scholarly journals The Influence of the Time Equation on Remote Sensing Data Interpretation

2015 ◽  
Vol XL-7/W3 ◽  
pp. 195-201
Author(s):  
B. Fichtelmann ◽  
E. Borg ◽  
E. Schwarz
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Zenith Distance ◽  
Acquisition Time ◽  
Observation Data ◽  
Sensing Data ◽  
Crossing Time ◽  
Equation Of Time

The interpretation of optical Earth observation data (remote sensing data from satellites) requires knowledge of the exact geographic position of each pixel as well as the exact local acquisition time. But these parameters are not available in each case. If a satellite has a sun-synchronous orbit, equator crossing time (ECT) can be used to determine the local crossing time (LCT) and its corresponding solar zenith distance. Relation between local equator crossing time (LECT) and LCT is given by orbit geometry. The calculation is based on ECT of satellite. The method of actual ECT determination for different satellites on basis of the two-line-elements (TLE), available for their full lifetime period and with help of orbit prediction package is well known. For land surface temperature (LST) studies mean solar conditions are commonly used in the relation between ECT given in Coordinated Universal Time (UTC) and LECT given in hours, thus neglecting the difference between mean and real Sun time (MST, RST). Its difference is described by the equation of time (ET). Of particular importance is the variation of LECT during the year within about ±15 minutes. This is in each case the variation of LECT of a satellite, including satellites with stable orbit as LANDSAT (L8 around 10:05 a.m.) or ENVISAT (around 10:00 a.m.). In case of NOAA satellites the variation of LECT is overlaid by a long-term orbital drift. Ignatov et al. (2004) developed a method to describe the drift-based variation of LECT that can be viewed as a formal mathematical approximation of a periodic function with one or two Fourier terms. But, nevertheless, ET is not included in actual studies of LST. Our paper aims to demonstrate the possible influence of equation of time on simple examples of data interpretation, e.g. NDVI.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Peng Liu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Observation Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Adversarial Networks ◽  
Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

Study on the Application and Comparative Analysis of Land Surface Temperature Retrieval Method Based on Multi-Sensor Remote Sensing Data

2014 ◽  
Vol 1010-1012 ◽  
pp. 1276-1279 ◽  
Author(s):  
Yin Tai Na
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Urban Heat ◽  
Split Window Algorithm ◽  
Temperature Retrieval

The three commonly used remote sensing land surface temperature retrieval methods are described, namely single-window algorithm, split window algorithm and multi-channel algorithm, which have their advantages and disadvantages. The land surface temperature (LST) of study area was retrieved with multi-source remote sensing data. LST of study area was retrieved with the split window algorithm on January 10, 2003 and November 19, 2003 which is comparatively analyzed with the LST result of ETM+data with the single-window algorithm and the LST result of ASTER data with multi channel algorithm in the same period. The results show that land surface temperature of different land features are significantly different, where the surface temperature of urban land is the highest, and that of rivers and lakes is the lowest, followed by woodland. It is concluded that the expansion of urban green space and protection of urban water can prevent or diminish the urban heat island.

scholarly journals Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

2020 ◽  
Vol 12 (16) ◽  
pp. 2660
Author(s):  
Philip Marzahn ◽  
Swen Meyer
Keyword(s):  
Remote Sensing ◽  
Soil Texture ◽  
Land Surface ◽  
Temporal Frequency ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Test Site ◽  
Soil Parameters ◽  
Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

scholarly journals A Novel Rayleigh Dynamical Model for Remote Sensing Data Interpretation

2020 ◽  
Vol 58 (7) ◽  
pp. 4989-4999
Author(s):  
Fabio M. Bayer ◽  
Debora M. Bayer ◽  
Andrea Marinoni ◽  
Paolo Gamba
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Dynamical Model ◽  
Sensing Data
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