Off-Center Bayesian Deep Learning,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Rußwurm, M.[Marc], Courty, N.[Nicolas], Emonet, R.[Rémi], Lefèvre, S.[Sébastien], Tuia, D.[Devis], Tavenard, R.[Romain],
End-to-end learned early classification of time series for in-season crop type mapping,
PandRS(196), 2023, pp. 445-456.
Elsevier DOI 2302
Deep learning, Satellite time series, Early classification, Crop type mapping, In-season crop type mapping BibRef

Shao, S.[Shuai], Takeuchi, W.[Wataru],
Bio-Geophysical Suitability Mapping for Chinese Cabbage of East Asia from 2001 to 2020,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303
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Xiang, J.J.[Jian-Jian], Liu, J.[Jia], Chen, D.[Du], Xiong, Q.[Qi], Deng, C.[Chongjiu],
CTFuseNet: A Multi-Scale CNN-Transformer Feature Fused Network for Crop Type Segmentation on UAV Remote Sensing Imagery,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Lyle, G.[Greg], Clarke, K.[Kenneth], Kilpatrick, A.[Adam], Summers, D.M.[David McCulloch], Ostendorf, B.[Bertram],
A Spatial and Temporal Evaluation of Broad-Scale Yield Predictions Created from Yield Mapping Technology and Landsat Satellite Imagery in the Australian Mediterranean Dryland Cropping Region,
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link 2303
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Kümmerer, R.[Robin], Noack, P.O.[Patrick Ole], Bauer, B.[Bernhard],
Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass,
RS(15), No. 6, 2023, pp. 1520.
DOI Link 2304
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Shao, C.Y.[Cong-Ying], Shuai, Y.M.[Yan-Min], Wu, H.[Hao], Deng, X.L.[Xiao-Lian], Zhang, X.C.[Xue-Cong], Xu, A.G.[Ai-Gong],
Development of a Spectral Index for the Detection of Yellow-Flowering Vegetation,
RS(15), No. 7, 2023, pp. 1725.
DOI Link 2304
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Karaca, C.[Cihan], Thompson, R.B.[Rodney B.], Peña-Fleitas, M.T.[M. Teresa], Gallardo, M.[Marisa], Padilla, F.M.[Francisco M.],
Evaluation of Absolute Measurements and Normalized Indices of Proximal Optical Sensors as Estimators of Yield in Muskmelon and Sweet Pepper,
RS(15), No. 8, 2023, pp. 2174.
DOI Link 2305
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Wang, X.[Xiaohu], Fang, S.F.[Shi-Feng], Yang, Y.C.[Yi-Chen], Du, J.Q.[Jia-Qiang], Wu, H.[Hua],
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Yu, F.[Feng], Zhang, Q.[Qian], Xiao, J.[Jun], Ma, Y.T.[Yun-Tao], Wang, M.[Ming], Luan, R.P.[Ru-Peng], Liu, X.[Xin], Ping, Y.[Yang], Nie, Y.[Ying], Tao, Z.Y.[Zhen-Yu], Zhang, H.[Hui],
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PandRS(202), 2023, pp. 334-355.
Elsevier DOI 2308
Planting date, Remote sensing, Crop growth model, Phenology BibRef

Lee, K.[Kangbeen], Han, X.Z.[Xiong-Zhe],
A Study on Leveraging Unmanned Aerial Vehicle Collaborative Driving and Aerial Photography Systems to Improve the Accuracy of Crop Phenotyping,
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Ma, Y.C.[Yu-Chi], Yang, Z.W.[Zheng-Wei], Huang, Q.Y.[Qun-Ying], Zhang, Z.[Zhou],
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Multi-year hyperspectral remote sensing of a comprehensive set of crop foliar nutrients in cranberries,
PandRS(205), 2023, pp. 135-146.
Elsevier DOI 2311
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Meng, J.[Jihua], You, X.Y.[Xin-Yan], Zhang, X.B.[Xiao-Bo], Shi, T.T.[Ting-Ting], Zhang, L.[Lei], Chen, X.F.[Xing-Feng], Zhao, H.[Hailan], Xu, M.[Meng],
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Wang, X.M.[Xiao-Mi], Liu, J.H.[Jiu-Hong], Peng, P.[Peng], Chen, Y.Y.[Yi-Yun], He, S.[Shan], Yang, K.[Kang],
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Ishaq, R.A.F.[Rana Ahmad Faraz], Zhou, G.H.[Guan-Hua], Tian, C.[Chen], Tan, Y.M.[Yu-Min], Jing, G.[Guifei], Jiang, H.Z.[Hong-Zhi], ur-Rehman, O.[Obaid],
A Systematic Review of Radiative Transfer Models for Crop Yield Prediction and Crop Traits Retrieval,
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Zhan, W.F.[Wen-Fang], Luo, F.[Feng], Luo, H.[Heng], Li, J.L.[Jun-Li], Wu, Y.[Yongchuang], Yin, Z.X.[Zhi-Xiang], Wu, Y.[Yanlan], Wu, P.[Penghai],
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Chen, M.N.[Meng-Na], Zhang, R.[Rong], Jia, M.M.[Ming-Ming], Cheng, L.[Lina], Zhao, C.P.[Chuan-Peng], Li, H.Y.[Hui-Ying], Wang, Z.M.[Zong-Ming],
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Lv, Y.[Yan], Feng, W.[Wei], Wang, S.[Shuo], Wang, S.Y.[Shi-Yu], Guo, L.[Liang], Dauphin, G.[Gabriel],
An Ensemble-Based Framework for Sophisticated Crop Classification Exploiting Google Earth Engine,
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Yu, F.[Feng], Wang, M.[Ming], Xiao, J.[Jun], Zhang, Q.[Qian], Zhang, J.M.[Jin-Meng], Liu, X.[Xin], Ping, Y.[Yang], Luan, R.P.[Ru-Peng],
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Gong, J.L.[Jia-Long], Zhong, X.[Xing], Zhu, R.F.[Rui-Fei], Xu, Z.X.[Zhao-Xin], Wang, D.[Dong], Yin, J.[Jian],
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Breunig, F.M.[Fábio Marcelo], Dalagnol, R.[Ricardo], Galvão, L.S.[Lênio Soares], da Conceição-Bispo, P.[Polyanna], Liu, Q.[Qing], Berra, E.F.[Elias Fernando], Gaida, W.[William], Liesenberg, V.[Veraldo], Sampaio, T.V.M.[Tony Vinicius Moreira],
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PandRS(215), 2024, pp. 271-291.
Elsevier DOI 2408
Crop mapping, Crop phenology, Near real-time, Deep learning, Agriculture BibRef

Angarano, S.[Simone], Martini, M.[Mauro], Navone, A.[Alessandro], Chiaberge, M.[Marcello],
Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation,
AgriVision24(5450-5459)
IEEE DOI 2410
Training, Precision agriculture, Adaptation models, Service robots, Semantic segmentation, Crops, Spraying, Domain Generalization, Agriculture BibRef

Chen, H.L.[Hui-Ling], He, G.J.[Guo-Jin], Peng, X.[Xueli], Wang, G.Z.[Gui-Zhou], Yin, R.[Ranyu],
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Zhang, X.Y.[Xin-Yu], Cai, Z.W.[Zhi-Wen], Hu, Q.[Qiong], Yang, J.Y.[Jing-Ya], Wei, H.D.[Hao-Dong], You, L.Z.[Liang-Zhi], Xu, B.D.[Bao-Dong],
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PandRS(218), 2024, pp. 87-101.
Elsevier DOI 2412
Crop type mapping, Temporal random masking, Spatial information aggregation, Satellite image time series BibRef

Joshi, A.[Abhasha], Pradhan, B.[Biswajeet], Chakraborty, S.[Subrata], Varatharajoo, R.[Renuganth], Gite, S.[Shilpa], Alamri, A.[Abdullah],
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Nau, A.W.[Amy W.], Lucieer, V.[Vanessa], Schimel, A.C.G.[Alexandre C. G.], Kunnath, H.[Haris], Ladroit, Y.[Yoann], Martin, T.[Tara],
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CVPR24(17201-17211)
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Charge coupled devices, Visualization, Crops, Object detection, Predictive models, Logic gates BibRef

Mohan, A.[Akshatha], Peeples, J.[Joshua],
Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis,
AgriVision24(5384-5392)
IEEE DOI 2410
Adaptation models, Accuracy, Computational modeling, Computer architecture, Feature extraction, Image Classification BibRef

Zawish, M.[Muhammad], Albert, P.[Paul], Esposito, F.[Flavio], Davy, S.[Steven], Abraham, L.[Lizy],
Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge,
AgriVision24(5357-5365)
IEEE DOI 2410
Deep learning, Accuracy, Uncertainty, Image edge detection, Filtering algorithms, Predictive models, Prediction algorithms, uncertainty modeling BibRef

Lin, F.D.[Fu-Dong], Crawford, S.[Summer], Guillot, K.[Kaleb], Zhang, Y.[Yihe], Chen, Y.[Yan], Yuan, X.[Xu], Chen, L.[Li], Williams, S.[Shelby], Minvielle, R.[Robert], Xiao, X.M.[Xiang-Ming], Gholson, D.[Drew], Ashwell, N.[Nicolas], Setiyono, T.[Tri], Tubana, B.[Brenda], Peng, L.[Lu], Bayoumi, M.[Magdy], Tzeng, N.F.[Nian-Feng],
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Casado-García, Á.[Ángela], Heras, J.[Jónathan], Martínez-Goñi, X.S.[Xabier Simon], Miranda-Apodaca, J.[Jon], Pérez-López, U.[Usue],
Estimation of Crop Production by Fusing Images and Crop Features,
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Baghdasaryan, L.[Liana], Melikbekyan, R.[Razmik], Dolmajain, A.[Arthur], Hobbs, J.[Jennifer],
Deep density estimation based on multi-spectral remote sensing data for in-field crop yield forecasting,
WiCV22(2013-2022)
IEEE DOI 2210
Deep learning, Satellites, Government, Estimation, Crops BibRef

dos Santos Oliveira, W.C.[Walysson Carlos], Junior, G.B.[Geraldo Braz], Junior, D.L.G.[Daniel Lima Gomes], de Paiva, A.C.[Anselmo Cardoso], Sousa de Almeida, J.D.[Joao Dallyson],
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Narin, O.G., Sekertekin, A., Saygin, A., Balik Sanli, F., Gullu, M.,
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Wang, Z.Q.[Zi-Qiao], Zhang, H.Y.[Hong-Yan], He, W.[Wei], Zhang, L.P.[Liang-Pei],
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AgriVision21(2934-2943)
IEEE DOI 2109
Training, Adaptation models, Time series analysis, Feature extraction, Agriculture BibRef

Tseng, G.[Gabriel], Kerner, H.[Hannah], Nakalembe, C.[Catherine], Becker-Reshef, I.[Inbal],
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EarthVision21(1111-1120)
IEEE DOI 2109
Training, Geography, Satellites, Machine learning, Agriculture BibRef

Herrero-Huerta, M., Rahmani, S.R., Rainey, K.M.,
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Choros, T., Oberski, T., Kogut, T.,
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Sagan, V., Maimaitijiang, M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., Peterson, K.T., Peterson, J., Burken, J., Fritschi, F.,
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Sibanda, M., Mutanga, O., Magwaza, L.S., Dube, T., Magwaza, S.T., Odindo, A.O., Mditshwa, A., Mafongoya, P.L.,
Discrimination of Tomato Plants (solanum Lycopersicum) Grown Under Anaerobic Baffled Reactor Effluent, Nitrified Urine Concentrate And Commercial Hydroponic Fertilizer Regimes Using Multi-source Satellite,
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Ahmed, I., Eramian, M., Ovsyannikov, I., van der Kamp, W., Nielsen, K., Duddu, H.S., Rumali, A., Shirtliffe, S., Bett, K.,
Automatic Detection and Segmentation of Lentil Crop Breeding Plots From Multi-Spectral Images Captured by UAV-Mounted Camera,
WACV19(1673-1681)
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autonomous aerial vehicles, biology computing, crops, Gaussian processes, image segmentation, object detection, Data mining BibRef

Rajapaksa, S., Eramian, M., Duddu, H., Wang, M., Shirtliffe, S., Ryu, S., Josuttes, A., Zhang, T., Vail, S., Pozniak, C., Parkin, I.,
Classification of Crop Lodging with Gray Level Co-occurrence Matrix,
WACV18(251-258)
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Gabor filters, agriculture, crops, image texture, support vector machines, GLCM features, Training BibRef

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EarthVision17(1496-1504)
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Agriculture, Earth, Logic gates, Recurrent neural networks, Remote sensing, Satellites, Vegetation, mapping BibRef

Olsen, S.I.[Søren I.], Nielsen, J.[Jon], Rasmussen, J.[Jesper],
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Gibson, D.[David], Burghardt, T.[Tilo], Campbell, N.[Neill], Canagarajah, N.[Nishan],
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Upadhyay, P., Ghosh, S.K., Kumar, A.,
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BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Gross Primary Production, Net Primary Production, GPP, NPP .