Predicting paddy yield at spatial scale using optical and Synthetic Aperture Radar (SAR) based satellite data in conjunction with field-based Crop Cutting Experiment (CCE) data

2020 ◽  
Vol 42 (6) ◽  
pp. 2046-2071
Author(s):  
Avinash Kumar Ranjan ◽  
Bikash Ranjan Parida
Keyword(s):  
Synthetic Aperture Radar ◽  
Spatial Scale ◽  
Satellite Data ◽  
Synthetic Aperture ◽  
Cutting Experiment ◽  
Paddy Yield ◽  
Aperture Radar

scholarly journals Integration of synthetic aperture radar and optical satellite data for corn biomass estimation

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 100857
Author(s):  
Mehdi Hosseini ◽  
Heather McNairn ◽  
Scott Mitchell ◽  
Laura Dingle Robertson ◽  
Andrew Davidson ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Satellite Data ◽  
Synthetic Aperture ◽  
Biomass Estimation ◽  
Optical Satellite Data ◽  
Aperture Radar

Neural networks for the inversion of soil surface parameters from synthetic aperture radar satellite data

2004 ◽  
Vol 31 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Mahmod Reza Sahebi ◽  
Ferdinand Bonn ◽  
Goze B Bénié
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Synthetic Aperture Radar ◽  
Satellite Data ◽  
Input Data ◽  
Soil Surface ◽  
Synthetic Aperture ◽  
Surface Parameters ◽  
Aperture Radar

This paper presents an application of neural networks to the extraction of bare soil surface parameters such as roughness and soil moisture content using synthetic aperture radar (SAR) satellite data. It uses a fast learning algorithm for training a multilayer feedforward neural network using the Kalman filter technique. Two different databases (theoretical and empirical) were used for the learning stage. Each database was configured as single and multiangular sets of input data (data acquired at two different incidence angles) that are compatible with data from one and two satellite images, respectively. All the configurations are trained and then evaluated using RADARSAT-1 and simulated data. The empirical (measured) database with the multiangular set of input data configuration had the best accuracy with a mean error of 1.54 cm for root mean square (rms) height of the surface roughness and 2.45 for soil dielectric constant in the study area. Based on these results the proposed approach was applied on RADARSAT-1 images from the Chateauguay watershed area (Quebec, Canada) and the final results are presented in the form of roughness and humidity maps.Key words: neural networks, Kalman filter, RADARSAT, SAR, soil roughness, soil moisture.

Synthetic aperture radar satellite data for offshore wind assessment: A strategic sampling approach

2011 ◽  
Vol 99 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Philippe Beaucage ◽  
Gaëtan Lafrance ◽  
Julie Lafrance ◽  
Julien Choisnard ◽  
Monique Bernier
Keyword(s):  
Synthetic Aperture Radar ◽  
Satellite Data ◽  
Offshore Wind ◽  
Synthetic Aperture ◽  
Radar Satellite ◽  
Sampling Approach ◽  
Aperture Radar

Synthetic aperture radar and optical satellite data for estimating the biomass of corn

2019 ◽  
Vol 83 ◽  
pp. 101933 ◽  
Author(s):  
Mehdi Hosseini ◽  
Heather McNairn ◽  
Scott Mitchell ◽  
Laura Dingle Robertson ◽  
Andrew Davidson ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Satellite Data ◽  
Synthetic Aperture ◽  
Optical Satellite Data ◽  
Aperture Radar
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