Temporal aliasing caused by the incomplete reduction of high frequency atmosphere and ocean variability contributes as a major error source in the time-variable gravity field products recovered from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO), and likely future gravity missions. The current state-of-the-art of satellite gravity data processing makes use of de-aliasing products to reduce high-frequency mass anomalies, for example, the most recent official Atmosphere and Ocean De-aliasing products (AOD1B-RL06) are applied to model non-tidal mass changes in the ocean and atmosphere. The products already achieved a temporal resolution of 3 hours that greatly improved the quality of gravity inversion compared to the previous releases. In this study, we explore a refined mass integration approach of the atmosphere that considers geometrical, physical, and numerical modifications of the current AOD1B method. Then, the newly available ERA-5 global climate data of 31 km spatial and 1-hour temporal resolution are used to produce a new set of non-tidal atmosphere de-aliasing product (HUST-ERA5) that is computed in terms of spherical harmonics up to degree/order 100 covering 2002 onwards. Despite of an overall agreement with the AOD1B-RL06 (correlation of low-degree coefficients are all greater than 0.99), discrepancy is still distinguished for spatial-temporal analysis, i.e., a better consistency of HUST-ERA5 from 2007 to 2010. The factors contributing the differences, including the input data, method and temporal resolution, are therefore respectively analyzed and quantified through extensive assessments. We find the difference of HUST-ERA5 and AOD1B-RL06 has led to a mean variation of 7.34 nm/s on the the LRI (Laser Ranging Interferometry) range-rate residual on Jan 2019, which is close to the LRI precision already. This impact is invisible for GRACE(-FO) gravity inversion because of the less accurate onboard KBR(K-band ranging) instrument, however, it will be nonnegligible and should be considered when the LRI completely replaces KBR in the future gravity mission. In addition, HUST-ERA5 can also be widely used in LEO satellite orbit determination and superconducting gravimeter atmospheric correction.
YANG Fan, LUO Zhicai
Based on GRACE Level-1b satellite gravity data, a time series of mass change over Greenland for the period 2002 to 2016, with a spatial resolution of 1 degree × 1 degree and a time resolution of one month was developed by the satellite gravity team led by Professor Shen Yunzhong from Tongji University. The reference time of this time series is the mean time span between January 2004 and December 2009. During data processing, ICE5G model was used to reduce the effect of GIA, and the contribution of GAD was added back by using AOD1B RL06 from GFZ
SHEN Yunzhong
The dataset of ground truth measurement synchronizing with the airborne microwave radiometers (L&K bands) mission was obtained in the Biandukou foci experimental area on May 25, 2008. Observation items included: (1) the soil temperature in L1, L2, L3, L4, L5, L6 and L7; (2) roughness measured by the roughness grid board and collected by the digital camera. Files with "result" field were processed data, in which the first row was RMS height (cm; one value), the second row was distance (cm), and the third row was correlation function (cm; changed into correlation length when it is 1/e). (3) GPR and TDR data. Five files were included, roughness photos and preprocessed data, the soil temperature, coordinates of quadrates and sampling lines, GPR and microwave radiometer data. All were archived as Excel and .txt files. Those provide reliable ground data for development and validation of soil moisture and freeze/thaw algorithms from active remote sensing approaches.
BAI Yunjie, CAO Yongpan, CHE Tao, DU Ziqiang, HAO Xiaohua, WANG Zhixia, WU Yueru, CHAI Yuan, CHANG Sheng, QIAN Yonggang, SUN Xiaoqing, WANG Jindi, YAO Dongping, ZHAO Shaojie, ZHENG Yue, ZHAO Yingshi, LI Xiaoyu, PATRICK Klenk, HUANG Bo, LI Shihua, LUO Zhen
The dataset of surface roughness measurements was obtained in A1, A2, A3, L1, L2, L3, L4, L5 and L6 of the A'rou foci experimental area. The quadrates were changed into 3×3 subsites during the foci experimental period, with each one spanning a 30×30 m2 plot. With the roughness plate 110cm long and the measuring points distance 1cm, the samples were collected along the strip from south to north and from east to west, respectively. As for the sampling lines, the samples were collected every 100 m along them from south to north. Photos were named in the form of A3-1EW, indicating No. 1 point in A3 measured from east to west. The coordinates of the sample would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The roughness data were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 radius is also included for further checking. Those provide reliable ground data for improving and verifying the remote sensing algorithms. Nine files were included, ARou_SampleArea1, ARou_SampleArea2, ARou_SampleArea3, ARou_SampleLine1, ARou_SampleLine2, ARou_SampleLine3, ARou_SampleLine4, ARou_SampleLine5 and ARou_SampleLine6.
CAO Yongpan, CHE Tao, HAN Xujun, LI Xin, LI Zhe, WANG Shuguo
The dataset of airborne L-band microwave radiometer and thermal imager mission was obtained in the Binggou-A'rou flight zone in the afternoon of Apr. 1, 2008. The frequency of L bands was 1.4 GHz with back sight of 35 degree and dual polarization (H&V) was acquired. The plane took off at Zhangye airport at 12:48 (BJT) and landed at 16:35 along the scheduled lines at the altitude about 5000m and speed about 260km/hr.. The raw data include microwave radiometer (L) data, thermal imager data (7.5-13 um; FOV: 24×18º) and GPS data; the first were instantaneous non-imaging observation recorded in text, which could be converted into brightness temperatures according to the caliberation coefficients (filed with raw data together), and the third are aircraft longitude, latitude and attitude. Moreover, based on the respective real-time clock log, observations by the microwave radiometer and GPS can be integrated to offer coordinates matching for the former. Yaw, flip, and pitch motions of aircraft were ignored due to the low resolution of microwave radiometer observations. Observation information can also be rasterized, as required, after calibration and coordinates matching. L band resolution (x) and footprint can be approximately estimated as x=0.3H (H is relative flight height). The thermal imager was 320*240 pixels and with FOV of 24×18º. The thermal imager data were stored in binary format with a text header file. The recorded value was brightness temperature at sensor with scale and gain parameter recorded in the header file. And the thermal images were not geometrically corrected because there were gaps between sequential images.
WANG Shuguo, WANG Xufeng, CHE Tao, ZHAO Kai, JIN Jinan, XIAO Qing, Liu Qiang
The dataset of surface roughness measurements was obtained in No. 1 and 2 quadrates of the Biandukou foci experimental area during the pre-observation period. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. The original photos of each sampling point, surface height standard deviation (cm) and correlation length (cm) were included. With the roughness grid board 110cm long and the measuring intervals of 1cm, the samples were collected along the soil surface from south to north and from east to west, respectively. The coordinates of sample points would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The roughness data files were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 needles is also included for further checking. Those provide reliable ground data for improving and verifying the microwave remote sensing algorithms.
CAO Yongpan, CHAO Zhenhua, CHE Tao, QIN Chun, WU Yueru
The dataset of ground truth measurement synchronizing with PROBA CHRIS was obtained in No. 2 and 3 quadrates of the A'rou foci experimental area on Jun. 23, 2008. Observation items included: (1) quadrates investigation including GPS by GARMIN GPS 76, plant species by manual cognition, the plant number by manual work, the height by the measuring tape repeated 4-5 times, phenology by manual work, the coverage by manual work (compartmentalizing 0.5m×0.5m into 100 to see the percentage the stellera takes) and the chlorophyll content by SPAD 502. Data were archived in Excel format. (2) roughness by the self-made roughness board and the camera. The processed data were archived as .txt files. (3) BRDF by ASD FieldSpec (350～2 500 nm), with 20% reference board and the observation platform made by Beijing Normal University. The processed reflectance and transmittivity were archived as .txt files. (4) LAI of stellera and pasture by the fisheye camera (CANON EOS40D with a lens of EF15/28), shooting straight downwards, with exceptions of higher plants, which were shot upwards. Data included original photos (.JPG) and those processed by can_eye5.0 (in Excel). For more details, see Readme file. Five files were included, spectrum in No.2 quadrate, multiangle observations in No.2 and 3 quadrates, roughness photos in No.2 and 3 quadrates, the fisheye camera observations, and the No.2 and 3 quadrates investigation.
CAO Yongpan, DING Songchuang, HAO Xiaohua, DONG Jian, Qu Yonghua, YU Yingjie
The dateset of the ground-based RPG-8CH-DP microwave radiometer observations was obtained in the Biandukou foci experimental area from Mar. 14 to 17, 2008. Observation items included the brightness temperature by the ground-based microwave radiometer (18.7GHz and 36.5GHz), the soil temperature by the thermal resistor, the gravimetric soil moisture by the microwave drying method, and the surface roughness by the grid board. The wheat stubble land (38°15'44.13"N, 100°55'35.34"E) was chosen for continuous observations from 11:00 to 24:00 on Mar. 14, with the incidence 20°-70° and the step length 5°. The rape stubble land (38°15'23.17"N, 100°58'37.84"E) was chosen for continuous observations from 10:00 to 21:30 on Mar. 16, with the incidence 20°-70° and the step length 5°. The deep plowed land (38°18'8.28"N, 101° 3'27.22"E) was chosen for short time observations from 17:26 to 19:20 on Mar. 17, with the azimuth angle 240°-300° and the step length 10°, the incidence 40°-70° and the step length 5°. The brightness temperature was archived as .BRT and .txt files (the ASCII format). Each row in .txt was listed by year, month, date, hour, minute, second, 6.925GHz (h), 6.925GHz (v), 10.65GHz (h), 10.65GHz (v) , 18.7GHz (h), 18.7GHz (v), 36.5GHz (h), 36.5GHz (v), the elevation angle, and the azimuth angle. Values for 6.925GHz and 10.65GHz were zero due to malfunction. The roughness data were obtained by the grid board and the camera and the RMS height (cm) and correlation length (cm) were also calculated and archived, which could be opened by Notepad or Microsoft Office Word. Those provide reliable reference for the roughness of the same land cover type. The gravimetric soil moisture (soil samples from 0-1cm, 1-3cm and 3-5cm) was measured by the microwave drying method. The file can be opened by Microsoft Office Word. The shallow layer soil moisture was measured by hydra prob from 12:00 to 17:00 on 14 and by the Hydra probe (straight downward for 0-5cm) and HH2 (level into the soil surface) on 16. The surface temperature was measured by the thermal resistor. The file can be opened by Microsoft Office Word. Four data files were included, the brightness temperature, the surface temperature, the soil moisture and the surface roughness.
CHANG Sheng, LIANG Xingtao, PAN Jinmei, PENG Danqing, ZHANG Yongpan, ZHANG Zhiyu, ZHAO Shaojie, Zhao Tianjie, ZHENG Yue, YIN Xiaojun, ZHANG Zhiyu
The dataset of surface roughness measurements by phototaking was obtained in the Huazhaizi desert steppe foci experimental area. Observation items included: (1) Surface roughness synchronizing with ASAR and MODIS in Huazhaizi desert No. 2 plot on May 24, 2008. (2) Surface roughness synchronizing with WiDAS in Huazhaizi desert No. 1 plot on May 30, 2008. The self-made roughness reference board (Cold and Arid Regions Environmental and Engineering Research Institute, CAS), the digital camera and the compass were used. Sample points were selected at equal intervals along the diagonals and marked in the photos.
XU Zhen, SHU Lele, WANG Jianhua
The dataset of surface roughness measurements was obtained in No. 1 and 2 quadrates of the E’bao foci experimental area during the pre-observation period. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. With the roughness board 110cm long and the measuring points distance 1cm, the samples were collected along the strip from south to north and from east to west, respectively. The coordinates of the sample would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be calculated based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The original photos of each sampling point, surface height standard deviation (cm) and correlation length (cm) were archived. The roughness data were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 needles is also included for further validation.
CAO Yongpan, CHAO Zhenhua, CHE Tao, QIN Chun, WU Yueru,
The dataset of surface roughness measurements was obtained in the reed plot A, the saline plots B and C of the Linze grassland foci experimental area on Jun. 7, 18 and 25, 2008. All the quadrates were divided into 4×4 subsites, with each one spanning a 120×120 m2 plot. With the roughness plate 110cm long and the measuring points distance 1cm, the samples were collected from south to north and from east to west, respectively. The coordinates of the sample would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The original photos of each sampling point, surface height standard deviation (cm) and correlation length (cm) were included this dataset. The roughness data were initialized with the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 needles is also included for further checking.
CAO Yongpan, GE Chunmei, WANG Shuguo, WANG Xufeng, WU Yueru, FENG Lei, YU Fan, WANG Jing
The dateset of surface roughness measurements was obtained in the Biandukou foci experimental area. With the roughness grid board 110cm long and the measuring intervals of 1cm, the samples were collected along the soil surface from south to north and from east to west, respectively. The coordinates of the sample would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The original photos of each sampling point, surface height standard deviation (cm) and correlation length (cm) were included. The roughness data files were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 needles is also included for further checking.
CAO Yongpan, WANG Jian, Wang Weizhen, WANG Xufeng, LIANG Xingtao, ZHANG Yongpan, Zhao Tianjie
The dataset of surface roughness was obtained at the super site (100m×100m, pure Qinghai spruce) around the Dayekou Guantan forest station. 25 corner points and 16 center points were collected and each point was measured twice and photos were taken. With the roughness plate 110cm long and the measuring points distance 1cm, the samples were collected along the strip from south to north and from east to west, respectively. The photos were processed using ArcView software; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The roughness data were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 radius is also included for further checking. Those provide reliable ground data for improving and verifying the remote sensing algorithms.
BAI Yunjie, CAO Yongpan, CHE Tao, CHEN Ling, Qu Yonghua, ZHOU Hongmin
The dataset of survey at the poplar sampling plot was obtained in the Linze station foci experimental area. Observation items included: (1) soil profile moisture and temperature (0-5cm, 0-5cm, 10-20cm, 20-40cm and 40-60cm) with photos measured twice by the cutting ring method (50cm^3, each layer), once by ML2X Soil Moisture Tachometer and the probe thermometer (15cm, twice each layer) on Jun. 3, 2008. Data were archived as Excel files. (2) shallow layer soil moisture (0-5cm) measured once by the cutting ring method (50cm^3, once each point) and twice by ML2X Soil Moisture Tachometer on Jun. 4, 2008. 13 points were selected and data were archived as Excel files. (3) LAI by TRAC on Jul. 20, 2008. Data were archived as Excel files. (4) roughness measured by the roughness plate together with the digital camera. 18 points were selected and data were archived in JPG format format. (5) forest investigation of Populus gansuensis from Jun. 5-13, 2008: coordinates, the diameter at breast height and the crown size by the measuring tape, full height by TruPulse200. 408 trees were selected and data were archived as Excel files. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.
BAI Yanfen, DING Songchuang, HAO Xiaohua, PAN Xiaoduo, Qian Jinbo, SONG Yi, WANG Yang, WANG Zhixia, ZHU Shijie
The data set is based on the geodetic coordinate data and other auxiliary data of the corner points of 16 subsamples of super sample plots, the setting points of lidar base station of the foundation and the base points of each tree trunk measured by the total station. The data acquisition time of total station is from June 3, 2008 to June 12, 2008, which is divided into two groups. One total station is used respectively, with the models of topcon602 and topcon7002. A total of 1468 Picea crassifolia trees in the super sample plot were measured, and all the corner points of the sub sample plot and the top points of the stake set on the base station of lidar were located. These positioning results are the main data content of the dataset. In addition, on June 3, 2008, June 4, 2008, June 6, 2011, the differential GPS z-max was used to locate all the stake vertices. By manually measuring the height of each stake, the height of the surface under the stake was calculated, and finally the three-dimensional coordinate position of the surface of each tree and the topographic map of super sample plot were generated. These data constitute the secondary data of the dataset. This data set can provide detailed ground observation data for the establishment of real three-dimensional forest scene, the development and correction of various three-dimensional forest remote sensing models, and ground validation data for the extraction of airborne lidar forest parameters.
GUO Zhifeng, LIANG Dashuang, WANG Qiang, ZHANG Hao, CHEN Erxue, LIU Qingwang
The super sample plot is composed of 16 sub samples. In order to locate each tree in the sample plot and facilitate the location of the base station point for ground-based radar observation, it is necessary to measure the geodetic coordinates of the sub sample plot corner point and the preset base station point for ground-based radar. The location of these points and each tree is measured by total station. Because the total station measures relative coordinates, in order to obtain geodetic coordinates, it is necessary to use differential GPS (DGPS) to measure at least one reference point around the super sample plot with high precision. In addition, we also use DGPS to observe the geodetic coordinates of all corner points of the subsample, and the measurement results can form the verification of the total station measurement results. The data set is based on all the positioning results measured by DGPS, excluding the positioning results of total station. The measurement time is from June 1 to 13, 2008, using the French Thales differential GPS measurement system, model z-max. The observation method is to use two GPS receivers for synchronous static measurement, one is the base station, which is set next to Gansu Water Conservation Forest Research Institute (the WGS geodetic coordinate of the base station is a first-class benchmark introduced from Zhangye City through multi station observation using z-max). The other is the mobile station, which is placed on the observation point of super sample plot. The observation time of each point varies from 10, 15, 20, 25, 30 minutes. The specific time depends on the satellite signal. The signal difference time is measured for several minutes more. Finally, the final positioning result is obtained by using the processing software of the instrument. WGS geodetic coordinate system is used for the positioning results. Firstly, six temporary control points were measured in the open area next to the super sample plot, providing reference points for the total station to measure the position of trees in the super sample plot. Then, flow stations were set up on each corner of 16 sub plots of super plot, and the coordinates of corner points were measured, and 41 observation points were obtained. The dataset stores the positioning results of these 47 points. This data is only for project use and not for external sharing.
LIU Qingwang, BAI Lina, CHEN Erxue
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