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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Multi-Frequency Estimation of Canopy Penetration Depths from SMAP/AMSR2 Radiometer and Icesat Lidar Data
摘要： In this study, the ?? - ?? model framework is used to derive extinction coefficient and canopy penetration depths from multi-frequency SMAP and AMSR2 retrievals of vegetation optical depth together with ICESat LiDAR vegetation heights. The vegetation extinction coefficient serves as an indicator of how strong absorption and scattering processes within the canopy attenuate microwaves at L and C-band. Through inversion of the extinction coefficient, the penetration depth into the canopy can be obtained, which is analyzed on local (Sahel, Illinois) and continental scale (Africa, parts of North America) as well as for a one year time series (04/2015-04/2016). First analyses of the retrieved penetration depth estimates reveal strongest attenuation for densely forested areas, therefore vegetation attenuation should be accounted for when retrieving soil moisture in these areas. For the continents of North America and Africa penetration depths decrease in average with an increase in frequency from L- to C-band. Moreover penetration depth time series were found to match with expected seasonal variations (e.g. vegetation growth period & rainy season) for analyzed local regions.
关键词： ICESat,Vegetation attenuation,SMAP,AMSR2,canopy penetration,multi-sensor
更新于2025-09-23 15:21:01
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Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement Over Lakes and Reservoirs in China
摘要： Although the Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was primarily designed for glacier and sea-ice measurement, it can also be applied to monitor lake surface height (LSH). However, its performance in monitoring lakes/reservoirs has rarely been assessed. Here, we report an accuracy evaluation of the ICESat-2 laser altimetry data over 30 reservoirs in China using gauge data. To show its characteristics in large-scale lake monitoring, we also applied an advanced radar altimeter SARAL (Satellite for ARgos and ALtika) and the first laser altimeter ICESat (Ice, Cloud and land Elevation Satellite) to investigate all lakes and reservoirs (>10 km2) in China. We found that the ICESat-2 has a greatly improved altimetric capability, and the relative altimetric error was 0.06 m, while the relative altimetric error was 0.25 m for SARAL. Compared with SARAL and ICESat data, the ICESat-2 data had the lowest measurement uncertainty (the standard deviation of along-track heights; 0.02 m vs. 0.07 m and 0.17 m), the greatest temporal frequency (3.43 vs. 1.35 and 1.48 times per year), and the second greatest lake coverage (636 vs. 814 and 311 lakes). The precise altimetric profiles derived from the ICESat-2 data indicate that most lakes (90% of 636 lakes) are quasi-horizontal (measurement uncertainty <0.05 m), and special methods are needed for mountainous lakes or shallow lakes to extract precise LSHs.
关键词： Water level,Validation,ICESat-2,ICESat,SARAL,Lake and reservoir
更新于2025-09-16 10:30:52
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The Ice, Cloud, and Land Elevation Satellite – 2 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System
摘要： The Ice, Cloud, and land Elevation Satellite – 2 (ICESat-2) observatory was launched on 15 September 2018 to measure ice sheet and glacier elevation change, sea ice freeboard, and enable the determination of the heights of Earth's forests. ICESat-2's laser altimeter, the Advanced Topographic Laser Altimeter System (ATLAS) uses green (532 nm) laser light and single-photon sensitive detection to measure time of flight and subsequently surface height along each of its six beams. In this paper, we describe the major components of ATLAS, including the transmitter, the receiver and the components of the timing system. We present the major components of the ICESat-2 observatory, including the Global Positioning System, star trackers and inertial measurement unit. The ICESat-2 Level 1B data product (ATL02) provides the precise photon round-trip time of flight, among other data. The ICESat-2 Level 2A data product (ATL03) combines the photon times of flight with the observatory position and attitude to determine the geodetic location (i.e. the latitude, longitude and height) of the ground bounce point of photons detected by ATLAS. The ATL03 data product is used by higher-level (Level 3A) surface-specific data products to determine glacier and ice sheet height, sea ice freeboard, vegetation canopy height, ocean surface topography, and inland water body height.
关键词： Laser altimeter,Ice sheets,Altimetry,ICESat-2,Sea ice,Photon counting,Remote sensing,Cryosphere
更新于2025-09-16 10:30:52
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - The Comparison of Denoising Methods for Photon Counting Laser Altimeter Data
摘要： The space-borne earth observation LiDAR satellite Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) equipped with photon counting LiDAR system was launched on September 15, 2018. Significantly different from waveform laser altimeter carried on ICESat, the multiple beams photon counting laser altimeter can better measure Earth’ surface and directly provide the location of the scattering event. Due to the high sensitivity, the photon detector responds photons come from the solar background and the atmospheric scattering, which causes plenty of noise. It is necessary to study an effective method to identify signal photons from noise. This paper aims to study serval typical noise filtering methods and discuss their characteristics. The experiments are conducted on both ice sheet and vegetation data sets. The results are presented and some suggestions for choosing which denoising method to face in different environmental conditions are given.
关键词： ICESat-2,laser altimeter,noise filtering,MABEL,photon-counting
更新于2025-09-16 10:30:52
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Precision and Bias Comparison Between Laser and Radar Altimetry Data in the Amundsen Sea Embayment and the Lambert-Amery System of Antarctica
摘要： This article focuses on the precision and bias of laser altimetry data [Ice, Cloud, and Land Elevation Satellite (ICESat)] and radar altimetry data (Envisat) L2 products during the contemporary period from 2003 to 2008 in the Amundsen Sea Embayment (ASE) in West Antarctica (an ice loss region) and the Lambert-Amery System (LAS) in East Antarctica (an ice gain region). We used the crossover method to obtain the elevation differences between ICESat tracks, Envisat tracks, and between ICESat and Envisat tracks. The crossover points were generated and the difference of each crossover pair was calculated as raw data. The standard deviations were then computed from the raw data in a grid cell for both ICESat and Envisat. The precision of both satellites varied as a function of the surface slope in the abovementioned two regions, from 6.6 to 16.6 cm for the ICESat data and from 0.11 to 0.35 m for the Envisat data. The crossover points from ICESat-Envisat showed a mean bias of 0.55 ± 4.00 m for the ASE and 0.45 ± 0.99 m for the LAS, in accordance with the penetration depth of the radar altimetry. The relationship between the precision of the satellite measurements with the slope of the ice sheet and the ice velocity in the study area showed that the regions with gentle slopes and low velocity obtained a better precision of altimetry data.
关键词： Antarctica,Envisat,bias,precision,Ice, Cloud, and Land Elevation Satellite (ICESat)
更新于2025-09-12 10:27:22
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Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching
摘要： The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing on the pointing angle calibration of this new spaceborne laser altimeter, this paper proposes a fast pointing angle calibration method using only a small range of terrain surveyed by airborne lidar. Based on the matching criterion of least elevation difference, an iterative pointing angle calibration method was proposed. In the experiment, the simulated photon-counting laser altimeter data and the Ice, Cloud and Land Elevation Satellite-2 data were used to verify the algorithm. The results show that when 1 km and 2.5 km lengths of track were used, the pointing angle error after calibration could be reduced to about 0.3 arc-seconds and less than 0.1 arc-seconds, respectively. Meanwhile, compared with the traditional pyramid search method, the proposed iterative pointing angle calibration method does not require well-designed parameters, which are important in the pyramid search method to balance calculation time and calibration result, and the iterative pointing angle calibration method could significantly reduce the calibration time to only about one-fifth of that of the pyramid search method.
关键词： ICESat-2,photon-counting,pointing angle calibration,spaceborne laser altimeter,terrain matching
更新于2025-09-12 10:27:22
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The ATL08 land and vegetation product for the ICESat-2 Mission
摘要： After being launched in September 2018, measurements from the ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) will be available to the science community starting in the Spring of 2019. These data offer new possibilities for the mapping of global terrain and vegetation as well as monitoring of the Earth's carbon stocks. The Advanced Topographic Laser Altimeter System (ATLAS) instrument on-board ICESat-2 will utilize photon counting technology for the altimetry observations. Photon counting technology is relatively new to the vegetation mapping community thus requires development of new algorithm approaches for terrain surface and canopy height retrievals. Photon counting systems provide range measurements for individual photons given the instrument's detection sensitivity. The sensitivity enables higher laser repetition rates for improved spatial coverage but is susceptible to solar background noise making the separation of signal and noise challenging and the data volume large. The algorithm developed specifically for the extraction of terrain and canopy heights from the ATLAS point clouds produces the ATL08 geophysical data product. This paper provides a detailed description of the ATL08 methodology, presents the data format, discusses many of the critical parameters likely to be of interest to future ICESat-2 data users, and describes the predicated uncertainties for terrain and canopy heights using two simulated ATLAS data sets. The first critical function in the ATL08 algorithm needs to accurately retrieve the surface is to identify the signal photons apart from the noise photons. Using a series of iterative filters, the ground and top of canopy surfaces are then identified in the signal. Next, individual photons are classified as either noise, canopy, or ground photons based on their distance above (or below) the estimated ground and top of canopy surfaces. The ATL08 algorithm has been tested on several simulated ATLAS data sets, and the results from two different ecosystems are described. The terrain extraction results saw sub-meter RMSE for the Alaska Tundra/Taiga ecotone and < 2 m RMSE in Sonoma County, California which is characterized by complex topography and dense vegetation. Although canopy heights on the ATL08 data product will underestimate the true canopy height within a segment, the ICESat-2 derived canopy height is found to be correlated with relative height metrics produced from airborne lidar (i.e. truth). For the sparse boreal forests of Alaska, the ATL08 canopy height was most correlated with the 95th percentile relative height (RH95). However, for the dense coniferous forests of Sonoma County, CA, ATL08 canopy height is correlated with 75th percentile relative height (RH75). Data from ICESat-2 will provide a new and exciting data set to the scientific community by providing global terrain and canopy height estimates as well as showing potential for estimation of forest biomass.
关键词： Lidar,Vegetation,Terrain,Land,ICESat-2
更新于2025-09-09 09:28:46
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A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data
摘要： The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.
关键词： EMD,noise removal algorithm,photon-counting LiDAR,ICESat-2,photon classification,ground elevation,vegetation height
更新于2025-09-04 15:30:14