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

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
BibRef

Song, P.L.[Pei-Lin], Mansaray, L.R.[Lamin R.], Huang, J.F.[Jing-Feng], Huang, W.J.[Wen-Jiang],
Mapping paddy rice agriculture over China using AMSR-E time series data,
PandRS(144), 2018, pp. 469-482.
Elsevier DOI 1809
Passive microwave, Whittaker smoother (WS), MODIS data, Surface soil moisture (SSM), Paddy rice mapping BibRef

Kawamura, K.[Kensuke], Ikeura, H.[Hiroshi], Phongchanmaixay, S.[Sengthong], Khanthavong, P.[Phanthasin],
Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Ndikumana, E.[Emile], Minh, D.H.T.[Dinh Ho Tong], Nguyen, H.T.D.[Hai Thu Dang], Baghdadi, N.[Nicolas], Courault, D.[Dominique], Hossard, L.[Laure], El Moussawi, I.[Ibrahim],
Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

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,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

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,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

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,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Jiang, T.[Tian], Liu, X.N.[Xiang-Nan], Wu, L.[Ling],
Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data,
IJGI(7), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Moreno-García, B.[Beatriz], Casterad, M.A.[M. Auxiliadora], Guillén, M.[Mónica], Quílez, D.[Dolores],
Agronomic and Economic Potential of Vegetation Indices for Rice Recommendations under Organic and Mineral Fertilization in Mediterranean Regions,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

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,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Wang, L.[Li], Chang, Q.R.[Qing-Rui], Li, F.L.[Fen-Ling], 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,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Sousa, D.[Daniel], Small, C.[Christopher],
Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

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,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

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.,
Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models,
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],
Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

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,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Stavrakoudis, D.[Dimitris], Katsantonis, D.[Dimitrios], Kadoglidou, K.[Kalliopi], Kalaitzidis, A.[Argyris], Gitas, I.Z.[Ioannis Z.],
Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

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],
Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

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,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

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,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Shew, A.M.[Aaron M.], Ghosh, A.[Aniruddha],
Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

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,
IJGI(8), No. 5, 2019, pp. xx-yy.
DOI Link 1906
BibRef

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,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

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,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

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,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Guo, Y.Q.[Yi-Qing], Jia, X.P.[Xiu-Ping], Paull, D.[David], Benediktsson, J.A.[Jón Atli],
Nomination-favoured opinion pool for optical-SAR-synergistic rice mapping in face of weakened flooding signals,
PandRS(155), 2019, pp. 187-205.
Elsevier DOI 1908
Rice mapping, Rice variety, Sowing method, Data fusion, Opinion pool, Consensus theory, Optical, Synthetic-Aperture Radar (SAR) BibRef

Rudiyanto, Minasny, B.[Budiman], Shah, R.M.[Ramisah M.], Soh, N.C.[Norhidayah Che], Arif, C.[Chusnul], Setiawan, B.I.[Budi Indra],
Automated Near-Real-Time Mapping and Monitoring of Rice Extent, Cropping Patterns, and Growth Stages in Southeast Asia Using Sentinel-1 Time Series on a Google Earth Engine Platform,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Yin, Q.[Qi], Liu, M.L.[Mao-Lin], Cheng, J.[Junyi], Ke, Y.H.[Ying-Hai], Chen, X.W.[Xiu-Wan],
Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

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],
Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

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],
Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

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.],
Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Hashimoto, N.[Naoyuki], Saito, Y.[Yuki], Maki, M.[Masayasu], Homma, K.[Koki],
Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

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,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

He, Z.[Ze], Li, S.H.[Shi-Hua], Wang, Y.[Yong], Hu, Y.M.[Yue-Ming], Chen, F.X.[Fei-Xiang],
Assessment of Leaf Area Index of Rice for a Growing Cycle Using Multi-Temporal C-Band PolSAR Datasets,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Zhang, S.Y.[Shuang-Yin], Li, J.[Jun], Wang, S.Y.[Si-Ying], Huang, Y.J.[Ying-Jing], Li, Y.Z.[Yi-Zhuo], Chen, Y.Y.[Yi-Yun], Fei, T.[Teng],
Repaid Identification and Prediction of Cadmium-Lead Cross-Stress of Different Stress Levels in Rice Canopy Based on Visible and Near-Infrared Spectroscopy,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Yang, M.D.[Ming-Der], Tseng, H.H.[Hsin-Hung], Hsu, Y.C.[Yu-Chun], Tsai, H.P.[Hui Ping],
Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Ding, M.J.[Ming-Jun], Guan, Q.H.[Qi-Hui], Li, L.[Lanhui], Zhang, H.M.[Hua-Min], Liu, C.[Chong], Zhang, L.[Le],
Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Wang, H.F.[Hong-Fei], Ghosh, A.[Aniruddha], Linquist, B.A.[Bruce A.], Hijmans, R.J.[Robert J.],
Satellite-Based Observations Reveal Effects of Weather Variation on Rice Phenology,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

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,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

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,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Jiang, R.[Rui], Wang, P.[Pei], Xu, Y.[Yan], Zhou, Z.Y.[Zhi-Yan], Luo, X.[Xiwen], Lan, Y.[Yubin], Zhao, G.[Genping], Sanchez-Azofeifa, A.[Arturo], Laakso, K.[Kati],
Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

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,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

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,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

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,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

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,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

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,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Jimenez-Sierra, D.A.[David Alejandro], Benítez-Restrepo, H.D.[Hernán Darío], Vargas-Cardona, H.D.[Hernán Darío], Chanussot, J.[Jocelyn],
Graph-Based Data Fusion Applied to Change Detection and Biomass Estimation in Rice Crops,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

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],
Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

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,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
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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.),
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
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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,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
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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,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
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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,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
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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,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
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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,
PandRS(169), 2020, pp. 135-151.
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,
GeoRS(58), No. 11, November 2020, pp. 7589-7601.
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,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
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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,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
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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,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
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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,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link 2012
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,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

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,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
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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,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
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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,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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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,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

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,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

Iwahashi, Y.[Yu], Ye, R.L.[Rong-Ling], 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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.Y.[Zhi-Yan], 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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
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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
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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
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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
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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
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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,
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DOI Link 2208
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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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Park, S.[Seonyoung], Lee, J.[Jaese], Yeom, J.[Jongmin], Seo, E.[Eunkyo], Im, J.[Jungho],
Performance of Drought Indices in Assessing Rice Yield in North Korea and South Korea under the Different Agricultural Systems,
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],
A Decision-Tree Approach to Identifying Paddy Rice Lodging with Multiple Pieces of Polarization Information Derived from Sentinel-1,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Liu, S.Y.[Shi-Yuan], Zhang, B.[Bin], Yang, W.G.[Wei-Guang], Chen, T.T.[Ting-Ting], Zhang, H.[Hui], Lin, Y.D.[Yong-Da], Tan, J.T.[Jiang-Tao], Li, X.[Xi], Gao, Y.[Yu], Yao, S.[Suzhe], Lan, Y.[Yubin], Zhang, L.[Lei],
Quantification of Physiological Parameters of Rice Varieties Based on Multi-Spectral Remote Sensing and Machine Learning Models,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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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,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Hu, J.[Jie], Chen, Y.P.[Yun-Ping], Cai, Z.W.[Zhi-Wen], Wei, H.D.[Hao-Dong], Zhang, X.Y.[Xin-Yu], Zhou, W.[Wei], Wang, C.[Cong], You, L.Z.[Liang-Zhi], Xu, B.D.[Bao-Dong],
Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Dimyati, M.[Muhammad], Supriatna, S.[Supriatna], Nagasawa, R.[Ryota], Pamungkas, F.D.[Fajar Dwi], Pramayuda, R.[Rizki],
A Comparison of Several UAV-Based Multispectral Imageries in Monitoring Rice Paddy (A Case Study in Paddy Fields in Tottori Prefecture, Japan),
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link 2303
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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,
IJIST(33), No. 2, 2023, pp. 622-643.
DOI Link 2303
brain MR image, hybrid rice optimization, multilevel thresholding, opposition-based learning BibRef

Xu, H.[Huiyao], Song, J.[Jia], Zhu, Y.Q.[Yun-Qiang],
Evaluation and Comparison of Semantic Segmentation Networks for Rice Identification Based on Sentinel-2 Imagery,
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Lu, Y.[Yang], Tao, X.P.[Xian-Peng], Zeng, N.Y.[Nian-Yin], Du, J.J.[Jiao-Jiao], Shang, R.[Rou],
Enhanced CNN Classification Capability for Small Rice Disease Datasets Using Progressive WGAN-GP: Algorithms and Applications,
RS(15), No. 7, 2023, pp. 1789.
DOI Link 2304
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Kurihara, J.[Junichi], Nagata, T.[Toru], Tomiyama, H.[Hiroyuki],
Rice Yield Prediction in Different Growth Environments Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging,
RS(15), No. 8, 2023, pp. 2004.
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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,
RS(15), No. 9, 2023, pp. xx-yy.
DOI Link 2305
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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.],
Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data,
RS(15), No. 10, 2023, pp. xx-yy.
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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,
RS(15), No. 11, 2023, pp. 2785.
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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],
Mapping Paddy Rice Planting Area in Dongting Lake Area Combining Time Series Sentinel-1 and Sentinel-2 Images,
RS(15), No. 11, 2023, pp. 2794.
DOI Link 2306
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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],
Deep-Learning-Based Rice Phenological Stage Recognition,
RS(15), No. 11, 2023, pp. 2891.
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Malathi, V., Gopinath, M.P.,
A Review on Rice Crop Disease Classification Using Computational Approach,
IJIG(23), No. 3 2023, pp. 2240007.
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Mirandilla, J.R.F.[Jean Rochielle F.], Yamashita, M.[Megumi], Yoshimura, M.[Mitsunori], Paringit, E.C.[Enrico C.],
Leaf Spectral Analysis for Detection and Differentiation of Three Major Rice Diseases in the Philippines,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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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,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Malvade, N.N.[Naveen N.], Yakkundimath, R.[Rajesh], Saunshi, G.B.[Girish B.], Elemmi, M.C.[Mahantesh C.],
Paddy variety identification from field crop images using deep learning techniques,
IJCVR(13), No. 4, 2023, pp. 405-419.
DOI Link 2307
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Chou, Y.S.[Yi-Shin], Chou, C.Y.[Cheng-Ying],
Deep Learning Approach for Paddy Field Detection Using Labeled Aerial Images: The Case of Detecting and Staging Paddy Fields in Central and Southern Taiwan,
RS(15), No. 14, 2023, pp. 3575.
DOI Link 2307
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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,
RS(15), No. 16, 2023, pp. 4021.
DOI Link 2309
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Huang, C.H.[Chen-Hao], You, S.[Shucheng], Liu, A.[Aixia], Li, P.[Penghan], Zhang, J.H.[Jian-Hua], Deng, J.S.[Jin-Song],
High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 Data,
RS(15), No. 16, 2023, pp. 4055.
DOI Link 2309
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Zhang, J.C.[Jing-Cheng], Li, H.[Huizi], Tian, Y.Y.[Yang-Yang], Qiu, H.X.[Han-Xiao], Zhou, X.[Xuehe], Ma, H.Q.[Hui-Qin], Yuan, L.[Lin],
Assessing Rice Sheath Blight Disease Habitat Suitability at a Regional Scale through Multisource Data Analysis,
RS(15), No. 23, 2023, pp. 5530.
DOI Link 2312
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Yamashita, M.[Megumi], Kaieda, T.[Tomoya], Toyoda, H.[Hiro], Yamaguchi, T.[Tomoaki], Katsura, K.[Keisuke],
Spatial Estimation of Daily Growth Biomass in Paddy Rice Field Using Canopy Photosynthesis Model Based on Ground and UAV Observations,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
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Wang, G.Z.[Geng-Ze], Meng, D.[Di], Chen, R.Q.[Ri-Qiang], Yang, G.J.[Gui-Jun], Wang, L.G.[Lai-Gang], Jin, H.L.[Hai-Liang], Ge, X.S.[Xiao-San], Feng, H.K.[Hai-Kuan],
Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images,
RS(16), No. 2, 2024, pp. 277.
DOI Link 2402
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Zhang, B.[Bolin], Li, K.[Kun], Zhang, F.[Fengli], Shao, Y.[Yun], Wang, D.[Duo], Lou, L.[Linjiang],
Rice Height Estimation with Multi-Baseline PolInSAR Data and Optimal Detection Baseline Combination Analysis,
RS(16), No. 2, 2024, pp. 358.
DOI Link 2402
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Zhu, W.C.[Wen-Chao], Peng, X.Q.[Xin-Qin], Ding, M.J.[Ming-Jun], Li, L.H.[Lan-Hui], Liu, Y.[Yaqun], Liu, W.[Wei], Yang, M.D.[Meng-Die], Chen, X.X.[Xin-Xin], Cai, J.[Jiale], Huang, H.B.[Han-Bing], Dong, Y.H.[Ying-Han], Lu, J.[Jiaye],
Decline in Planting Areas of Double-Season Rice by Half in Southern China over the Last Two Decades,
RS(16), No. 3, 2024, pp. 440.
DOI Link 2402
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Hosonuma, K.[Kohei], Aida, K.[Kentaro], Ballaran, V.[Vicente], Nagumo, N.[Naoko], Sanchez, P.A.J.[Patricia Ann J.], Sumita, T.[Tsuyoshi], Homma, K.[Koki],
Evaluation of Geographical and Annual Changes in Rice Planting Patterns Using Satellite Images in the Flood-Prone Area of the Pampanga River Basin, the Philippines,
RS(16), No. 3, 2024, pp. 499.
DOI Link 2402
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Prabhakar, M.[Mathyam], Gopinath, K.A.[Kodigal A.], Kumar, N.R.[Nakka Ravi], Thirupathi, M.[Merugu], Sravan, U.S.[Uppu Sai], Kumar, G.S.[Golla Srasvan], Siva, G.S.[Gutti Samba], Chandana, P.[Pebbeti], Singh, V.K.[Vinod Kumar],
Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing,
RS(16), No. 6, 2024, pp. 954.
DOI Link 2403
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Wang, Q.Y.[Qing-Yan], Sun, L.Z.[Long-Zhi], Yang, X.[Xuan],
Identifying Spatial Determinants of Rice Yields in Main Producing Areas of China Using Geospatial Machine Learning,
IJGI(13), No. 3, 2024, pp. 76.
DOI Link 2404
BibRef

Gao, X.Y.[Xin-Yi], Chi, H.[Hong], Huang, J.L.[Jin-Liang], Han, Y.F.[Yi-Fei], Li, Y.F.[Yi-Fan], Ling, F.[Feng],
Comparison of Cloud-Mask Algorithms and Machine-Learning Methods Using Sentinel-2 Imagery for Mapping Paddy Rice in Jianghan Plain,
RS(16), No. 7, 2024, pp. 1305.
DOI Link 2404
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Du, X.Y.[Xiao-Yue], Zheng, L.Y.[Li-Yuan], Zhu, J.P.[Jiang-Peng], He, Y.[Yong],
Enhanced Leaf Area Index Estimation in Rice by Integrating UAV-Based Multi-Source Data,
RS(16), No. 7, 2024, pp. 1138.
DOI Link 2404
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Sato, Y.[Yuki], Tsuji, T.[Takeshi], Matsuoka, M.[Masayuki],
Estimation of Rice Plant Coverage Using Sentinel-2 Based on UAV-Observed Data,
RS(16), No. 9, 2024, pp. 1628.
DOI Link 2405
BibRef

Clarke, A.[Allister], Yates, D.[Darren], Blanchard, C.[Christopher], Islam, M.Z.[Md. Zahidul], Ford, R.[Russell], Rehman, S.U.[Sabih-Ur], Walsh, R.P.[Robert Paul],
Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia,
RS(16), No. 10, 2024, pp. 1815.
DOI Link 2405
BibRef

Yang, M.L.[Mao-Lin], Guo, B.[Bin], Wang, J.L.[Jian-Lin],
A novel and robust method for large-scale single-season rice mapping based on phenology and statistical data,
PandRS(213), 2024, pp. 14-32.
Elsevier DOI 2406
Rice mapping, Phenology index, Statistical data, Northern China, Sentinel-2 BibRef

Yu, M.L.[Ming-Lei], He, J.Y.[Jiao-Yang], Li, W.[Wanyu], Zheng, H.B.[Heng-Biao], Wang, X.[Xue], Yao, X.[Xia], Cheng, T.[Tao], Zhang, X.[Xiaohu], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Tian, Y.C.[Yong-Chao],
Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs),
RS(16), No. 12, 2024, pp. 2073.
DOI Link 2406
BibRef

Lin, F.F.[Fen-Fang], Li, B.R.[Bao-Rui], Zhou, R.[Ruiyu], Chen, H.Z.[Hong-Zhou], Zhang, J.[Jingcheng],
Early Detection of Rice Sheath Blight Using Hyperspectral Remote Sensing,
RS(16), No. 12, 2024, pp. 2047.
DOI Link 2406
BibRef

Li, Y.C.[Yue-Chen], Zhao, R.K.[Rong-Kun], Wang, Y.[Yue],
Mapping Ratoon Rice Fields Based on SAR Time Series and Phenology Data in Cloudy Regions,
RS(16), No. 15, 2024, pp. 2703.
DOI Link 2408
BibRef

Filho, A.S.F.[Alexandre S. Fernandes], Fonseca, L.M.G.[Leila M. G.], Bendini, H.D.N.[Hugo Do N.],
Mapping Irrigated Rice in Brazil Using Sentinel-2 Spectral-Temporal Metrics and Random Forest Algorithm,
RS(16), No. 16, 2024, pp. 2900.
DOI Link 2408
BibRef

Zhang, Y.[Yuan], Jiang, Y.[Youyi], Xu, B.[Bo], Yang, G.J.[Gui-Jun], Feng, H.K.[Hai-Kuan], Yang, X.D.[Xiao-Dong], Yang, H.[Hao], Liu, C.B.[Chang-Bin], Cheng, Z.[Zhida], Feng, Z.H.[Zi-Heng],
Study on the Estimation of Leaf Area Index in Rice Based on UAV RGB and Multispectral Data,
RS(16), No. 16, 2024, pp. 3049.
DOI Link 2408
BibRef

Chen, X.B.[Xue-Bing], Shen, R.[Ruoque], Pan, B.H.[Bai-Hong], Peng, Q.Y.[Qiong-Yan], Zhang, X.[Xi], Fu, Y.Y.[Yang-Yang], Yuan, W.P.[Wen-Ping],
A High-Resolution Distribution Dataset of Paddy Rice in India Based on Satellite Data,
RS(16), No. 17, 2024, pp. 3180.
DOI Link 2409
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Liu, R.[Ruoqi], Dong, J.[Jinwei], Ge, Y.[Yong], Lin, H.[Hui], Che, X.[Xianghong], Di, Y.Y.[Yuan-Yuan], Chen, X.[Xi], Qi, S.H.[Shu-Hua], Ding, M.J.[Ming-Jun], Xiao, X.M.[Xiang-Ming], Zhang, G.[Geli],
Tracking paddy rice acreage, flooding impacts, and mitigations during El Niño flooding events using Sentinel-1/2 imagery and cloud computing,
PandRS(217), 2024, pp. 165-178.
Elsevier DOI 2409
In-season rice mapping, Flood mapping, Post-flood rice replanting, Extreme weather, Agricultural mitigation BibRef

Ibrahim, S.[Sa'ad], Balzter, H.[Heiko],
Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies,
RS(16), No. 19, 2024, pp. 3657.
DOI Link 2410
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Appalanaidu, M.V.[Majji V.], Kumaravelan, G.,
Nutrient Deficiency Classification in Rice Plants Using DenseNet121,
IJIG(24), No. 5, September 2024, pp. 2340010.
DOI Link 2410
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Baldin, C.M.[Christian Massimiliano], Casella, V.M.[Vittorio Marco],
Vegetative Index Intercalibration Between PlanetScope and Sentinel-2 Through a SkySat Classification in the Context of 'Riserva San Massimo' Rice Farm in Northern Italy,
RS(16), No. 21, 2024, pp. 3921.
DOI Link 2411
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Hu, X.L.[Xiao-Long], Shi, L.[Liangsheng], Lin, L.[Lin], Li, S.[Shenji], Deng, X.Z.[Xian-Zhi], Li, J.[Jinmin], Bian, J.[Jiang], Su, C.[Chenye], Du, S.[Shuai], Wang, T.[Tinghan], Wang, Y.J.[Yu-Jie], Zhang, Z.[Zhitao],
Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model,
RS(16), No. 20, 2024, pp. 3906.
DOI Link 2411
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Dubey, R.K.[Ratnesh Kumar], Choubey, D.K.[Dilip Kumar],
Rice Impact Illness Acknowledgment Employing a Profound Convolutional Neural Arrange,
ICCVMI23(1-6)
IEEE DOI 2403
Support vector machines, Histograms, Image recognition, Databases, Convolution, Classification BibRef

Wang, X.Q.[Xiao-Qi], Wang, Y.J.[Yao-Jun], Zhao, J.B.[Jing-Bo], Niu, J.[Jing],
ECA-ConvNeXt: A Rice Leaf Disease Identification Model Based on ConvNeXt,
AgriVision23(6235-6243)
IEEE DOI 2309
BibRef

Yamashita, M., Toyoda, H., Tanaka, Y.,
Light Environment and LAI Monitoring In Rice Community By Ground- And UAV-based Remote Sensing,
ISPRS20(B3:1085-1089).
DOI Link 2012
BibRef

Saadat, M., Hasanlou, M., Homayouni, S.,
Rice Crop Mapping Using Sentinel-1 Time Series Images (case Study: Mazandaran, Iran),
SMPR19(897-904).
DOI Link 1912
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Sakata, K., Rikimaru, A., Yasunaga, Y.,
Study On Extraction Methodology of Rice Growth Characteristics in Field Using Terrestrial Laser Measurement Data,
Environmental19(61-67).
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Yamada, Y.,
Crop Species Recognition And Discrimination Paddy-rice-growingfields From Reaped-fields By The Radar Vegetation Index (rvi) Of Alos-2/palsar2,
ISPRS16(B8: 1083-1087).
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Kimura, A., Kondo, A., Mochizuki, K.,
A Study on the SAR Data Observation Time For The Classification Of Planting Condition Of Paddy Fields,
ISPRS16(B8: 927-930).
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Willkomm, M., Bolten, A., Bareth, G.,
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,
ISPRS16(B1: 1071-1077).
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Chen, C.F., Son, N.T., Chen, C.R., Chang, L.Y., Chiang, S.H.,
Rice Crop Mapping Using Sentinel-1a Phenological Metrics,
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Son, N.T., Chen, C.F., Chen, C.R., Chang, L.Y., Chiang, S.H.,
Rice Yield Estimation Through Assimilating Satellite Data Into A Crop Simumlation Model,
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Elshorbagy, A.M., Imam, E.H., Nour, M.H.,
Rice Area Inter Annual Variation through a Remote Sensing Based Mapping Algorithm,
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Hashjin, S.S.[S. Sharifi], Darvishzadeh, R., Khandan, R.,
A Study toward the Evaluation of ALOS Images for LAI Estimation in Rice Fields,
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Bendig, J., Willkomm, M., Tilly, N., Gnyp, M.L., Bennertz, S., Qiang, C., Miao, Y., Lenz-Wiedemann, V.I.S., Bareth, G.,
Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring In Northeast China,
UAV-g13(45-50).
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Tilly, N., Hoffmeister, D., Liang, H., Cao, Q., Liu, Y., Lenz-Wiedemann, V., Miao, Y., Bareth, G.,
Evaluation of Terrestrial Laser Scanning for Rice Growth Monitoring,
ISPRS12(XXXIX-B7:351-356).
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Wu, L.[Ling], Liu, X.N.[Xiang-Nan], Liu, M.L.[Mei-Ling],
Analysis of the fractal characteristic of the hyperspectral curves of the rice under plumbum pollution stress,
CVRS12(296-300).
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Sarkar, S.[Subrata], Bhattacharyya, N.[Nabarun], Mukherjee, S.[Subhankar],
Determination of Amylose content in rice using electronic tongue,
ICIIP11(1-4).
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Zhou, Y.F.[Ying-Feng], Wang, Y.M.[Ya-Ming], Yao, Q.[Qing],
Segmentation of rice disease spots based on improved BPNN,
IASP10(575-578).
IEEE DOI 1004
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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,
CISP09(1-4).
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Wheat Crop Analysis, Detection, Change .

Last update:Nov 26, 2024 at 16:40:19