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Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Manuel Campos-Taberner, Francisco García-Haro, Gustau Camps-Valls, Gonçal Grau-Muedra, Francesco Nutini, Lorenzo Busetto, Dimitrios Katsantonis, Dimitris Stavrakoudis, Chara Minakou, Luca Gatti, Massimo Barbieri, Francesco Holecz (+2 others)
2017 Remote Sensing  
The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving  ...  This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts  ...  Acknowledgments: The research leading to these results was conducted within the ERMES FP7 project  ... 
doi:10.3390/rs9030248 fatcat:2v6g3wfey5dhveeuuc7wv67dxy

Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model

Tri Setiyono, Emma Quicho, Luca Gatti, Manuel Campos-Taberner, Lorenzo Busetto, Francesco Collivignarelli, Francisco García-Haro, Mirco Boschetti, Nasreen Khan, Francesco Holecz
2018 Remote Sensing  
SAR data were used to generate rice area maps using MAPScape-RICE to mask LAI map products for further processing, including smoothing with logistic function and running yield simulation using the ORYZA  ...  This study is dedicated to demonstrating and validating the methodology of remote sensing and crop growth model-based rice yield estimation with the intention of historical yield data generation for application  ...  SAR data were provided by the European Space Agency (ESA) from Sentinel-1A satellite.  ... 
doi:10.3390/rs10020293 fatcat:ctwq67r2m5eqjiay3aqnnxnyze

Recent Advancement of Synthetic Aperture Radar (SAR) Systems and Their Applications to Crop Growth Monitoring [chapter]

Jiali Shang, Jiangui Liu, Zhongxin Chen, Heather McNairn, Andrew Davidson
2022 Remote Sensing [Working Title]  
Engineering advancements in SAR technologies, new processing algorithms, and the availability of open-access SAR data, have led to the recent acceleration in the uptake of this technology to map and monitor  ...  parameter estimation, and change detection; and (3) summary and perspectives for future application development.  ...  The potential of SAR for supporting crop growth monitoring through the quantitative estimation of crop parameters-such as Leaf (or Plant) Area Index (LAI or PAI), plant height and density, fresh and dry  ... 
doi:10.5772/intechopen.102917 fatcat:alc4lsj5ofa37os4ij6sptn6qm

Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework

Mo Wang, Jing Wang, Li Chen, Zhigang Du
2022 Open Geosciences  
Monitoring rice planting areas and their phenological phases is crucial for yield estimation and informed decision-making.  ...  This study proposed a unified method for mapping rice field and rice phenology with a dynamic time wrapping (DTW) distance-based classifier and its variant sub-DTW algorithm using Sentinel-1's synthetic  ...  We then applied data smoothing on the interpolated time series to acquire continuous and smooth dynamics of the SAR data.  ... 
doi:10.1515/geo-2022-0369 fatcat:fnaon3bahnfxrotjvjg3466l6y

Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

S. Pazhanivelan, P. Kannan, P. Christy Nirmala Mary, E. Subramanian, S. Jeyaraman, A. Nelson, T. Setiyono, F. Holecz, M. Barbieri, M. Yadav
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth  ...  Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal  ...  SAR data were provided by ASI/e-GEOS for COSMO-SkyMed and by InfoTerra GmbH for TerraSAR-X.  ... 
doi:10.5194/isprsarchives-xl-7-w3-85-2015 fatcat:bbebz453pbevnkrnrhsokeetay

Sinergistic use of radar and optical data for agricultural data products assimilation: A case study in Central Italy

R. Anniballe, R. Casa, F. Castaldi, F. Fascetti, F. Fusilli, W. Huang, G. Laneve, P. Marzialetti, A. Palombo, S. Pascucci, N. Pierdicca, S. Pignatti (+5 others)
2015 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
of soil and crops.  ...  From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics  ...  Optical data The processing of optical data has been devoted to the retrieval of biochemical and biophysical variables, both at the canopy and leaf levels.  ... 
doi:10.1109/igarss.2015.7326544 dblp:conf/igarss/AnniballeCCFFHL15 fatcat:p3v2eu3blrexdhdsienuq5jxf4


M. A. Gomarasca, A. Tornato, D. Spizzichino, E. Valentini, A. Taramelli, G. Satalino, M. Vincini, M. Boschetti, R. Colombo, L. Rossi, E. Borgogno Mondino, L. Perotti (+2 others)
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The paper introduce topics as crop mapping and monitoring, biophysical parameters, phenology and yield estimations, through several concluded or ongoing international projects such as: ERMES -FP7 (http  ...  As conclusion, SNAP software for image processing of Sentinel data was demonstrated and tested together with Earth Engine software for specific vertical agriculture applications.  ...  information on crop growth and seasonal dynamics are increasingly needed to develop monitoring systems aimed to detect seasonal anomalies, support site specific management and estimate crop yield at the  ... 
doi:10.5194/isprs-archives-xlii-3-w6-91-2019 fatcat:ypka3yewnfgnfaswewc77vt3ym

Understanding Dense Time Series of Sentinel-1 Backscatter from Rice Fields: Case Study in a Province of the Mekong Delta, Vietnam

Hoa Phan, Thuy Le Toan, Alexandre Bouvet
2021 Remote Sensing  
The objective of this study is to analyze in detail the backscatter temporal variation of rice fields, using Sentinel-1 from 2015 to 2020 and in-situ data for the 5 rice seasons over 2 years 2017–2018,  ...  New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, phenological stage and the mapping of the short cycle and long cycle rice varieties have been developed  ...  Acknowledgments: Field data collection was funded by the CESBIO project linked to GEOGLAM/Asia-RICE, under agreement of the Southern Satellite Technology and Applications Centre (VAST).  ... 
doi:10.3390/rs13050921 fatcat:npy367cuibfkvfy7w3hlgwjcgq

A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

Mehdi Hosseini, Heather McNairn, Scott Mitchell, Laura Dingle Robertson, Andrew Davidson, Nima Ahmadian, Avik Bhattacharya, Erik Borg, Christopher Conrad, Katarzyna Dabrowska-Zielinska, Diego de de Abelleyra, Radoslaw Gurdak (+10 others)
2021 Remote Sensing  
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors.  ...  In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM.  ...  Provision of the RADARSAT-2 data was through the Canadian Space Agency and MacDonald, Dettwiler and Associates Ltd.  ... 
doi:10.3390/rs13071348 fatcat:2angnlviyngbbapn23bcoliiau

Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions

Laura Stendardi, Stein Karlsen, Georg Niedrist, Renato Gerdol, Marc Zebisch, Mattia Rossi, Claudia Notarnicola
2019 Remote Sensing  
Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.  ...  In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South  ...  We are grateful to Eurac research (Alessandro Zandonai and Stefano Della Chiesa) for field maintenance and Norut for the for logistical support and data preprocessing.  ... 
doi:10.3390/rs11050542 fatcat:yqthr7m6unegronvav5tx4dqom

A high-resolution, integrated system for rice yield forecasting at district level

Valentina Pagani, Tommaso Guarneri, Lorenzo Busetto, Luigi Ranghetti, Mirco Boschetti, Ermes Movedi, Manuel Campos-Taberner, Francisco Javier Garcia-Haro, Dimitrios Katsantonis, Dimitris Stavrakoudis, Elisabetta Ricciardelli, Filomena Romano (+5 others)
2018 Agricultural Systems  
RS was used to identify rice-cropped 28 area and to derive spatially distributed sowing dates, as well as for the dynamic assimilation of RS-29 derived leaf area index (LAI) data within the crop model.  ...  The systemextending the 39 one used for rice within the EC-JRC-MARS forecasting systemwas pre-operationally used in 2015 40 and 2016 to provide early yield estimates to private companies and institutional  ...  Exploitation of SAR and optical sentinel data to detect rice crop and 517 estimate seasonal dynamics of leaf area index. Remote Sens. 9, 248.  ... 
doi:10.1016/j.agsy.2018.05.007 fatcat:4sn5llmlvjejvdg24apd3hbecu

Radar Remote Sensing of Agricultural Canopies: A Review

Susan C. Steele-Dunne, Heather McNairn, Alejandro Monsivais-Huertero, Jasmeet Judge, Pang-Wei Liu, Kostas Papathanassiou
2017 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Agronomists are 673 often interested in exploiting Leaf area Index (LAI) or biomass as surrogates, since both are good 674 indicators of potential crop yield [206].  ...  Prevot et al. [96] review these approaches, which have considered canopy (or leaf) water content and Leaf Area Index (LAI) as descriptors of the vegetation canopy.  ... 
doi:10.1109/jstars.2016.2639043 fatcat:6w5qd3dg2jd7ncdod7nn2un7gi

Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for Rice Crop Characterisation: Examples from Bangladesh [chapter]

Andrew Nelson, Mirco Boschetti, Giacinto Manfron, Francesco Holecz, Francesco Collivignarelli, Luca Gatti, Massimo Barbieri, Lorena Villano, Parvesh Chandna, Tri Setiyono
2014 Land Applications of Radar Remote Sensing  
We first briefly describe the rice environments of Bangladesh, and then demonstrate how a combination of hypertemporal synthetic-aperture radar (SAR) and optical RS data can be combined to generate both  ...  This is followed by the aus season (sometimes referred to as early kharif), which runs from March to Land Applications of Radar Remote Sensing Combining Moderate-Resolution Time-Series RS Data from SAR  ...  It was also funded by the CGIAR Global Rice Science Partnership (GRiSP) programme. We are grateful to the European Space Agency for access to the archive of ENVISAT ASAR data.  ... 
doi:10.5772/57443 fatcat:57d7mbdvevdl3chczdunkipjq4

Lowland Rice Mapping in Sédhiou Region (Senegal) Using Sentinel 1 and Sentinel 2 Data and Random Forest

Edoardo Fiorillo, Edmondo Di Giuseppe, Giacomo Fontanelli, Fabio Maselli
2020 Remote Sensing  
In this study, Sentinel 1 (S1) and Sentinel 2 (S2) imagery was used to map lowland rice crop areas in the Sédhiou region (Senegal) for the 2017, 2018, and 2019 growing seasons using the Random Forest (  ...  An example is finally provided that illustrates how the maps obtained can be combined with ground observations through a ratio estimator in order to yield a statistically sound prediction of rice area  ...  rice cropping system in Casamance and field sampling support.  ... 
doi:10.3390/rs12203403 fatcat:jw7w6z6t35fipigffbohxczwuu

Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data

Yaotong Cai, Xinyu Li, Meng Zhang, Hui Lin
2020 International Journal of Applied Earth Observation and Geoinformation  
This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data.  ...  Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification.  ...  Acknowledgments Our deepest gratitude goes to the editor and anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this manuscript.  ... 
doi:10.1016/j.jag.2020.102164 fatcat:ztjhrv4uyrbw3cdkyjb7co6e6u
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