Landsat and MODIS Data Fusion products based phenology analysis of dryland in Shan Dong province

2016 ◽  
Author(s):  
Xiyang Zhuang ◽  
Shuhe Zhao ◽  
Xian Li ◽  
Dianmin Cong
Keyword(s):  
Data Fusion ◽  
Modis Data ◽  
Fusion Products

Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology

2012 ◽  
Vol 117 ◽  
pp. 381-393 ◽  
Author(s):  
J.J. Walker ◽  
K.M. de Beurs ◽  
R.H. Wynne ◽  
F. Gao
Keyword(s):  
Data Fusion ◽  
Modis Data ◽  
Fusion Products

Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis

2013 ◽  
Vol 54 ◽  
pp. 249-258 ◽  
Author(s):  
Simon J. Buckley ◽  
Tobias H. Kurz ◽  
John A. Howell ◽  
Danilo Schneider
Keyword(s):  
Data Fusion ◽  
Hyperspectral Data ◽  
Terrestrial Lidar ◽  
Fusion Products

scholarly journals IASI-METOP and MIPAS-ENVISAT data fusion

2010 ◽  
Vol 10 (1) ◽  
pp. 183-211 ◽  
Author(s):  
S. Ceccherini ◽  
U. Cortesi ◽  
S. Del Bianco ◽  
P. Raspollini ◽  
B. Carli
Keyword(s):  
Data Fusion ◽  
Degrees Of Freedom ◽  
Solution Method ◽  
Information Gain ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information ◽  
Single Instrument ◽  
Fusion Products ◽  
Temporal Coverage

Abstract. The combination of data obtained with different sensors (data fusion) is a powerful technique that can provide target products of the best quality in terms of precision and accuracy, as well as spatial and temporal coverage and resolution. In this paper the results are presented of the data fusion of measurements of ozone vertical profile performed by two space-borne interferometers (IASI on METOP and MIPAS on ENVISAT) using the new measurement-space-solution method. With this method both the loss of information due to interpolation and the propagation of possible biases (caused by a priori information) are avoided. The data fusion products are characterized by means of retrieval errors, information gain, averaging kernels and number of degrees of freedom. The analysis is performed both on simulated and real measurements and the results demonstrate and quantify the improvement of data fusion products with respect to measurements of a single instrument.

An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery

2016 ◽  
Vol 31 ◽  
pp. 14-25 ◽  
Author(s):  
Mingquan Wu ◽  
Chaoyang Wu ◽  
Wenjiang Huang ◽  
Zheng Niu ◽  
Changyao Wang ◽  
...  
Keyword(s):  
Data Fusion ◽  
Landsat Imagery ◽  
Temporal Data ◽  
Modis Data ◽  
Fusion Approach

Soil Moisture Inversion Based on AMSR-E and MODIS Data Fusion: A Case Study of Huaihe River Basin

2014 ◽  
Vol 716-717 ◽  
pp. 1064-1067
Author(s):  
Jing Wen Xu ◽  
Yu Peng Wang ◽  
Jun Fang Zhao ◽  
Fei Yu Pu ◽  
Peng Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Fusion ◽  
River Basin ◽  
Surface Condition ◽  
Remote Sensing Data ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Sensing Data ◽  
Modis Data

In this paper, the correlation between fused data and original data, the measured soil and the precipitation data over Huaihe river basin by exploring the inversion of soil moisture from the time and space based on the method of multi-source remote sensing data fusion has been studied. In order to fuse the AMSR-E data which is all-day and all-weather and can penetrate the earth surface to some extent, with the MODIS data that can reflect the surface condition and temperature characteristics, the method of wavelet fusion was carried out in MATLAB. The conclusions of this study are listed as follows: (1) the inversion result of the fused data based on AMSE-E and MODIS is much better than a single remote sensing data inversion; (2) the fused data based on AMSE-E and MODIS is sensitive to soil moisture change trend when the seasons alternated every year, especially in the spring, summer and autumn.

A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion

2015 ◽  
Vol 156 ◽  
pp. 34-44 ◽  
Author(s):  
Caroline M. Gevaert ◽  
F. Javier García-Haro
Keyword(s):  
Data Fusion ◽  
Modis Data

scholarly journals IASI-METOP and MIPAS-ENVISAT data fusion

2010 ◽  
Vol 10 (10) ◽  
pp. 4689-4698 ◽  
Author(s):  
S. Ceccherini ◽  
U. Cortesi ◽  
S. Del Bianco ◽  
P. Raspollini ◽  
B. Carli
Keyword(s):  
Data Fusion ◽  
Degrees Of Freedom ◽  
Solution Method ◽  
Information Gain ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information ◽  
Single Instrument ◽  
Fusion Products ◽  
Temporal Coverage

Abstract. The combination of data obtained with different sensors (data fusion) is a powerful technique that can provide target products of the best quality in terms of precision and accuracy, as well as spatial and temporal coverage and resolution. In this paper the results are presented of the data fusion of measurements of ozone vertical profile performed by two space-borne interferometers (IASI on METOP and MIPAS on ENVISAT) using the new measurement-space-solution method. With this method both the loss of information due to interpolation and the propagation of possible biases (caused by a priori information) are avoided. The data fusion products are characterized by means of retrieval errors, information gain, averaging kernels and number of degrees of freedom. The analysis is performed both on simulated and real measurements and the results demonstrate and quantify the improvement of data fusion products with respect to measurements of a~single instrument.

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