The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Yuanyuan Yang - One of the best experts on this subject based on the ideXlab platform.
-
Modeling of Thin-Cloud TOA Reflectance Using Empirical Relationships and Two Landsat-8 Visible Band Data
IEEE Transactions on Geoscience and Remote Sensing, 2019Co-Authors: Yong Wang, Yuanyuan YangAbstract:Clouds are a common barrier of satellite optical images and adversely affect applications of remotely sensed optical data sets. The optical thickness of clouds varies spatiotemporally. The thickness can be very thin making the detection of thin clouds difficult. A new cirrus Band (Band-9) of Landsat-8 has been added to detect thin clouds. However, the majority of spaceborne optical sensors existed previously or in operation do not have the cirrus Band. An algorithm is developed to detect thin clouds without using a cirrus Band. In particular, the top-of-atmosphere reflectance of thin clouds is modeled using the empirical relationships of the deep Blue and Blue Bands of Landsat-8 Operational Land Imager. A Landsat-8 image of path 14/row 36 near southeastern North Carolina, USA, is used to validate the algorithm. Thin clouds are well-identified when compared to Landsat-8 Band-9 data. The spatial correlation coefficient for both is 93.49%. Therefore, the algorithm is valid. The algorithm is further verified when a Blue Band and a green Band are used to develop the algorithm. Thus, the analytical approach should be extendible to Landsats 4, 5, and 7 sensors or optical sensors as long as they have a Blue Band and a green Band. Finally, the applicability of the algorithm under various atmospheric conditions is verified after analyzing two water vapor absorption spectral Bands of NASA/JPL Airborne Visible/Infrared Imaging Spectrometer data.
Diego Loyola - One of the best experts on this subject based on the ideXlab platform.
-
Total column water vapor retrieval for TROPOMI/S5P observations in the visible Blue Band
2020Co-Authors: Ka Lok Chan, Sander Slijkhuis, Pieter Valks, Claas Köhler, Diego LoyolaAbstract:<p>We present a new total column water vapor (TCWV) retrieval algorithm in the visible Blue Band for the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5P) satellite. Retrieving water vapor columns in the Blue Band has numerous advantages over longer wavelengths. Measurements in the Blue Band are more sensitive at lower troposphere over oceans due to higher surface albedo at this wavelength Band. In addition, no correction for spectral saturation effects is required as water vapor is optically thin in this spectral Band. The Blue Band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM).</p><p>The new algorithm is applied to TROPOMI observations to retrieve TCWV. Due to the long measurement record of GOME-2, the new algorithm is also used to retrieve TCWV from GOME-2. The TCWV data set is validated by comparing to the GOME-2 TCWV operational product retrieved in the red spectral Band, MODIS and SSMIS satellite observations. In addition, the new TCWV data set is also compared to ground based sun-photometer and radiosonde measurements. Water vapor columns retrieved in the Blue Band are in good agreement with the other data sets, indicating that the new algorithm derives precise results. Therefore, it was selected for the S5P Processor Algorithm Laboratory (PAL) project as a future operational product. This algorithm can also be used for the forthcoming Copernicus Sentinel S4 and S5 missions.</p>
-
Total column water vapor retrieval for GOME-2 visible Blue observations
2020Co-Authors: Ka Lok Chan, Sander Slijkhuis, Pieter Valks, Claas Köhler, Diego LoyolaAbstract:Abstract. We present a new total column water vapor (TCWV) retrieval algorithm in the visible Blue spectral Band for the Global Ozone Monitoring Experience 2 (GOME-2) instruments on board the EUMETSAT MetOp satellites. The Blue Band algorithm allows retrieval of water vapor from sensors which do not cover longer wavelengths, such as Ozone Monitoring Instrument (OMI) and the Copernicus atmospheric composition missions Sentinel-5 Precursor (S5P), Sentinel-4 (S4) and Sentinel-5 (S5). The Blue Band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM). The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. The new algorithm is applied to GOME-2A and GOME-2B observations to retrieve TCWV. The data set is validated by comparing to the operational product retrieved in the red spectral Band, sun-photometer and radiosonde measurements. Water vapor columns retrieved in the Blue Band are in good agreement with the other data sets, indicating that the new algorithm derives precise results, and can be used for the current and forthcoming Copernicus Sentinel missions S4 and S5.
Tomoharu Kato - One of the best experts on this subject based on the ideXlab platform.
-
Optical Band gap and photoluminescence studies in Blue-Band region of Zn-doped LiInS2 single crystals
Solid State Communications, 1994Co-Authors: Kazuo Kuriyama, Tomoharu KatoAbstract:The photoluminescence in Blue-Band region of Zn-doped LiInS2, which comprises the pseudowurtzite structure in an orthorhombic unit cell, is studied. The optical Band gap is found to be direct nature of 3.39 eV at 300 K. In comparison with undoped crystals, the Band-gap shrinkage of 160 meV arises from the expansion of the lattice due to Zn-doping. The visible-emission peak is shifted from 435 nm (2.85 eV) to 480 nm (2.58 eV) at temperatures ranging from 13 to 300 K. It is proposed that the origin of the bright Blue-Band emission is a multiple donor—valence-Band transition related to the S vacancy and antisite defects such as Zn on a Li site and In on a Li site.
-
Optical Band gap and Blue-Band emission of a LiInS2 single crystal.
Physical Review B, 1992Co-Authors: K. Kuriyama, Tomoharu Kato, Akihiro TakahashiAbstract:We report on the optical Band gap of LiInS 2 , which comprises the pseudowurtzite structure in an orthorhombic unit cell. The Band-structure nature of LiInS 2 is confirmed to be direct with a forbidden gap of 3.73 eV at 77 K. The fundamental absorption edge is shifted to the lower energy by 160 meV at 300 K. In the photoluminescence study at 13 K a Blue-Band emission is observed ataround 460 nm (2.70 eV), which has been assigned as a donor (sulfur vacancy or its complex) -valence-Band transition, but the Band-edge emission corresponding to the optical Band gap is not found
Tomoaki Miura - One of the best experts on this subject based on the ideXlab platform.
-
development of a two Band enhanced vegetation index without a Blue Band
Remote Sensing of Environment, 2008Co-Authors: Zhangyan Jiang, Alfredo Huete, Kamel Didan, Tomoaki MiuraAbstract:Abstract The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a Blue Band, in addition to the red and near-infrared Bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-Band EVI (EVI2), without a Blue Band, which has the best similarity with the 3-Band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor β is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, β and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within ± 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a Blue Band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied.
Zhangyan Jiang - One of the best experts on this subject based on the ideXlab platform.
-
development of a two Band enhanced vegetation index without a Blue Band
Remote Sensing of Environment, 2008Co-Authors: Zhangyan Jiang, Alfredo Huete, Kamel Didan, Tomoaki MiuraAbstract:Abstract The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a Blue Band, in addition to the red and near-infrared Bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-Band EVI (EVI2), without a Blue Band, which has the best similarity with the 3-Band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor β is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, β and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within ± 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a Blue Band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied.
-
2-Band enhanced vegetation index without a Blue Band and its application to AVHRR data
Remote Sensing and Modeling of Ecosystems for Sustainability IV, 2007Co-Authors: Zhangyan Jiang, Alfredo Huete, Youngwook Kim, Kamel DidanAbstract:The enhanced vegetation index (EVI) has been found useful in improving linearity with biophysical vegetation properties and in reducing saturation effects found in densely vegetated surfaces, commonly encountered in the normalized difference vegetation index (NDVI). However, EVI requires a Blue Band and is sensitive to variations in Blue Band reflectance, which limits consistency of EVI across different sensors. The objectives of this study are to develop a 2-Band EVI (EVI2) without a Blue Band that has the best similarity with the 3-Band EVI, and to investigate the crosssensor continuity of the EVI2 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). A linearity-adjustment factor (β) was introduced and coupled with the soil adjustment factor (L) used in the soil-adjusted vegetation index (SAVI) in the development of the EVI2 equation. The similarity between EVI and EVI2 was validated at the global scale. After a linear adjustment, the AVHRR EVI2 was found to be comparable with the MODIS EVI2. The good agreement between the AVHRR and MODIS EVI2 suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another longterm vegetation record different from the NDVI counterpart.