Scientific Papers

Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images | Plant Methods

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  • Pallottino F, Biocca M, Nardi P, Figorilli S, Menesatti P, Costa C. Science mapping approach to analyze the research evolution on precision agriculture: world, EU and Italian situation. Precis Agric. 2018;19(6):1011–26.

    Article 

    Google Scholar
     

  • Goel RK, Yadav CS, Vishnoi S, Rastogi R. Smart agriculture-urgent need of the day in developing countries. SUSTAIN COMPUT-INFOR. 2021; 30.

  • Duy Ba N, Wagner W. European Rice Cropland Mapping with Sentinel-1 Data: the Mediterranean Region Case Study. Water. 2017; 9(6).

  • Cisternas I, Velasquez I, Caro A, Rodriguez A. Systematic literature review of implementations of precision agriculture. Comput Electron Agric. 2020; 176.

  • Sharma V, Tripathi AK, Mittal H. Technological revolutions in smart farming: current trends, challenges & future directions. Comput Electron Agric. 2022; 201.

  • Ding H-q, Lu Q-p. Research Progress and Application Prospect of Near Infrared Spectroscopy in Soil Nutrition Analysis. SPECTROSC SPECT ANAL. 2012;32(1):88–91.

    CAS 

    Google Scholar
     

  • Kumar SA, Ilango P. The Impact of Wireless Sensor Network in the field of Precision Agriculture: a review. Wirel PERS COMMUN. 2018;98(1):685–98.

    Article 

    Google Scholar
     

  • Han J, Zhang Z, Luo Y, Cao J, Zhang L, Zhuang H, Cheng F, Zhang J, Tao F. Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020. Agric Syst. 2022; 200.

  • Mosleh MK, Hassan QK, Chowdhury EH. Application of remote sensors in Mapping Rice Area and forecasting its production: a review. Sensors. 2015;15(1):769–91.

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zheng H, Cheng T, Yao X, Deng X, Tian Y, Cao W, Zhu Y. Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Res. 2016;198:131–9.

    Article 

    Google Scholar
     

  • Hasan SS, Zhen L, Miah MG, Ahamed T, Samie A. Impact of land use change on ecosystem services: a review. Environ Dev. 2020;34:100527.

    Article 

    Google Scholar
     

  • Son N-T, Chen C-F, Chen C-R, Toscano P, Cheng Y-S, Guo H-Y, Syu C-H. A phenological object-based approach for rice crop classification using time-series Sentinel-1 synthetic aperture radar (SAR) data in Taiwan. Int J Remote Sens. 2021;42(7):2722–39.

    Article 

    Google Scholar
     

  • Zhan P, Zhu W, Li N. An automated rice mapping method based on flooding signals in synthetic aperture radar time series. Remote Sens Environ. 2021;252:112112.

    Article 

    Google Scholar
     

  • Xu S, Zhu X, Chen J, Zhu X, Duan M, Qiu B, Wan L, Tan X, Xu YN, Cao R. A robust index to extract paddy fields in cloudy regions from SAR time series. Remote Sens Environ. 2023;285(1):113374.

    Article 

    Google Scholar
     

  • Weiss M, Jacob F, Duveiller G. Remote sensing for agricultural applications: a meta-review. Remote Sens Environ. 2020;236:111402.

    Article 

    Google Scholar
     

  • Dong J, Xiao X. Evolution of regional to global paddy rice mapping methods: a review. ISPRS J Photogramm Remote Sens. 2016;119:214–27.

    Article 
    ADS 

    Google Scholar
     

  • Wang L, Ma H, Li J, Gao Y, Fan L, Yang Z, Yang Y, Wang C. An automated extraction of small- and middle-sized rice fields under complex terrain based on SAR time series: a case study of Chongqing. Comput Electron Agric. 2022; 200.

  • Bouvet A, Toan TL, Lam-Dao N. Monitoring of the Rice Cropping System in the Mekong Delta Using ENVISAT/ASAR Dual Polarization Data. IEEE Trans Geosci Remote Sens. 2009;47(2):517–26.

    Article 
    ADS 

    Google Scholar
     

  • Chauhan S, Darvishzadeh R, Boschetti M, Pepe M, Nelson A. Remote sensing-based crop lodging assessment: current status and perspectives. ISPRS J Photogramm Remote Sens. 2019;151:124–40.

    Article 
    ADS 

    Google Scholar
     

  • Mandal D, Kumar V, Ratha D, Lopez-Sanchez JM, Bhattacharya A, McNairn H, Rao YS, Ramana KV. Assessment of rice growth conditions in a semi-arid region of India using the generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data. Remote Sens Environ. 2020;237(1):111561.

    Article 

    Google Scholar
     

  • Moeini RA, Ashourloo D, Salehi SH, Nematollahi H. Developing an automatic phenology-based algorithm for Rice Detection using Sentinel-2 Time-Series Data. IEEE J Sel Top Appl Earth Obs Remote Sens. 2019;12(5):1471–81.

    Article 
    ADS 

    Google Scholar
     

  • He Y, Dong J, Liao X, Sun L, Wang Z, You N, Li Z, Fu P. Examining rice distribution and cropping intensity in a mixed single- and double-cropping region in South China using all available Sentinel 1/2 images. Int J Appl Earth Obs Geoinf. 2021; 101.

  • Pang J, Zhang R, Yu B, Liao M, Lv J, Xie L, Li S, Zhan J. Pixel-level rice planting information monitoring in Fujin City based on time-series SAR imagery. Int J Appl Earth Obs Geoinf. 2021;104:102551.


    Google Scholar
     

  • Belgiu M, Bijker W, Csillik O, Stein A. Phenology-based sample generation for supervised crop type classification. Int J Appl Earth Obs Geoinf. 2021; 95.

  • Cao J, Cai X, Tan J, Cui Y, Xie H, Liu F, Yang L, Luo Y. Mapping paddy rice using landsat time series data in the Ganfu Plain irrigation system, Southern China, from 1988 – 2017. Int J Remote Sens. 2020;42(4):1556–76.

    Article 

    Google Scholar
     

  • Gong P, Wang J, Yu L, Zhao Y, Zhao Y, Liang L, Niu Z, Huang X, Fu H, Liu S, Li C, Li X, Fu W, Liu C, Xu Y, Wang X, Cheng Q, Hu L, Yao W, Zhang H, Zhu P, Zhao Z, Zhang H, Zheng Y, Ji L, Zhang Y, Chen H, Yan A, Guo J, Yu L, Wang L, Liu X, Shi T, Zhu M, Chen Y, Yang G, Tang P, Xu B, Giri C, Clinton N, Zhu Z, Chen J, Chen J. Finer resolution observation and monitoring of global land cover: first mapping results with landsat TM and ETM + data. Int J Remote Sens. 2013;34(7):2607–54.

    Article 

    Google Scholar
     

  • Dong J, Xiao X, Kou W, Qin Y, Zhang G, Li L, Jin C, Zhou Y, Wang J, Biradar C, Liu J, Moore B. Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms. Remote Sens Environ. 2015;160:99–113.

    Article 
    ADS 

    Google Scholar
     

  • Ai L, Sun S, Li S, Ma H. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing. Remote Sens Nat Resour. 2021;33(4):10–8.


    Google Scholar
     

  • Zhou N, Yang p, Wei C, Shen Z, Yu J, Ma X, Luo J. Accurate extraction method for cropland in mountainous areas based on field parcel. Trans CSAE. 2021;37(19):260–6.


    Google Scholar
     

  • Dela Torre DMG, Gao J, Macinnis-Ng C. Remote sensing-based estimation of rice yields using various models: a critical review. Geo-spatial Inf Sci. 2021;24(4):580–603.

    Article 

    Google Scholar
     

  • Jay S, Maupas F, Bendoula R, Gorretta N. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Res. 2017;210:33–46.

    Article 

    Google Scholar
     

  • Oyoshi K, Tomiyama N, Okumura T, Sobue S, Sato J. Mapping rice-planted areas using time-series synthetic aperture radar data for the Asia-RiCE activity. Paddy Water Environ. 2016;14(4):463–72.

    Article 

    Google Scholar
     

  • Shao Y, Fan X, Liu H, Xiao J, Ross S, Brisco B, Brown R, Staples G. Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sens Environ. 2001;76(3):310–25.

    Article 
    ADS 

    Google Scholar
     

  • Yang H, Pan B, Li N, Wang W, Zhang J, Zhang X. A systematic method for spatio-temporal phenology estimation of paddy rice using time series Sentinel-1 images. Remote Sens Environ. 2021;259(11):112394.

    Article 

    Google Scholar
     

  • Nguyen DB, Gruber A, Wagner W. Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data. Remote Sens Lett. 2016;7(12):1209–18.

    Article 

    Google Scholar
     

  • Phan H, Le Toan T, Bouvet A. Understanding dense Time Series of Sentinel-1 backscatter from Rice fields: Case Study in a Province of the Mekong Delta, Vietnam. Remote Sens. 2021;13(5):921.

    Article 
    ADS 

    Google Scholar
     

  • Singha M, Dong J, Zhang G, Xiao X. High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Sci Data. 2019;6(1):26.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang H, Pan B, Wu W, Tai J. Field-based rice classification in Wuhua County through integration of multi-temporal Sentinel-1A and Landsat-8 OLI data. Int J Appl Earth Obs Geoinf. 2018;69:226–36.


    Google Scholar
     

  • Singha M, Dong J, Sarmah S, You N, Zhou Y, Zhang G, Doughty R, Xiao X. Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine. ISPRS J Photogramm Remote Sens. 2020;166:278–93.

    Article 
    ADS 

    Google Scholar
     

  • Jardim R, Morgado-Dias F. Savitzky–Golay filtering as image noise reduction with sharp color reset. Microprocess Microsyst. 2020;74:103006.

    Article 

    Google Scholar
     

  • Clauss K, Ottinger M, Kuenzer C. Mapping rice areas with Sentinel-1 time series and superpixel segmentation. Int J Remote Sens. 2017;39(5):1399–420.

    Article 

    Google Scholar
     

  • Dong J, Xiao X, Menarguez MA, Zhang G, Qin Y, Thau D, Biradar C, Moore B 3. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens Environ. 2016;185:142–54.

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Torbick N, Chowdhury D, Salas W, Qi J. Monitoring Rice Agriculture across Myanmar using Time Series Sentinel-1 assisted by Landsat-8 and PALSAR-2. Remote Sens. 2017;9(2):119.

    Article 
    ADS 

    Google Scholar
     

  • Bakar SBA, Shaari AT, Chuah HT, Ewe HT. A preliminary study of Phenological Growth Stages of Wetland Rice using ERS1/2 SAR Data. IEEE Geosci Remote Sens Lett. 1997:1069–71.

  • Setiyono T, Quicho E, Gatti L, Campos-Taberner M, Busetto L, Collivignarelli F, García-Haro F, Boschetti M, Khan N, Holecz F. Spatial Rice Yield Estimation based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model. Remote Sens. 2018;10(2):293–313.

    Article 
    ADS 

    Google Scholar
     

  • Zhang X, Wu B, Ponce-Campos G, Zhang M, Chang S, Tian F. Mapping up-to-date Paddy Rice Extent at 10 M resolution in China through the Integration of Optical and Synthetic aperture radar images. Remote Sens. 2018;10(8):1200–26.

    Article 
    ADS 

    Google Scholar
     

  • You N, Dong J. Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. ISPRS J Photogramm Remote Sens. 2020;161:109–23.

    Article 
    ADS 

    Google Scholar
     

  • Yaotong C, Hui L, Meng Z. Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data. Adv Space Res. 2019;64(11):2233–44.

    Article 

    Google Scholar
     

  • Yang Z, Shao Y, Li K, Liu Q, Liu L, Brisco B. An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data. Remote Sens Environ. 2017;195:184–201.

    Article 
    ADS 

    Google Scholar
     

  • Du M, Huang J, Wei P, Yang L, Chai D, Peng D, Sha J, Sun W, Huang R. Dynamic mapping of Paddy Rice using Multi-temporal Landsat Data based on a deep semantic segmentation model. Agronomy. 2022; 12(7).

  • Wei P, Chai D, Lin T, Tang C, Du M, Huang J. Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model. ISPRS J Photogramm Remote Sens. 2021;174:198–214.

    Article 
    ADS 

    Google Scholar
     

  • Ni R, Tian J, Li X, Yin D, Li J, Gong H, Zhang J, Zhu L, Wu D. An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 2021; 178:282–296.

  • Li Z-p, Long Y-q, Tang P-q, Tan J-y, Li Z-g, Wu W-b, Hu Y-n, Yang P. Spatio-temporal changes in rice area at the northern limits of the rice cropping system in China from 1984 to 2013. J Integr Agric. 2017;16(2):360–7.

    Article 

    Google Scholar
     

  • Yin Q, Liu M, Cheng J, Ke Y, Chen X. Mapping Paddy Rice planting area in Northeastern China using Spatiotemporal Data Fusion and phenology-based method. Remote Sens. 2019; 11(14).

  • Wang L, Ma H, Li J, Gao Y, Fan L, Yang Z, Yang Y, Wang C. An automated extraction of small- and middle-sized rice fields under complex terrain based on SAR time series: a case study of Chongqing. Comput Electron Agric. 2022;200:107232.

    Article 

    Google Scholar
     

  • Lasko K, Vadrevu KP, Tran VT, Justice C. Mapping double and single crop Paddy Rice with Sentinel-1A at varying spatial scales and polarizations in Hanoi, Vietnam. IEEE J Sel Top Appl Earth Obs Remote Sens. 2018;11(2):498–512.

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhou L. The theory and practice of agricultural regional planning in China M. Hefei: University of Science and Technology of China Press; 1993.


    Google Scholar
     

  • Wang Y, Zhang Z, Zuo L, Wang X, Zhao X, Sun F. Mapping crop distribution patterns and changes in China from 2000 to 2015 by fusing Remote-Sensing, statistics, and knowledge-based crop phenology. Remote Sens. 2022;14(8):1800.

    Article 
    ADS 

    Google Scholar
     

  • Zhu L, Liu X, Wu L, Liu M, Lin Y, Meng Y, Ye L, Zhang Q, Li Y. Detection of paddy rice cropping systems in southern China with time series Landsat images and phenology-based algorithms. GISci Remote Sens. 2021;58(5):733–55.

    Article 

    Google Scholar
     

  • Yao F, Liu D, Zhang J, Wang P. Estimation of Rice Yield with a process-based model and remote Sensing Data in the Middle and Lower reaches of Yangtze River of China. J Indian Soc Remote Sens. 2017;45(3):477–84.

    Article 

    Google Scholar
     

  • Ren H-r, Zhang Y-q, He Q-j, Li R-p. Zhou G-s. extraction of Pddy Rice planting Area based on multi -temporal FY-3 MERSI Remote sensing images. SPECTROSC SPECT ANAL. 2023;43(5):1606–11.

    CAS 

    Google Scholar
     

  • Housman I, Chastain R, Finco M. An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-based remote sensing forest change detection methods: Case studies in the United States. Remote Sens. 2018; 10(8).

  • Ma H, Wang L, Sun W, Yang S, Gao Y, Fan L, Yang G, Wang Y. A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar. Front Ecol Evol. 2023; 11.

  • Yu F, Lin Q, Huang Z. Detection Method Research for Adulteration to Rice Bran OilBased on Fingerprint Similarity. J Chin Cereals Oils Assoc. 2013;28(10):118–22.

    CAS 

    Google Scholar
     

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