23.2.8.4 Rice Crop Analysis, Production, Detection, Health, Change

Chapter Contents (Back)
Classification. Rice Classification. Rice Yield.
See also Agricultural Field Extraction.
See also Leaf Nitrogen, Crop Nitrogen.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

Song, S.[Shalei], Gong, W.[Wei], Zhu, B.[Bo], Huang, X.[Xin],
Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance,
PandRS(66), No. 5, September 2011, pp. 672-682.
Elsevier DOI 1110
Hyperspectral data; Wavelength selection; Spectral discrimination; Rice BibRef

Sakamoto, T.[Toshihiro], Shibayama, M.[Michio], Kimura, A.[Akihiko], Takada, E.[Eiji],
Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth,
PandRS(66), No. 6, November 2011, pp. 872-882.
Elsevier DOI 1112
Crop phenology; Active sensing; Flashlight BibRef

Lopez-Sanchez, J.M., Cloude, S.R., Ballester-Berman, J.D.,
Rice Phenology Monitoring by Means of SAR Polarimetry at X-Band,
GeoRS(50), No. 7, July 2012, pp. 2695-2709.
IEEE DOI 1208
BibRef

Hosoi, F.[Fumiki], Omasa, K.[Kenji],
Estimation of vertical plant area density profiles in a rice canopy at different growth stages by high-resolution portable scanning lidar with a lightweight mirror,
PandRS(74), No. 1, November 2012, pp. 11-19.
Elsevier DOI 1212
BibRef
Earlier:
Estimating Vertical Leaf Area Density Profiles of Tree Canopies Using Three-Dimensional Portable Lidar Imaging,
Laser09(152). 0909
Laser beam coverage index; Plant area density; Portable scanning lidar; Rice; Three-dimensional imaging; Voxel-based canopy profiling BibRef

Gnyp, M.L.[Martin Leon], Yu, K.[Kang], Aasen, H.[Helge], Yao, Y.K.[Yin-Kun], Huang, S.Y.[Shan-Yu], Miao, Y.X.[Yu-Xin], Bareth, G.[Georg],
Analysis of Crop Reflectance for Estimating Biomass in Rice Canopies at Different Phenological Stages,
PFG(2013), No. 4, 2013, pp. 351-365.
DOI Link 1309
BibRef

Gnyp, M.L., Yao, Y.K., Yu, K., Huang, S.Y., Aasen, H., Lenz-Wiedemann, V.I.S., Miao, Y.X., Bareth, G.,
Hyperspectral Analysis Of Rice Phenological Stages In Northeast China,
AnnalsPRS(I-7), No. 2012, pp. 77-82.
DOI Link 1209
BibRef

Son, N.T., Chen, C.F., Chen, C.R., Chang, L.Y.,
Satellite-based investigation of flood-affected rice cultivation areas in Chao Phraya River Delta, Thailand,
PandRS(86), No. 1, 2013, pp. 77-88.
Elsevier DOI 1312
MODIS BibRef

Son, N.T.[Nguyen-Thanh], Chen, C.F.[Chi-Farn], Chen, C.R.[Cheng-Ru], Duc, H.N.[Huynh-Ngoc], Chang, L.Y.[Ly-Yu],
A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam,
RS(6), No. 1, 2013, pp. 135-156.
DOI Link 1402
BibRef

Maki, M.[Masayasu], Homma, K.[Koki],
Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale,
RS(6), No. 6, 2014, pp. 4764-4779.
DOI Link 1407
BibRef

Mosleh, M.K.[Mostafa K.], Hassan, Q.K.[Quazi K.],
Development of a Remote Sensing-Based 'Boro' Rice Mapping System,
RS(6), No. 3, 2014, pp. 1938-1953.
DOI Link 1404
BibRef

Karila, K.[Kirsi], Nevalainen, O.[Olli], Krooks, A.[Anssi], Karjalainen, M.[Mika], Kaasalainen, S.[Sanna],
Monitoring Changes in Rice Cultivated Area from SAR and Optical Satellite Images in Ben Tre and Tra Vinh Provinces in Mekong Delta, Vietnam,
RS(6), No. 5, 2014, pp. 4090-4108.
DOI Link 1407
BibRef

Gumma, M.K.[Murali Krishna], Thenkabail, P.S.[Prasad S.], Maunahan, A.[Aileen], Islam, S.[Saidul], Nelson, A.[Andrew],
Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 mu-m data for the year 2010,
PandRS(91), No. 1, 2014, pp. 98-113.
Elsevier DOI 1404
Seasonal rice mapping BibRef

Lopez-Sanchez, J.M., Vicente-Guijalba, F., Ballester-Berman, J.D., Cloude, S.R.,
Polarimetric Response of Rice Fields at C-Band: Analysis and Phenology Retrieval,
GeoRS(52), No. 5, May 2014, pp. 2977-2993.
IEEE DOI 1403
Backscatter BibRef

Inoue, Y.[Yoshio], Sakaiya, E.[Eiji], Wang, C.Z.[Cui-Zhen],
Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice,
RS(6), No. 7, 2014, pp. 5995-6019.
DOI Link 1408
BibRef

Zhou, K.[Kai], Guo, Y.J.[Yong-Jiu], Geng, Y.[Yanan], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Tian, Y.C.[Yong-Chao],
Development of a Novel Bidirectional Canopy Reflectance Model for Row-Planted Rice and Wheat,
RS(6), No. 8, 2014, pp. 7632-7659.
DOI Link 1410
BibRef

Rossi, C., Erten, E.,
Paddy-Rice Monitoring Using TanDEM-X,
GeoRS(53), No. 2, February 2015, pp. 900-910.
IEEE DOI 1411
crops BibRef

Nelson, A.[Andrew], Setiyono, T.[Tri], Rala, A.B.[Arnel B.], Quicho, E.D.[Emma D.], Raviz, J.V.[Jeny V.], Abonete, P.J.[Prosperidad J.], Maunahan, A.A.[Aileen A.], Garcia, C.A.[Cornelia A.], Bhatti, H.Z.M.[Hannah Zarah M.], Villano, L.S.[Lorena S.], Thongbai, P.[Pongmanee], Holecz, F.[Francesco], Barbieri, M.[Massimo], Collivignarelli, F.[Francesco], Gatti, L.[Luca], Quilang, E.J.P.[Eduardo Jimmy P.], Mabalay, M.R.O.[Mary Rose O.], Mabalot, P.E.[Pristine E.], Barroga, M.I.[Mabel I.], Bacong, A.P.[Alfie P.], Detoito, N.T.[Norlyn T.], Berja, G.B.[Glorie Belle], Varquez, F.[Frenciso], Wahyunto, Kuntjoro, D.[Dwi], Murdiyati, S.R.[Sri Retno], Pazhanivelan, S.[Sellaperumal], Kannan, P.[Pandian], Mary, P.C.N.[Petchimuthu Christy Nirmala], Subramanian, E.[Elangovan], Rakwatin, P.[Preesan], Intrman, A.[Amornrat], Setapayak, T.[Thana], Lertna, S.[Sommai], Minh, V.Q.[Vo Quang], Tuan, V.Q.[Vo Quoc], Duong, T.H.[Trinh Hoang], Quyen, N.H.[Nguyen Huu], Kham, D.V.[Duong Van], Hin, S.[Sarith], Veasna, T.[Touch], Yadav, M.[Manoj], Chin, C.[Chharom], Ninh, N.H.[Nguyen Hong],
Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project,
RS(6), No. 11, 2014, pp. 10773-10812.
DOI Link 1412
BibRef

Asilo, S.[Sonia], de Bie, K.[Kees], Skidmore, A.[Andrew], Nelson, A.[Andrew], Barbieri, M.[Massimo], Maunahan, A.[Aileen],
Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR,
RS(6), No. 12, 2014, pp. 12789-12814.
DOI Link 1412
BibRef

Zhao, Q.Y.[Quan-Ying], Lenz-Wiedemann, V.I.S.[Victoria I.S.], Yuan, F.[Fei], Jiang, R.F.[Rong-Feng], Miao, Y.X.[Yu-Xin], Zhang, F.[Fusuo], Bareth, G.[Georg],
Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China,
IJGI(4), No. 1, 2015, pp. 236-261.
DOI Link 1502
BibRef

Tornos, L.[Lucia], Huesca, M.[Margarita], Dominguez, J.A.[Jose Antonio], Moyano, M.C.[Maria Carmen], Cicuendez, V.[Victor], Recuero, L.[Laura], Palacios-Orueta, A.[Alicia],
Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod,
PandRS(101), No. 1, 2015, pp. 110-124.
Elsevier DOI 1503
Agriculture BibRef

Yu, K.[Kang], Gnyp, M.L.[Martin Leon], Gao, L.[Lei], Miao, Y.X.[Yu-Xin], Cheng, X.P.[Xin-Ping], Bareth, G.[Georg],
Estimate Leaf Chlorophyll of Rice Using Reflectance Indices and Partial Least Squares,
PFG(2015), No. 1, 2015, pp. 45-54.
DOI Link 1503
BibRef

Wang, J.[Jing], Huang, J.F.[Jing-Feng], Zhang, K.Y.[Kang-Yu], Li, X.X.[Xin-Xing], She, B.[Bao], Wei, C.W.[Chuan-Wen], Gao, J.[Jian], Song, X.D.[Xiao-Dong],
Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images,
RS(7), No. 4, 2015, pp. 3467-3488.
DOI Link 1505
BibRef

Qin, Y.W.[Yuan-Wei], Xiao, X.M.[Xiang-Ming], Dong, J.W.[Jin-Wei], Zhou, Y.T.[Yu-Ting], Zhu, Z.[Zhe], Zhang, G.[Geli], Du, G.M.[Guo-Ming], Jin, C.[Cui], Kou, W.L.[Wei-Li], Wang, J.[Jie], Li, X.P.[Xiang-Ping],
Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery,
PandRS(105), No. 1, 2015, pp. 220-233.
Elsevier DOI 1506
Rice paddy BibRef

Zhang, G.[Geli], Xiao, X.M.[Xiang-Ming], Dong, J.W.[Jin-Wei], Kou, W.L.[Wei-Li], Jin, C.[Cui], Qin, Y.W.[Yuan-Wei], Zhou, Y.T.[Yu-Ting], Wang, J.[Jie], Menarguez, M.A.[Michael Angelo], Biradar, C.[Chandrashekhar],
Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data,
PandRS(106), No. 1, 2015, pp. 157-171.
Elsevier DOI 1507
Paddy rice fields BibRef

Guo, Y.J.[Yong-Jiu], Zhang, L.[Ling], Qin, Y.[Yehui], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Tian, Y.C.[Yong-Chao],
Exploring the Vertical Distribution of Structural Parameters and Light Radiation in Rice Canopies by the Coupling Model and Remote Sensing,
RS(7), No. 5, 2015, pp. 5203-5221.
DOI Link 1506
BibRef

Boschetti, M.[Mirco], Nelson, A.[Andrew], Nutini, F.[Francesco], Manfron, G.[Giacinto], Busetto, L.[Lorenzo], Barbieri, M.[Massimo], Laborte, A.[Alice], Raviz, J.[Jeny], Holecz, F.[Francesco], Mabalay, M.R.O.[Mary Rose O.], Bacong, A.P.[Alfie P.], Quilang, E.J.P.[Eduardo Jimmy P.],
Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan,
RS(7), No. 6, 2015, pp. 6535.
DOI Link 1507
BibRef

Teluguntla, P.[Pardhasaradhi], Ryu, D.[Dongryeol], George, B.[Biju], Walker, J.P.[Jeffrey P.], Malano, H.M.[Hector M.],
Mapping Flooded Rice Paddies Using Time Series of MODIS Imagery in the Krishna River Basin, India,
RS(7), No. 7, 2015, pp. 8858.
DOI Link 1506
BibRef

Shi, J.J.[Jing-Jing], Huang, J.F.[Jing-Feng],
Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images,
RS(7), No. 7, 2015, pp. 8883.
DOI Link 1506
BibRef

Yeom, J.M.[Jong-Min], Kim, H.O.[Hyun-Ok],
Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice,
RS(7), No. 9, 2015, pp. 11326.
DOI Link 1511
BibRef

Nguyen, D.B.[Duy Ba], Clauss, K.[Kersten], Cao, S.[Senmao], Naeimi, V.[Vahid], Kuenzer, C.[Claudia], Wagner, W.[Wolfgang],
Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data,
RS(7), No. 12, 2015, pp. 15808.
DOI Link 1601
BibRef

Kwak, Y.J.[Young-Joo], Arifuzzanman, B.[Bhuyan], Iwami, Y.[Yoichi],
Prompt Proxy Mapping of Flood Damaged Rice Fields Using MODIS-Derived Indices,
RS(7), No. 12, 2015, pp. 15805.
DOI Link 1601
BibRef

Guan, X.D.[Xu-Dong], Huang, C.[Chong], Liu, G.[Gaohuan], Meng, X.L.[Xue-Lian], Liu, Q.S.[Qing-Sheng],
Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance,
RS(8), No. 1, 2016, pp. 19.
DOI Link 1602
BibRef

Campos-Taberner, M.[Manuel], García-Haro, F.J.[Franciso Javier], Confalonieri, R.[Roberto], Martínez, B.[Beatriz], Moreno, Á.[Álvaro], Sánchez-Ruiz, S.[Sergio], Gilabert, M.A.[María Amparo], Camacho, F.[Fernando], Boschetti, M.[Mirco], Busetto, L.[Lorenzo],
Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI,
RS(8), No. 3, 2016, pp. 202.
DOI Link 1604
BibRef

Clauss, K.[Kersten], Yan, H.M.[Hui-Min], Kuenzer, C.[Claudia],
Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series,
RS(8), No. 5, 2016, pp. 434.
DOI Link 1606
BibRef

Singha, M.[Mrinal], Wu, B.F.[Bing-Fang], Zhang, M.[Miao],
An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India,
RS(8), No. 6, 2016, pp. 479.
DOI Link 1608
BibRef

Liu, Y., Chen, K.S., Xu, P., Li, Z.L.,
Modeling and Characteristics of Microwave Backscattering From Rice Canopy Over Growth Stages,
GeoRS(54), No. 11, November 2016, pp. 6757-6770.
IEEE DOI 1610
Agriculture BibRef

Dong, J.W.[Jin-Wei], Xiao, X.M.[Xiang-Ming],
Evolution of regional to global paddy rice mapping methods: A review,
PandRS(119), No. 1, 2016, pp. 214-227.
Elsevier DOI 1610
Paddy rice mapping BibRef

Yang, Z.[Zhi], Li, K.[Kun], Shao, Y.[Yun], Brisco, B.[Brian], Liu, L.[Long],
Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images,
RS(8), No. 10, 2016, pp. 878.
DOI Link 1609
BibRef

Wang, J.[Jing], Huang, J.F.[Jing-Feng], Gao, P.[Ping], Wei, C.[Chuanwen], Mansaray, L.R.[Lamin R.],
Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data,
RS(8), No. 11, 2016, pp. 931.
DOI Link 1612
BibRef
And: Correction: RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Lee, B.[Bora], Kwon, H.[Hyojung], Miyata, A.[Akira], Lindner, S.[Steve], Tenhunen, J.[John],
Evaluation of a Phenology-Dependent Response Method for Estimating Leaf Area Index of Rice Across Climate Gradients,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Naito, H.[Hiroki], Ogawa, S.[Satoshi], Valencia, M.O.[Milton Orlando], Mohri, H.[Hiroki], Urano, Y.[Yutaka], Hosoi, F.[Fumiki], Shimizu, Y.[Yo], Chavez, A.L.[Alba Lucia], Ishitani, M.[Manabu], Selvaraj, M.G.[Michael Gomez], Omasa, K.[Kenji],
Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras,
PandRS(125), No. 1, 2017, pp. 50-62.
Elsevier DOI 1703
Breeding BibRef

Torbick, N.[Nathan], Chowdhury, D.[Diya], Salas, W.[William], Qi, J.G.[Jia-Guo],
Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Cheng, T.[Tao], Song, R.Z.[Ren-Zhong], Li, D.[Dong], Zhou, K.[Kai], Zheng, H.B.[Heng-Biao], Yao, X.[Xia], Tian, Y.C.[Yong-Chao], Cao, W.X.[Wei-Xing], Zhu, Y.[Yan],
Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Campos-Taberner, M.[Manuel], García-Haro, F.J.[Francisco Javier], Camps-Valls, G.[Gustau], Grau-Muedra, G.[Gonçal], Nutini, F.[Francesco], Busetto, L.[Lorenzo], Katsantonis, D.[Dimitrios], Stavrakoudis, D.[Dimitris], Minakou, C.[Chara], Gatti, L.[Luca], Barbieri, M.[Massimo], Holecz, F.[Francesco], Stroppiana, D.[Daniela], Boschetti, M.[Mirco],
Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Mansaray, L.R.[Lamin R.], Huang, W.J.[Wei-Jiao], Zhang, D.D.[Dong-Dong], Huang, J.F.[Jing-Feng], Li, J.[Jun],
Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Arii, M., Yamada, H., Kobayashi, T., Kojima, S., Umehara, T., Komatsu, T., Nishimura, T.,
Theoretical Characterization of X-Band Multiincidence Angle and Multipolarimetric SAR Data From Rice Paddies at Late Vegetative Stage,
GeoRS(55), No. 5, May 2017, pp. 2706-2715.
IEEE DOI 1705
geophysical techniques, radar polarimetry, synthetic aperture radar, vegetation, Japan, MIMP SAR observation, Niigata City, Pi-SAR2, SAR, X-band multiincidence angle, X-band polarimetric-interferometric SAR 2, discrete scatterer model, late vegetative stage, multiincidence angle-multipolarimetric, multipolarimetric SAR data, polarimetric decomposition techniques, radar backscatter, rice paddies, scattering mechanism, synthetic aperture radar, BibRef

Arii, M., Yamada, H., Ohki, M.,
Characterization of L-Band MIMP SAR Data From Rice Paddies at Late Vegetative Stage,
GeoRS(56), No. 7, July 2018, pp. 3852-3860.
IEEE DOI 1807
Backscatter, Data models, L-band, Radar polarimetry, Scattering, Synthetic aperture radar, Discrete scatterer model (DSM), rice paddies BibRef

Arii, M., Yamada, H., Kojima, S., Ohki, M.,
Sensitivity Analysis of Multifrequency MIMP SAR Data From Rice Paddies,
GeoRS(57), No. 6, June 2019, pp. 3543-3551.
IEEE DOI 1906
Synthetic aperture radar, Scattering, L-band, Backscatter, Data models, Sensitivity analysis, rice paddies BibRef

Yuzugullu, O.[Onur], Marelli, S.[Stefano], Erten, E.[Esra], Sudret, B.[Bruno], Hajnsek, I.[Irena],
Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Zhou, G.X.[Gao-Xiang], Liu, X.N.[Xiang-Nan], Liu, M.[Ming],
Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Yang, M.D.[Ming-Der], Huang, K.S.[Kai-Siang], Kuo, Y.H.[Yi-Hsuan], Tsai, H.P.[Hui Ping], Lin, L.M.[Liang-Mao],
Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Zhou, X., Zheng, H.B., Xu, X.Q., He, J.Y., Ge, X.K., Yao, X., Cheng, T., Zhu, Y., Cao, W.X., Tian, Y.C.,
Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery,
PandRS(130), No. 1, 2017, pp. 246-255.
Elsevier DOI 1708
UAVs BibRef

Granell, C.[Carlos], Miralles, I.[Ignacio], Rodríguez-Pupo, L.E.[Luis E.], González-Pérez, A.[Alberto], Casteleyn, S.[Sven], Busetto, L.[Lorenzo], Pepe, M.[Monica], Boschetti, M.[Mirco], Huerta, J.[Joaquín],
Conceptual Architecture and Service-Oriented Implementation of a Regional Geoportal for Rice Monitoring,
IJGI(6), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Liu, L., Shao, Y., Pinel, N., Li, K., Yang, Z., Gong, H., Wang, Y.,
Modeling Microwave Backscattering From Parabolic Rice Leaves,
GeoRS(55), No. 11, November 2017, pp. 6044-6053.
IEEE DOI 1711
Computational modeling, Mathematical model, Microwave FET integrated circuits, Spaceborne radar, Discrete dipole approximation (DDA), BibRef

He, Z.[Ze], Li, S.H.[Shi-Hua], Wang, Y.[Yong], Dai, L.Y.[Lei-Yu], Lin, S.[Sen],
Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Ranghetti, L.[Luigi], Cardarelli, E.[Elisa], Boschetti, M.[Mirco], Busetto, L.[Lorenzo], Fasola, M.[Mauro],
Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Park, S.[Seonyoung], Im, J.[Jungho], Park, S.[Seohui], Yoo, C.[Cheolhee], Han, H.S.[Hyang-Sun], Rhee, J.Y.[Jin-Young],
Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Setiyono, T.D.[Tri D.], Quicho, E.D.[Emma D.], Gatti, L.[Luca], Campos-Taberner, M.[Manuel], Busetto, L.[Lorenzo], Collivignarelli, F.[Francesco], García-Haro, F.J.[Francisco Javier], Boschetti, M.[Mirco], Khan, N.I.[Nasreen Islam], Holecz, F.[Francesco],
Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Zampieri, M.[Matteo], Garcia, G.C.[Gema Carmona], Dentener, F.[Frank], Gumma, M.K.[Murali Krishna], Salamon, P.[Peter], Seguini, L.[Lorenzo], Toreti, A.[Andrea],
Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Xu, X.J.[Xin-Jie], Ji, X.S.[Xu-Sheng], Jiang, J.[Jiale], Yao, X.[Xia], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Cao, Q.A.[Qi-Ang], Yang, H.J.[Hong-Jian], Shi, Z.K.[Zhong-Kui], Cheng, T.[Tao],
Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Campos-Taberner, M.[Manuel], García-Haro, F.J.[Francisco Javier], Busetto, L.[Lorenzo], Ranghetti, L.[Luigi], Martínez, B.[Beatriz], Gilabert, M.A.[María Amparo], Camps-Valls, G.[Gustau], Camacho, F.[Fernando], Boschetti, M.[Mirco],
A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Liu, M.X.[Meng-Xue], Liu, X.N.[Xiang-Nan], Wu, L.[Ling], Zou, X.Y.[Xin-Yu], Jiang, T.[Tian], Zhao, B.Y.[Bing-Yu],
A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Sianturi, R.[Riswan], Jetten, V.G.[Victor G.], Ettema, J.[Janneke], Sartohadi, J.[Junun],
Distinguishing between Hazardous Flooding and Non-Hazardous Agronomic Inundation in Irrigated Rice Fields: A Case Study from West Java,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Zhang, X.[Xin], Wu, B.F.[Bing-Fang], Ponce-Campos, G.E.[Guillermo E.], Zhang, M.[Miao], Chang, S.[Sheng], Tian, F.[Fuyou],
Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
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PandRS(144), 2018, pp. 469-482.
Elsevier DOI 1809
Passive microwave, Whittaker smoother (WS), MODIS data, Surface soil moisture (SSM), Paddy rice mapping BibRef

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Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France,
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Jeong, S.[Seungtaek], Ko, J.[Jonghan], Yeom, J.M.[Jong-Min],
Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea,
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DOI Link 1811
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Lee, S.K.[Seung-Kuk], Yoon, S.Y.[Sun Yong], Won, J.S.[Joong-Sun],
Vegetation Height Estimate in Rice Fields Using Single Polarization TanDEM-X Science Phase Data,
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DOI Link 1812
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Zhang, M.[Meng], Lin, H.[Hui], Wang, G.X.[Guang-Xing], Sun, H.[Hua], Fu, J.[Jing],
Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China,
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Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data,
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Agronomic and Economic Potential of Vegetation Indices for Rice Recommendations under Organic and Mineral Fertilization in Mediterranean Regions,
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Jiang, M.[Min], Xin, L.J.[Liang-Jie], Li, X.B.[Xiu-Bin], Tan, M.H.[Ming-Hong], Wang, R.J.[Ren-Jing],
Decreasing Rice Cropping Intensity in Southern China from 1990 to 2015,
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Wang, L.[Li], Chang, Q.[Qingrui], Li, F.[Fenling], Yan, L.[Lin], Huang, Y.[Yong], Wang, Q.[Qi], Luo, L.[Lili],
Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models,
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Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models,
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Shiu, Y.S.[Yi-Shiang], Chuang, Y.C.[Yung-Chung],
Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models,
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Xu, X.Q., Lu, J.S., Zhang, N., Yang, T.C., He, J.Y., Yao, X., Cheng, T., Zhu, Y., Cao, W.X., Tian, Y.C.,
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PandRS(150), 2019, pp. 185-196.
Elsevier DOI 1903
UAV-multispectral image, Rice, Radiative transfer model, Bayesian network, Leaf area index, Canopy chlorophyll content BibRef

Zhang, K.[Ke], Ge, X.K.[Xiao-Kang], Shen, P.C.[Peng-Cheng], Li, W.Y.[Wan-Yu], Liu, X.J.[Xiao-Jun], Cao, Q.A.[Qi-Ang], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Tian, Y.C.[Yong-Chao],
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Kawamura, K.[Kensuke], Tsujimoto, Y.[Yasuhiro], Nishigaki, T.[Tomohiro], Andriamananjara, A.[Andry], Rabenarivo, M.[Michel], Asai, H.[Hidetoshi], Rakotoson, T.[Tovohery], Razafimbelo, T.[Tantely],
Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar,
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Wu, J.T.[Jin-Tao], Yang, G.J.[Gui-Jun], Yang, X.D.[Xiao-Dong], Xu, B.[Bo], Han, L.[Liang], Zhu, Y.H.[Yao-Hui],
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Bazzi, H.[Hassan], Baghdadi, N.[Nicolas], El Hajj, M.[Mohammad], Zribi, M.[Mehrez], Minh, D.H.T.[Dinh Ho Tong], Ndikumana, E.[Emile], Courault, D.[Dominique], Belhouchette, H.[Hatem],
Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France,
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Jiang, Q.[Qi], Fang, S.H.[Sheng-Hui], Peng, Y.[Yi], Gong, Y.[Yan], Zhu, R.S.[Ren-Shan], Wu, X.T.[Xian-Ting], Ma, Y.[Yi], Duan, B.[Bo], Liu, J.[Jian],
UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features,
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Shew, A.M.[Aaron M.], Ghosh, A.[Aniruddha],
Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive,
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Minh, H.V.T.[Huynh Vuong Thu], Avtar, R.[Ram], Mohan, G.[Geetha], Misra, P.[Prakhar], Kurasaki, M.[Masaaki],
Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province,
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Wang, Y.Y.[Yan-Yu], Zhang, K.[Ke], Tang, C.L.[Chun-Lan], Cao, Q.A.[Qi-Ang], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Liu, X.J.[Xiao-Jun],
Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles,
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Asilo, S.[Sonia], Nelson, A.[Andrew], de Bie, K.[Kees], Skidmore, A.[Andrew], Laborte, A.[Alice], Maunahan, A.[Aileen], Quilang, E.J.P.[Eduardo Jimmy P.],
Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases,
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Zhang, J.[Jing], Zhang, Z.[Zhao], Wang, C.Z.[Chen-Zhi], Tao, F.[Fulu],
Double-Rice System Simulation in a Topographically Diverse Region: A Remote-Sensing-Driven Case Study in Hunan Province of China,
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Guo, Y.Q.[Yi-Qing], Jia, X.P.[Xiu-Ping], Paull, D.[David], Benediktsson, J.A.[Jón Atli],
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Elsevier DOI 1908
Rice mapping, Rice variety, Sowing method, Data fusion, Opinion pool, Consensus theory, Optical, Synthetic-Aperture Radar (SAR) BibRef

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Yin, Q.[Qi], Liu, M.L.[Mao-Lin], Cheng, J.[Junyi], Ke, Y.H.[Ying-Hai], Chen, X.W.[Xiu-Wan],
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Li, S.Y.[Song-Yang], Yuan, F.[Fei], Ata-UI-Karim, S.T.[Syed Tahir], Zheng, H.B.[Heng-Biao], Cheng, T.[Tao], Liu, X.J.[Xiao-Jun], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Cao, Q.A.[Qi-Ang],
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He, J.Y.[Jiao-Yang], Zhang, N.[Ni], Su, X.[Xi], Lu, J.S.[Jing-Shan], Yao, X.[Xia], Cheng, T.[Tao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Tian, Y.C.[Yong-Chao],
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Phan, A.[Anh], Ha, D.N.[Duong N.], Man, C.D.[Chuc D.], Nguyen, T.T.[Thuy T.], Bui, H.Q.[Hung Q.], Nguyen, T.T.N.[Thanh T. N.],
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Wang, J.J.[Jian-Jun], Dai, Q.X.[Qi-Xing], Shang, J.L.[Jia-Li], Jin, X.L.[Xiu-Liang], Sun, Q.[Quan], Zhou, G.S.[Gui-Sheng], Dai, Q.G.[Qi-Gen],
Field-Scale Rice Yield Estimation Using Sentinel-1A Synthetic Aperture Radar (SAR) Data in Coastal Saline Region of Jiangsu Province, China,
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Repaid Identification and Prediction of Cadmium-Lead Cross-Stress of Different Stress Levels in Rice Canopy Based on Visible and Near-Infrared Spectroscopy,
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Ding, M.J.[Ming-Jun], Guan, Q.H.[Qi-Hui], Li, L.[Lanhui], Zhang, H.M.[Hua-Min], Liu, C.[Chong], Zhang, L.[Le],
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Wang, H.F.[Hong-Fei], Ghosh, A.[Aniruddha], Linquist, B.A.[Bruce A.], Hijmans, R.J.[Robert J.],
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Zhang, W.C.[Wei-Chun], Liu, H.B.[Hong-Bin], Wu, W.[Wei], Zhan, L.Q.[Lin-Qing], Wei, J.[Jing],
Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability,
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Inoue, S.P.[Shim-Pei], Ito, A.[Akihiko], Yonezawa, C.[Chinatsu],
Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine,
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Jiang, R.[Rui], Wang, P.[Pei], Xu, Y.[Yan], Zhou, Z.[Zhiyan], Luo, X.[Xiwen], Lan, Y.[Yubin], Zhao, G.[Genping], Sanchez-Azofeifa, A.[Arturo], Laakso, K.[Kati],
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Raksapatcharawong, M.[Mongkol], Veerakachen, W.[Watcharee], Homma, K.[Koki], Maki, M.[Masayasu], Oki, K.[Kazuo],
Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW-RS,
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Alebele, Y.[Yeshanbele], Zhang, X.[Xue], Wang, W.H.[Wen-Hui], Yang, G.X.[Gao-Xiang], Yao, X.[Xia], Zheng, H.B.[Heng-Biao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Cheng, T.[Tao],
Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking,
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Karydas, C.[Christos], Iatrou, M.[Miltiadis], Iatrou, G.[George], Mourelatos, S.[Spiros],
Management Zone Delineation for Site-Specific Fertilization in Rice Crop Using Multi-Temporal RapidEye Imagery,
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Ryu, J.H.[Jae-Hyun], Jeong, H.[Hoejeong], Cho, J.[Jaeil],
Performances of Vegetation Indices on Paddy Rice at Elevated Air Temperature, Heat Stress, and Herbicide Damage,
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Crisóstomo de Castro Filho, H.[Hugo], de Carvalho Júnior, O.A.[Osmar Abílio], de Carvalho, O.L.F.[Osmar Luiz Ferreira], Pozzobon de Bem, P.[Pablo], dos Santos de Moura, R.[Rebeca], Olino de Albuquerque, A.[Anesmar], Silva, C.R.[Cristiano Rosa], Ferreira, P.H.G.[Pedro Henrique Guimarães], Guimarães, R.F.[Renato Fontes], Gomes, R.A.T.[Roberto Arnaldo Trancoso],
Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series,
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Jimenez-Sierra, D.A.[David Alejandro], Benítez-Restrepo, H.D.[Hernán Darío], Vargas-Cardona, H.D.[Hernán Darío], Chanussot, J.[Jocelyn],
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Du, Y.M.[Yong-Ming], Cao, B.[Biao], Li, H.[Hua], Bian, Z.J.[Zun-Jian], Qin, B.X.[Bo-Xiong], Xiao, Q.[Qing], Liu, Q.H.[Qin-Huo], Zeng, Y.J.[Yi-Jian], Su, Z.B.[Zhong-Bo],
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Chen, N.C.[Neng-Cheng], Yu, L.X.N.[Li-Xiao-Na], Zhang, X.[Xiang], Shen, Y.L.[Yong-Lin], Zeng, L.L.[Ling-Lin], Hu, Q.[Qiong], Niyogi, D.[Dev],
Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform,
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O'Shea, K.[Kristen], LaRoe, J.[Jillian], Vorster, A.[Anthony], Young, N.[Nicholas], Evangelista, P.[Paul], Mayer, T.[Timothy], Carver, D.[Daniel], Simonson, E.[Eli], Martin, V.[Vanesa], Radomski, P.[Paul], Knopik, J.[Joshua], Kern, A.[Anthony], Khoury, C.K.[Colin K.],
Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.),
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Hoang-Phi, P.[Phung], Lam-Dao, N.[Nguyen], Pham-Van, C.[Cu], Chau-Nguyen-Xuan, Q.[Quang], Nguyen-Van-Anh, V.[Vu], Gummadi, S.[Sridhar], Le-Van, T.[Trung],
Sentinel-1 SAR Time Series-Based Assessment of the Impact of Severe Salinity Intrusion Events on Spatiotemporal Changes in Distribution of Rice Planting Areas in Coastal Provinces of the Mekong Delta, Vietnam,
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Qiu, Z.C.[Zheng-Chao], Xiang, H.T.[Hai-Tao], Ma, F.[Fei], Du, C.W.[Chang-Wen],
Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery,
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Liu, S.S.[Shi-Shi], Chen, Y.R.[Yu-Ren], Ma, Y.T.[Yin-Tao], Kong, X.X.[Xiao-Xuan], Zhang, X.Y.[Xin-Yu], Zhang, D.Y.[Dong-Ying],
Mapping Ratoon Rice Planting Area in Central China Using Sentinel-2 Time Stacks and the Phenology-Based Algorithm,
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Fiorillo, E.[Edoardo], di Giuseppe, E.[Edmondo], Fontanelli, G.[Giacomo], Maselli, F.[Fabio],
Lowland Rice Mapping in Sédhiou Region (Senegal) Using Sentinel 1 and Sentinel 2 Data and Random Forest,
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Dey, S.[Subhadip], Bhattacharya, A.[Avik], Ratha, D.[Debanshu], Mandal, D.[Dipankar], McNairn, H.[Heather], Lopez-Sanchez, J.M.[Juan M.], Rao, Y.S.,
Novel clustering schemes for full and compact polarimetric SAR data: An application for rice phenology characterization,
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Elsevier DOI 2011
Unsupervised clustering, Entropy, RADARSAT-2, Crop monitoring, PolSAR, Roll-invariant parameter BibRef

Jo, H.W., Lee, S., Park, E., Lim, C.H., Song, C., Lee, H., Ko, Y., Cha, S., Yoon, H., Lee, W.K.,
Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea,
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IEEE DOI 2011
Machine learning, Image resolution, Remote sensing, Satellites, Feature extraction, Synthetic aperture radar, semisupervised classification BibRef

Ramadhani, F.[Fadhlullah], Pullanagari, R.[Reddy], Kereszturi, G.[Gabor], Procter, J.[Jonathan],
Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1,
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Arjasakusuma, S.[Sanjiwana], Kusuma, S.S.[Sandiaga Swahyu], Rafif, R.[Raihan], Saringatin, S.[Siti], Wicaksono, P.[Pramaditya],
Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia,
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Li, H.[He], Fu, D.J.[Dong-Jie], Huang, C.[Chong], Su, F.Z.[Fen-Zhen], Liu, Q.S.[Qing-Sheng], Liu, G.H.[Gao-Huan], Wu, S.R.[Shang-Rong],
An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand,
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Zhou, D.B.[Dong-Bo], Liu, S.J.[Shuang-Jian], Yu, J.[Jie], Li, H.[Hao],
A High-Resolution Spatial and Time-Series Labeled Unmanned Aerial Vehicle Image Dataset for Middle-Season Rice,
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Dataset, Rice. BibRef

Yamaguchi, T.[Tomoaki], Tanaka, Y.[Yukie], Imachi, Y.[Yuto], Yamashita, M.[Megumi], Katsura, K.[Keisuke],
Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice,
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DOI Link 2101
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Ma, Y.[Yi], Jiang, Q.[Qi], Wu, X.T.[Xian-Ting], Zhu, R.S.[Ren-Shan], Gong, Y.[Yan], Peng, Y.[Yi], Duan, B.[Bo], Fang, S.H.[Sheng-Hui],
Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data,
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Kim, H.[Hyewon], Kim, W.[Woojung], Kim, S.D.[Sang Don],
Damage Assessment of Rice Crop after Toluene Exposure Based on the Vegetation Index (VI) and UAV Multispectral Imagery,
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Wei, P.L.[Peng-Liang], Chai, D.F.[Deng-Feng], Lin, T.[Tao], Tang, C.[Chao], Du, M.Q.[Mei-Qi], Huang, J.F.[Jing-Feng],
Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model,
PandRS(174), 2021, pp. 198-214.
Elsevier DOI 2103
Sentinel-1 images, Multi-temporal, Rice mapping, Large-scale, Deep semantic segmentation BibRef

Lin, Z.X.[Zhi-Xian], Zhong, R.H.[Ren-Hai], Xiong, X.G.[Xing-Guo], Guo, C.Q.[Chang-Qiang], Xu, J.F.[Jin-Fan], Zhu, Y.[Yue], Xu, J.[Jialu], Ying, Y.B.[Yi-Bin], Ting, K.C., Huang, J.F.[Jing-Feng], Lin, T.[Tao],
Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series,
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Jiang, X.Q.[Xue-Qin], Fang, S.H.[Sheng-Hui], Huang, X.[Xia], Liu, Y.H.[Yang-Hua], Guo, L.L.[Lin-Lin],
Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands,
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DOI Link 2103
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Yu, Y.[Yue], Bao, Y.[Yidan], Wang, J.C.[Ji-Chun], Chu, H.J.[Hang-Jian], Zhao, N.[Nan], He, Y.[Yong], Liu, Y.F.[Yu-Fei],
Crop Row Segmentation and Detection in Paddy Fields Based on Treble-Classification Otsu and Double-Dimensional Clustering Method,
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DOI Link 2103
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Phan, H.[Hoa], Toan, T.L.[Thuy Le], Bouvet, A.[Alexandre],
Understanding Dense Time Series of Sentinel-1 Backscatter from Rice Fields: Case Study in a Province of the Mekong Delta, Vietnam,
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Xiao, W.[Wu], Xu, S.[Suchen], He, T.T.[Ting-Ting],
Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm: A Implementation in Hangjiahu Plain in China Using GEE Platform,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
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Yang, L.B.[Ling-Bo], Wang, L.M.[Li-Min], Abubakar, G.A.[Ghali Abdullahi], Huang, J.F.[Jing-Feng],
High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
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Yang, M.D.[Ming-Der], Tseng, H.H.[Hsin-Hung], Hsu, Y.C.[Yu-Chun], Yang, C.Y.[Chin-Ying], Lai, M.H.[Ming-Hsin], Wu, D.H.[Dong-Hong],
A UAV Open Dataset of Rice Paddies for Deep Learning Practice,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
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Zhu, A.X.[A-Xing], Zhao, F.H.[Fang-He], Pan, H.B.[Hao-Bo], Liu, J.Z.[Jun-Zhi],
Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
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Fernandez-Beltran, R.[Ruben], Baidar, T.[Tina], Kang, J.[Jian], Pla, F.[Filiberto],
Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
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Ramadhani, F.[Fadhlullah], Pullanagari, R.[Reddy], Kereszturi, G.[Gabor], Procter, J.[Jonathan],
Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
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Kang, Y.S.[Ye-Seong], Nam, J.[Jinwoo], Kim, Y.G.[Young-Gwang], Lee, S.T.[Seong-Tae], Seong, D.[Deokgyeong], Jang, S.Y.[Sih-Yeong], Ryu, C.[Chanseok],
Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
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Sitokonstantinou, V.[Vasileios], Koukos, A.[Alkiviadis], Drivas, T.[Thanassis], Kontoes, C.[Charalampos], Papoutsis, I.[Ioannis], Karathanassi, V.[Vassilia],
A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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Wang, L.[Li], Chen, S.[Shuisen], Peng, Z.P.[Zhi-Ping], Huang, J.C.[Ji-Chuan], Wang, C.Y.[Chong-Yang], Jiang, H.[Hao], Zheng, Q.[Qiong], Li, D.[Dan],
Phenology Effects on Physically Based Estimation of Paddy Rice Canopy Traits from UAV Hyperspectral Imagery,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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Elsherbiny, O.[Osama], Zhou, L.[Lei], Feng, L.[Lei], Qiu, Z.J.[Zheng-Jun],
Integration of Visible and Thermal Imagery with an Artificial Neural Network Approach for Robust Forecasting of Canopy Water Content in Rice,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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Jin, H.X.[Hong-Xiao], Köppl, C.J.[Christian Josef], Fischer, B.M.C.[Benjamin M. C.], Rojas-Conejo, J.[Johanna], Johnson, M.S.[Mark S.], Morillas, L.[Laura], Lyon, S.W.[Steve W.], Durán-Quesada, A.M.[Ana M.], Suárez-Serrano, A.[Andrea], Manzoni, S.[Stefano], Garcia, M.[Monica],
Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
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Iwahashi, Y.[Yu], Ye, R.[Rongling], Kobayashi, S.[Satoru], Yagura, K.[Kenjiro], Hor, S.[Sanara], Soben, K.[Kim], Homma, K.[Koki],
Quantification of Changes in Rice Production for 2003-2019 with MODIS LAI Data in Pursat Province, Cambodia,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
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Yuan, N.G.[Ning-Ge], Gong, Y.[Yan], Fang, S.H.[Sheng-Hui], Liu, Y.T.[Ya-Ting], Duan, B.[Bo], Yang, K.[Kaili], Wu, X.T.[Xian-Ting], Zhu, R.S.[Ren-Shan],
UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Arumugam, P.[Ponraj], Chemura, A.[Abel], Schauberger, B.[Bernhard], Gornott, C.[Christoph],
Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
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Peprah, C.O.[Clement Oppong], Yamashita, M.[Megumi], Yamaguchi, T.[Tomoaki], Sekino, R.[Ryo], Takano, K.[Kyohei], Katsura, K.[Keisuke],
Spatio-Temporal Estimation of Biomass Growth in Rice Using Canopy Surface Model from Unmanned Aerial Vehicle Images,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
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Ge, H.X.[Hai-Xiao], Ma, F.[Fei], Li, Z.W.[Zhen-Wang], Tan, Z.Z.[Zheng-Zheng], Du, C.W.[Chang-Wen],
Improved Accuracy of Phenological Detection in Rice Breeding by Using Ensemble Models of Machine Learning Based on UAV-RGB Imagery,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Ni, R.G.[Rong-Guang], Tian, J.Y.[Jin-Yan], Li, X.J.[Xiao-Juan], Yin, D.[Dameng], Li, J.[Jiwei], Gong, H.[Huili], Zhang, J.[Jie], Zhu, L.[Lin], Wu, D.L.[Dong-Li],
An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine,
PandRS(178), 2021, pp. 282-296.
Elsevier DOI 2108
Paddy rice mapping, Phenology, Time-series analysis, Pixel-based, One-class classifier BibRef

Jiang, R.[Rui], Sanchez-Azofeifa, A.[Arturo], Laakso, K.[Kati], Xu, Y.[Yan], Zhou, Z.[Zhiyan], Luo, X.[Xiwen], Huang, J.H.[Jun-Hao], Chen, X.[Xin], Zang, Y.[Yu],
Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Yang, K.[Kaili], Gong, Y.[Yan], Fang, S.H.[Sheng-Hui], Duan, B.[Bo], Yuan, N.G.[Ning-Ge], Peng, Y.[Yi], Wu, X.T.[Xian-Ting], Zhu, R.S.[Ren-Shan],
Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

An, G.Q.[Gang-Qiang], Xing, M.F.[Min-Feng], He, B.B.[Bin-Bin], Kang, H.Q.[Hai-Qi], Shang, J.L.[Jia-Li], Liao, C.H.[Chun-Hua], Huang, X.D.[Xiao-Dong], Zhang, H.G.[Hong-Guo],
Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Feng, S.[Shuai], Cao, Y.L.[Ying-Li], Xu, T.Y.[Tong-Yu], Yu, F.H.[Feng-Hua], Zhao, D.X.[Dong-Xue], Zhang, G.S.[Guo-Sheng],
Rice Leaf Blast Classification Method Based on Fused Features and One-Dimensional Deep Convolutional Neural Network,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
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Lu, J.S.[Jing-Shan], Eitel, J.U.H.[Jan U. H.], Jennewein, J.S.[Jyoti S.], Zhu, J.[Jie], Zheng, H.[Hengbiao], Yao, X.[Xia], Cheng, T.[Tao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Tian, Y.C.[Yong-Chao],
Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Wang, F.M.[Fu-Min], Yao, X.P.[Xiao-Ping], Xie, L.[Lili], Zheng, J.Y.[Jue-Yi], Xu, T.Y.[Tian-Yue],
Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
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Xu, T.Y.[Tian-Yue], Wang, F.M.[Fu-Min], Shi, Z.[Zhou], Xie, L.[Lili], Yao, X.P.[Xiao-Ping],
Dynamic estimation of rice aboveground biomass based on spectral and spatial information extracted from hyperspectral remote sensing images at different combinations of growth stages,
PandRS(202), 2023, pp. 169-183.
Elsevier DOI 2308
Data fusion, Optical, Vegetative growth stages, Gray level co-occurrence matrix, Time series BibRef

Liu, S.Z.[Shen-Zhou], Zeng, W.Z.[Wen-Zhi], Wu, L.F.[Li-Feng], Lei, G.Q.[Guo-Qing], Chen, H.R.[Hao-Rui], Gaiser, T.[Thomas], Srivastava, A.K.[Amit Kumar],
Simulating the Leaf Area Index of Rice from Multispectral Images,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
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Li, D.C.[Dai-Chao], Liang, J.Q.[Jian-Qin], Wang, X.F.[Xing-Feng], Wu, S.[Sheng], Xie, X.W.[Xiao-Wei], Lu, J.Q.[Jia-Qi],
Rice Yield Simulation and Planting Suitability Environment Pattern Recognition at a Fine Scale,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
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Xu, L.[Lu], Zhang, H.[Hong], Wang, C.[Chao], Wei, S.[Sisi], Zhang, B.[Bo], Wu, F.[Fan], Tang, Y.X.[Yi-Xian],
Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Xue, W.[Wei], Jeong, S.[Seungtaek], Ko, J.[Jonghan], Yeom, J.M.[Jong-Min],
Contribution of Biophysical Factors to Regional Variations of Evapotranspiration and Seasonal Cooling Effects in Paddy Rice in South Korea,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Franch, B.[Belen], Bautista, A.S.[Alberto San], Fita, D.[David], Rubio, C.[Constanza], Tarrazó-Serrano, D.[Daniel], Sánchez, A.[Antonio], Skakun, S.[Sergii], Vermote, E.[Eric], Becker-Reshef, I.[Inbal], Uris, A.[Antonio],
Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
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Zhao, R.K.[Rong-Kun], Li, Y.C.[Yue-Chen], Chen, J.[Jin], Ma, M.G.[Ming-Guo], Fan, L.[Lei], Lu, W.[Wei],
Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Pan, B.[Baihong], Zheng, Y.[Yi], Shen, R.[Ruoque], Ye, T.[Tao], Zhao, W.Z.[Wen-Zhi], Dong, J.[Jie], Ma, H.Q.[Han-Qing], Yuan, W.P.[Wen-Ping],
High Resolution Distribution Dataset of Double-Season Paddy Rice in China,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Wei, H.D.[Hao-Dong], Hu, Q.[Qiong], Cai, Z.W.[Zhi-Wen], Yang, J.Y.[Jing-Ya], Song, Q.[Qian], Yin, G.F.[Gao-Fei], Xu, B.D.[Bao-Dong],
An Object- and Topology-Based Analysis (OTBA) Method for Mapping Rice-Crayfish Fields in South China,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Guo, X.Y.[Xian-Yu], Yin, J.J.[Jun-Jun], Li, K.[Kun], Yang, J.[Jian],
Fine Classification of Rice Paddy Based on RHSI-DT Method Using Multi-Temporal Compact Polarimetric SAR Data,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
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Wei, L.[Lele], Luo, Y.S.[Yu-Sen], Xu, L.Z.[Li-Zhang], Zhang, Q.[Qian], Cai, Q.B.[Qi-Bing], Shen, M.J.[Ming-Jun],
Deep Convolutional Neural Network for Rice Density Prescription Map at Ripening Stage Using Unmanned Aerial Vehicle-Based Remotely Sensed Images,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Lu, W.Y.[Wen-Yi], Okayama, T.[Tsuyoshi], Komatsuzaki, M.[Masakazu],
Rice Height Monitoring between Different Estimation Models Using UAV Photogrammetry and Multispectral Technology,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Zhang, T.[Tao], Jiang, X.D.[Xiao-Dong], Jiang, L.L.[Lin-Lin], Li, X.[Xuran], Yang, S.B.[Shen-Bin], Li, Y.X.[Ying-Xue],
Hyperspectral Reflectance Characteristics of Rice Canopies under Changes in Diffuse Radiation Fraction,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
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Wei, P.L.[Peng-Liang], Huang, R.[Ran], Lin, T.[Tao], Huang, J.F.[Jing-Feng],
Rice Mapping in Training Sample Shortage Regions Using a Deep Semantic Segmentation Model Trained on Pseudo-Labels,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
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Anuar, M.M.[Mohamed Marzhar], Halin, A.A.[Alfian Abdul], Perumal, T.[Thinagaran], Kalantar, B.[Bahareh],
Aerial Imagery Paddy Seedlings Inspection Using Deep Learning,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
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Wei, J.[Jun], Cui, Y.[Yuanlai], Luo, W.Q.[Wan-Qi], Luo, Y.F.[Yu-Feng],
Mapping Paddy Rice Distribution and Cropping Intensity in China from 2014 to 2019 with Landsat Images, Effective Flood Signals, and Google Earth Engine,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Yang, H.J.[Hui-Jin], Li, H.P.[He-Ping], Wang, W.[Wei], Li, N.[Ning], Zhao, J.H.[Jian-Hui], Pan, B.[Bin],
Spatio-Temporal Estimation of Rice Height Using Time Series Sentinel-1 Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Chen, F.N.[Feng-Nong], Zhang, Y.[Yao], Zhang, J.C.[Jing-Cheng], Liu, L.M.[Lian-Meng], Wu, K.H.[Kai-Hua],
Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Qiu, Z.C.[Zheng-Chao], Ma, F.[Fei], Li, Z.W.[Zhen-Wang], Xu, X.B.[Xue-Bin], Du, C.W.[Chang-Wen],
Development of Prediction Models for Estimating Key Rice Growth Variables Using Visible and NIR Images from Unmanned Aerial Systems,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Guo, X.Y.[Xian-Yu], Yin, J.J.[Jun-Jun], Li, K.[Kun], Yang, J.[Jian], Shao, Y.[Yun],
Scattering Intensity Analysis and Classification of Two Types of Rice Based on Multi-Temporal and Multi-Mode Simulated Compact Polarimetric SAR Data,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Fatchur-Rachman, Rudiyanto, Soh, N.C.[Norhidayah Che], Shah, R.M.[Ramisah Mohd], Giap, S.G.E.[Sunny Goh Eng], Setiawan, B.I.[Budi Indra], Minasny, B.[Budiman],
High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Chang, Y.L.[Yang-Lang], Tan, T.H.[Tan-Hsu], Chen, T.H.[Tsung-Hau], Chuah, J.H.[Joon Huang], Chang, L.[Lena], Wu, M.C.[Meng-Che], Tatini, N.B.[Narendra Babu], Ma, S.C.[Shang-Chih], Alkhaleefah, M.[Mohammad],
Spatial-Temporal Neural Network for Rice Field Classification from SAR Images,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
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Uehara, H.[Hiroshi], Iuchi, Y.[Yasuhiro], Fukazawa, Y.[Yusuke], Kaneta, Y.[Yoshihiro],
Predicting A Growing Stage of Rice Plants Based on The Cropping Records over 25 Years: A Trial of Feature Engineering Incorporating Hidden Regional Characteristics,
IEICE(E105-D), No. 5, May 2022, pp. 955-963.
WWW Link. 2205
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Kang, J.M.[Jun-Mei], Yang, X.M.[Xiao-Mei], Wang, Z.H.[Zhi-Hua], Huang, C.[Chong], Wang, J.[Jun],
Collaborative Extraction of Paddy Planting Areas with Multi-Source Information Based on Google Earth Engine: A Case Study of Cambodia,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Xu, T.Y.[Tian-Yue], Wang, F.M.[Fu-Min], Xie, L.[Lili], Yao, X.P.[Xiao-Ping], Zheng, J.[Jueyi], Li, J.[Jiale], Chen, S.[Siting],
Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Ji, S.[Shu], Gu, C.[Chen], Xi, X.B.[Xiao-Bo], Zhang, Z.H.[Zheng-Hua], Hong, Q.Q.[Qing-Qing], Huo, Z.Y.[Zhong-Yang], Zhao, H.T.[Hai-Tao], Zhang, R.H.[Rui-Hong], Li, B.[Bin], Tan, C.W.[Chang-Wei],
Quantitative Monitoring of Leaf Area Index in Rice Based on Hyperspectral Feature Bands and Ridge Regression Algorithm,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Tseng, H.H.[Hsin-Hung], Yang, M.D.[Ming-Der], Saminathan, R., Hsu, Y.C.[Yu-Chun], Yang, C.Y.[Chin-Ying], Wu, D.H.[Dong-Hong],
Rice Seedling Detection in UAV Images Using Transfer Learning and Machine Learning,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Liu, R.[Ruoqi], Zhang, G.[Geli], Dong, J.W.[Jin-Wei], Zhou, Y.[Yan], You, N.[Nanshan], He, Y.L.[Ying-Li], Xiao, X.M.[Xiang-Ming],
Evaluating Effects of Medium-Resolution Optical Data Availability on Phenology-Based Rice Mapping in China,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Sun, C.L.[Chun-Ling], Zhang, H.[Hong], Ge, J.[Ji], Wang, C.[Chao], Li, L.[Liutong], Xu, L.[Lu],
Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Kobayashi, S.[Shoko], Ide, H.[Hiyuto],
Rice Crop Monitoring Using Sentinel-1 SAR Data: A Case Study in Saku, Japan,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, H.G.[Hong-Guo], He, B.B.[Bin-Bin], Xing, J.[Jin],
Mapping Paddy Rice in Complex Landscapes with Landsat Time Series Data and Superpixel-Based Deep Learning Method,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Xu, T.Y.[Tian-Yue], Wang, F.M.[Fu-Min], Yi, Q.X.[Qiu-Xiang], Xie, L.[Lili], Yao, X.P.[Xiao-Ping],
A Bibliometric and Visualized Analysis of Research Progress and Trends in Rice Remote Sensing over the Past 42 Years (1980-2021),
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Carrasco, L.[Luis], Fujita, G.[Go], Kito, K.[Kensuke], Miyashita, T.[Tadashi],
Historical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine,
PandRS(191), 2022, pp. 277-289.
Elsevier DOI 2208
Crop mapping, Gap-filling, Phenology-based, Pixel-based, Rice paddy, Rice transplanting, Temporal reduction BibRef

Zhang, J.[Jie], Song, X.Y.[Xiao-Yu], Jing, X.[Xia], Yang, G.J.[Gui-Jun], Yang, C.H.[Cheng-Hai], Feng, H.K.[Hai-Kuan], Wang, J.J.[Jiao-Jiao], Ming, S.K.[Shi-Kang],
Remote Sensing Monitoring of Rice Grain Protein Content Based on a Multidimensional Euclidean Distance Method,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, J.[Jing], Wu, H.Q.[Hua-Qing], Zhang, Z.[Zhao], Zhang, L.L.[Liang-Liang], Luo, Y.C.[Yu-Chuan], Han, J.C.[Ji-Chong], Tao, F.[Fulu],
Asian Rice Calendar Dynamics Detected by Remote Sensing and Their Climate Drivers,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Ma, X.S.[Xiao-Shuang], Huang, Z.[Zunyi], Zhu, S.Y.[Sheng-Yuan], Fang, W.[Wei], Wu, Y.L.[Ying-Lei],
Rice Planting Area Identification Based on Multi-Temporal Sentinel-1 SAR Images and an Attention U-Net Model,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Liu, Y.Y.[Yuan-Yuan], Wang, S.Q.[Shao-Qiang], Chen, J.H.[Jing-Hua], Chen, B.[Bin], Wang, X.B.[Xiao-Bo], Hao, D.Z.[Dong-Ze], Sun, L.[Leigang],
Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Zhang, K.[Kaili], Chen, Y.G.[Yong-Gang], Zhang, B.[Bokun], Hu, J.J.[Jun-Jie], Wang, W.T.[Wen-Tao],
A Multitemporal Mountain Rice Identification and Extraction Method Based on the Optimal Feature Combination and Machine Learning,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Li, N.[Nan], Lopez-Sanchez, J.M.[Juan M.], Fu, H.Q.[Hai-Qiang], Zhu, J.J.[Jian-Jun], Han, W.T.[Wen-Tao], Xie, Q.H.[Qing-Hua], Hu, J.[Jun], Xie, Y.Z.[Yan-Zhou],
Rice Crop Height Inversion from TanDEM-X PolInSAR Data Using the RVoG Model Combined with the Logistic Growth Equation,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Sun, T.[Tao], Chen, L.[Liding], Sun, R.[Ranhao],
A Method to Estimate Clear-Sky Albedo of Paddy Rice Fields,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
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And: Correction, Add A2: Fang, H.L.[Hong-Liang], RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Cipta, I.M.[Iqbal Maulana], Jaelani, L.M.[Lalu Muhamad], Sanjaya, H.[Hartanto],
Identification of Paddy Varieties from Landsat 8 Satellite Image Data Using Spectral Unmixing Method in Indramayu Regency, Indonesia,
IJGI(11), No. 10, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Liao, S.C.[Shi-Cheng], Xu, X.[Xiong], Xie, H.[Huan], Chen, P.[Peng], Wang, C.[Chao], Jin, Y.M.[Yan-Min], Tong, X.H.[Xiao-Hua], Xiao, C.J.[Chang-Jiang],
A Modified Shape Model Incorporating Continuous Accumulated Growing Degree Days for Phenology Detection of Early Rice,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Xia, L.[Lang], Zhao, F.[Fen], Chen, J.[Jin], Yu, L.[Le], Lu, M.[Miao], Yu, Q.Y.[Qiang-Yi], Liang, S.F.[She-Fang], Fan, L.L.[Ling-Ling], Sun, X.[Xiao], Wu, S.R.[Shang-Rong], Wu, W.B.[Wen-Bin], Yang, P.[Peng],
A full resolution deep learning network for paddy rice mapping using Landsat data,
PandRS(194), 2022, pp. 91-107.
Elsevier DOI 2212
Paddy rice, Deep learning, Resolution fusion, Landsat, Training dataset BibRef

Iatrou, M.[Miltiadis], Karydas, C.[Christos], Tseni, X.[Xanthi], Mourelatos, S.[Spiros],
Representation Learning with a Variational Autoencoder for Predicting Nitrogen Requirement in Rice,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Dai, X.M.[Xue-Mei], Chen, S.S.[Shui-Sen], Jia, K.[Kai], Jiang, H.[Hao], Sun, Y.S.[Yi-Shan], Li, D.[Dan], Zheng, Q.[Qiong], Huang, J.X.[Jian-Xi],
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Ma, S.Q.[Si-Qi], Wang, D.Y.[Dan-Yang], Yang, H.C.[Hai-Chao], Hou, H.[Huagang], Li, C.[Cheng], Li, Z.[Zhaofu],
A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China,
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Ye, Z.W.[Zhi-Wei], Song, Z.L.[Zi-Lun], Li, P.F.[Peng-Fei], Wang, M.W.[Ming-Wei], Hou, W.G.[Wen-Guang],
A modified threshold score-based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition-based learning hybrid rice optimization algorithm,
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Xu, H.[Huiyao], Song, J.[Jia], Zhu, Y.Q.[Yun-Qiang],
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Kurihara, J.[Junichi], Nagata, T.[Toru], Tomiyama, H.[Hiroyuki],
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Yang, L.B.[Ling-Bo], Huang, R.[Ran], Zhang, J.C.[Jing-Cheng], Huang, J.F.[Jing-Feng], Wang, L.M.[Li-Min], Dong, J.C.[Jian-Cong], Shao, J.[Jie],
Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability,
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Mia, M.S.[Md. Suruj], Tanabe, R.[Ryoya], Habibi, L.N.[Luthfan Nur], Hashimoto, N.[Naoyuki], Homma, K.[Koki], Maki, M.[Masayasu], Matsui, T.[Tsutomu], Tanaka, T.S.T.[Takashi S. T.],
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Tian, G.X.[Gui-Xiang], Li, H.P.[He-Ping], Jiang, Q.[Qi], Qiao, B.J.[Bao-Jun], Li, N.[Ning], Guo, Z.W.[Zheng-Wei], Zhao, J.H.[Jian-Hui], Yang, H.J.[Hui-Jin],
An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images,
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Jiang, Q.[Qin], Tang, Z.G.[Zhi-Guang], Zhou, L.[Linghua], Hu, G.J.[Guo-Jie], Deng, G.[Gang], Xu, M.[Meifeng], Sang, G.Q.[Guo-Qing],
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Qin, J.[Jiale], Hu, T.[Tianci], Yuan, J.H.[Jiang-Hao], Liu, Q.Z.[Qing-Zhi], Wang, W.S.[Wen-Sheng], Liu, J.[Jie], Guo, L.F.[Lei-Feng], Song, G.Z.[Guo-Zhu],
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Leaf Spectral Analysis for Detection and Differentiation of Three Major Rice Diseases in the Philippines,
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Wang, Y.X.[Yan-Xiang], Xing, M.F.[Min-Feng], Zhang, H.G.[Hong-Guo], He, B.B.[Bin-Bin], Zhang, Y.[Yi],
Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data,
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Malvade, N.N.[Naveen N.], Yakkundimath, R.[Rajesh], Saunshi, G.B.[Girish B.], Elemmi, M.C.[Mahantesh C.],
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Fu, T.[Tingyan], Tian, S.[Shufang], Ge, J.[Jia],
R-Unet: A Deep Learning Model for Rice Extraction in Rio Grande do Sul, Brazil,
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Assessing Rice Sheath Blight Disease Habitat Suitability at a Regional Scale through Multisource Data Analysis,
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Wang, X.Q.[Xiao-Qi], Wang, Y.J.[Yao-Jun], Zhao, J.B.[Jing-Bo], Niu, J.[Jing],
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A Study on the SAR Data Observation Time For The Classification Of Planting Condition Of Paddy Fields,
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Non-destructive Monitoring Of Rice By Hyperspectral In-field Spectrometry And Uav-based Remote Sensing: Case Study Of Field-grown Rice In North Rhine-westphalia, Germany,
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Son, N.T., Chen, C.F., Chen, C.R., Chang, L.Y., Chiang, S.H.,
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Tilly, N., Hoffmeister, D., Liang, H., Cao, Q., Liu, Y., Lenz-Wiedemann, V., Miao, Y., Bareth, G.,
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Wu, L.[Ling], Liu, X.N.[Xiang-Nan], Liu, M.L.[Mei-Ling],
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Zhou, Y.F.[Ying-Feng], Wang, Y.M.[Ya-Ming], Yao, Q.[Qing],
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IASP10(575-578).
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Li, M.W.[Ming-Wei], Zhang, W.[Wei],
Research and Implement of Head Milled Rice Detection High-Speed Algorithm Based on FPGA,
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Wheat Crop Analysis, Detection, Change .


Last update:Mar 16, 2024 at 20:36:19