scholarly journals Assessing the Consistency of Estimated Ground Cover Fractions between the BLM AIM Method and Optical Remote Sensing Method

2021 ◽  
Author(s):  
Yuki Hamada ◽  
Mark Grippo
Keyword(s):  
Remote Sensing ◽  
Ground Cover ◽  
Optical Remote Sensing ◽  
Aim Method

scholarly journals Crop modelling and remote sensing for yield prediction

1995 ◽  
Vol 43 (2) ◽  
pp. 143-161 ◽  
Author(s):  
B.A.M. Bouman
Keyword(s):  
Remote Sensing ◽  
Growth Models ◽  
Remote Sensing Data ◽  
Ground Cover ◽  
Crop Growth ◽  
Model Parameters ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Forcing Function ◽  
Crop Growth Models

Methods for the application of crop growth models, remote sensing and their integrative use for yield forecasting and prediction are presented. First, the general principles of crop growth models are explained. When crop simulation models are used on regional scales, uncertainty and spatial variation in model parameters can result in broad bands of simulated yield. Remote sensing can be used to reduce some of this uncertainty. With optical remote sensing, standard relations between the Weighted Difference Vegetation Index and fraction ground cover and LAI were established for a number of crops. The radar backscatter of agricultural crops was found to be largely affected by canopy structure, and, for most crops, no consistent relationships with crop growth indicators were established. Two approaches are described to integrate remote sensing data with crop growth models. In the first one, measures of light interception (ground cover, LAI) estimated from optical remote sensing are used as forcing function in the models. In the second method, crop growth models are extended with remote sensing sub-models to simulate time-series of optical and radar remote sensing signals. These simulated signals are compared to measured signals, and the crop growth model is re-calibrated to match simulated with measured remote sensing data. The developed methods resulted in increased accuracy in the simulation of crop growth and yield of wheat and sugar beet in a number of case-studies.

Optical remote sensing of the ocean

1984 ◽  
Author(s):  
W. HOVIS
Keyword(s):  
Remote Sensing ◽  
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

Measurement and Mapping of Riverine Environments by Optical Remote Sensing

2011 ◽  
Author(s):  
Carl J. Legleiter
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing
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