scholarly journals Estimation of the Effect of Soil Texture on Nitrate-Nitrogen Content in Groundwater Using Optical Remote Sensing

2011 ◽  
Vol 8 (8) ◽  
pp. 3416-3436 ◽  
Author(s):  
Yongyoot Witheetrirong ◽  
Nitin Kumar Tripathi ◽  
Taravudh Tipdecho ◽  
Preeda Parkpian
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Soil Texture ◽  
Nitrate Nitrogen ◽  
Optical Remote Sensing

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Design of global-contextual detection model for optical remote sensing targets

2020 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
ZHANG Rui-yan ◽  
◽  
JIANG Xiu-jie ◽  
AN Jun-she ◽  
CUI Tian-shu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Detection Model

Inverse Problems in Optical Remote Sensing of Coastal Waters

2006 ◽  
Author(s):  
Irina Dolina ◽  
Lev Dolin ◽  
Alexander Luchinin ◽  
Iosif Levin ◽  
Liza Levina
Keyword(s):  
Remote Sensing ◽  
Inverse Problems ◽  
Coastal Waters ◽  
Optical Remote Sensing

scholarly journals Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

2021 ◽  
Vol 11 (15) ◽  
pp. 6923
Author(s):  
Rui Zhang ◽  
Zhanzhong Tang ◽  
Dong Luo ◽  
Hongxia Luo ◽  
Shucheng You ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Identification Accuracy ◽  
Optical Remote Sensing ◽  
Sar Image ◽  
Single Time ◽  
Growing Period ◽  
Sensing Technology ◽  
Sar Imagery ◽  
Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

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