Biological productivity refers to the material production capacity of organisms and their groups or even larger scale (including ecosystem and biosphere). It changes with the environment. Therefore, it becomes an indicator of environmental change and the health of the earth system. Net primary productivity (NPP) of vegetation refers to the remaining part of total organic matter (GPP) produced by photosynthesis of green plants in unit time unit area after deducting autotrophic respiration (RA). The NPP products in Heihe River Basin mainly focus on the important parameters par and FPAR of the model of light energy utilization, and improve the algorithm and product production. The FPAR inversion model that distinguishes the direct radiation from the scattered radiation and the par inversion method based on the combination of static and polar orbit satellites are proposed. Finally, the net primary productivity data set of Heihe River Basin is produced by using the light utilization model. The algorithm improves the temporal and spatial resolution of data products, and the accuracy of products is also significantly improved.
LI Li, ZHONG Bo, WU Junjun, WU Shanlong, XIN Xiaozhou
On 25 August 2012, Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was utilized to obtain point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5200 m with the point cloud density 1 point per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
The NDVI data set is the sixth version of the MODIS Normalized Difference Vegetation Index product (2001-2016) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey (USGS EROS). The product has a temporal resolution of 16 days and a spatial resolution of 0.05 degrees. This version is a Climate Modeling Grid (CMG) data product generated from the original NDVI product (MYD13A2) with a resolution of 1 kilometer. Please indicate the source of these data as follows in acknowledgments: The MOD13C NDVI product was retrieved online courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, The [PRODUCT] was (were) retrieved from the online [TOOL], courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.
NASA
The 1 km / 5-day Lai data set of Heihe River basin provides the 5-day Lai synthesis results of 2010-2014. The data uses Terra / MODIS, Aqua / MODIS, as well as domestic satellites fy3a / MERSI and fy3b / MERSI sensor data to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
This dataset contains three basic remote sensing data of digital topography (DEM), TM remote sensing image and NDVI vegetation index of badan jilin desert. 1. DEM, digital terrain data, from the SRTM1 data set released by NASA in the United States, was cropped in the desert area.The resolution is 30 m.The data is stored in the DEM folder, and the dm.ovr file can be opened by ArcGIS. 2. TM image data.The composite data of Landsat TM/ETM + 543 band released by NASA were cropped in the desert lake group distribution area.The resolution is 30 m.From 1990 to 2010, one scene was selected in summer and one scene in autumn every five years to analyze the long-term changes of the lake.In 2002, there was a scene for each quarter to analyze the changes of the lake during the year.The data is stored in TM folder, TIFF format, can be opened by ArcGIS or ENVI software.The file naming rule is yyyymm.tif, where yyyy refers to the year and mm to the month. For example, 199009 refers to the time corresponding to the impact data of September 1990. 3. NDVI, vegetation index.The modis-ndvi product MOD13Q1, released by NASA, was cropped in desert areas.The NDVI data of every ten days of the growing season (June, July, August and September) from 2000 to 2012 are included. The spatial resolution is 250 m and the temporal resolution is 16 days.Stored in NDVI folder, TIFF format, can be opened by ArcGIS or ENVI software.Mosaic_tmp_yyyyddd.hdfout.250m_16_days_ndvi_roi.tif, Where yyyy represents the year and DDD represents the day of DDD of the year.
JIN Xiaomei, HU Xiaonong
30m month compositing Fraction Vegetation Cover (FVC) data set of Heihe River Basin provides the results of monthly FVC synthesis in 2011-2014. The data constructs multi-angle observation data sets by using China's domestic satellite HJ/CCD data with high temporal resolution (2 days after networking) and spatial resolution (30m) , and divides the country into different vegetation divisions and land types. The conversion coefficients of NDVI and FVC are calculated respectively, and use the calculated conversion coefficient lookup table and monthly compositing NDVI to produce the regional monthly compositing FVC products. The 30m month compositing FVC product in the Heihe River Basin can directly obtain the vegetation coverage ratio through high-resolution data, and mitigate the influence of low-resolution data heterogeneity; in addition, selecting the typical period of vegetation growth change, by fitting the vegetation index of each pixel time series to obtain the growth curve parameters that correspond to each pixel; then the land use map and the vegetation classification map are combined to find the representative uniform pixels of the region for training the conversion coefficients of the vegetation index. Compared with the ASTER reference FVC results, the 30m/month compositing FVC product in the Heihe River Basin is slightly higher than the ASTER reference result, but the overall deviation is not large, and the maximum value of the root mean square error (RMSE) of the product and the reference value is less than 0.175. In addition, compared with the ground survey data of Huailai experimental site in Hebei Province, the 30 m/month compositing FVC products generally reflect the seasonal variation of vegetation growth, and the deviation from the ground survey data is less than 0.1. At the same time, compared with the ground measurements of vegetation coverage in many watersheds in Northeast, North China and Southeast China, the overall error between the compositing FVC products and the ground measurements is less than 0.2. In all, the 30m/month compositing FVC data set of Heihe River Basin comprehensively utilizes multi-temporal and multi-angle remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products, so as to better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, WU Junjun, WU Shanlong, LIU Qinhuo
The purpose of differential GPS positioning survey is to unify multiple survey areas into the same coordinate system and realize accurate absolute positioning through joint survey with national high-level control point coordinates. Under the national geodetic coordinate system of 2000, the accurate positioning of flux observation matrix, hulugou small watershed, tianmuchi small watershed and dayokou watershed and target is completed. In order to realize the geometric correction and absolute positioning of optical image, SAR image and airborne lidar data, the layout of ground control points and high-precision measurement are completed. In the middle reaches of the area, one national high-level control point is jointly surveyed in the five directions of East, South, West, North and middle. Measuring instrument: There are 3 sets of triple R8 GNSS system. Measurement principle: For the control network encryption point, it is connected with the high-level known points in four quadrants around the survey area and distributed evenly in the survey area. For the ground control point (GCP), the obvious characteristic points (such as house corner, road intersection, inflection point, etc.) of the ground layout target and the independent ground objects are adopted and evenly distributed in the survey area. For the ground points with high accuracy requirements, the principle of average value of multiple (at least three) measurements is adopted. Measurement method: In the test area, the control network is encrypted, and GPS static measurement and national high-level control network are used for joint measurement and calculation. During measurement, multiple GPS receivers conduct static synchronous observation at different stations, and the observation time is strictly in accordance with the control network measurement specifications. The ground points in the test area are accurately located. GPS-RTK positioning technology is used and the national high-level control points are used to calibrate to the local coordinate system. When the mobile station obtains the fixed solution during the coordinate acquisition, the measurement is carried out again and the single measurement lasts for 5S. Measuring position: (1) Flux observation matrix 17 stations, Las tower, waternet, soilnet and bnunet nodes in the core area of flux observation matrix; ground control points in CASI flight area; ground corner reflector positions in radar coverage area; ground target positions in lidar flight area. (2) Hulugou small watershed Ground target location of lidar flight area. (3) Tianmuchi small watershed Ground target location of lidar flight area. (4) Dayokou Basin Satellite image geometric correction ground control point. Data format: GPS static survey, the original data format is ". Dat" and ". T01" (or ". T02") files (or converted renix data) and "field record". GPS-RTK survey, the original project is ". Job" file (or converted ". DC" file). The test results are submitted in the format of exported ". CSV" data, which can be viewed and edited by Excel software. Measurement time: June 19, 2012 to July 30, 2012
LIU Xiangfeng, MA Mingguo
The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
ZHANG Yu, ZHANG Guoqing
Near-surface atmospheric driving data prepared by ETMonitor and WRF models based on remote sensing surface evapotranspiration model were used to estimate the average surface evapotranspiration of the heihe river basin with a resolution of 250m in 8 days from may to September 2012.The coordinate system is the projection of equal latitude and longitude, and the spatial range is 96.5e -- 102.5e, 37.5n -- 43N.8 days data using synthetic way of storage, the data format for GEOTIFF, naming: 2012 ddd_evapotranspiration. Tif, including a DDD, ordinal number, for example 2012121 _evapotranspiration. Tif said 2012 day ordinal number is 121-128 days, the average surface evaporation unit is mm/d.The data type is single-precision floating point with an invalid value of -9.
JIA Li
250m/1km month compositing Fraction Vegetation Cover (FVC) data set of Heihe River Basin provides the results of monthly FVC synthesis in 2011-2014. The data is produced by using MODIS vegetation index products MOD13A2 and MOD13Q1 based on dimidiate pixel model.
ZHONG Bo, WU Junjun
The proportion data set of daily cloudless MODIS snow cover area in babaohe river basin (2008.1.1-2014.6.1) was obtained after cloud removal processing using a cloud removal algorithm based on cubic spline function interpolation on the basis of daily cloudless MODIS snow cover product-mod10a1 (tang zhiguang, 2013). This data set adopts the projection method of UTM (horizontal axis isometric cutting cylinder), with a spatial resolution of 500m, and provides Daily Snow Albedo daily-sad results for the babao river basin.The data set is a daily file from January 1, 2008 to June 1, 2014.Each file is the snow albedo result of the day, with a value of 0-100 (%), is the ENVI standard file, and the naming rule is: mod10a1.ayyyyddd_h25v05_snow_sad_grid_2d_reproj_babaohe_nocloud.img, where YYYY represents the year, DDD stands for Julian day (001-365/366).The file can be opened directly with ENVI or ARCMAP software. The original MODIS snow cover data products processed by declouding are derived from MOD10A1 products processed by the us national snow and ice data center (NSIDC). This data set is in HDF format and USES sinusoidal projection. The attributes of the cloud-free MODIS albedo data set (2008.1.1-2014.1.1) in babaohe river basin are composed of the spatial and temporal resolution, projection information and data format of the dataset.
WANG Jian, PAN Haizhu
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
ZHANG Na
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
ZHANG Na
The 30 m / month vegetation index (NDVI / EVI) data set of Heihe River basin provides the monthly NDVI / EVI composite products from 2011 to 2014. This data uses the characteristics of HJ / CCD data of China's domestic satellite, which has both high time resolution (2 days after Networking) and spatial resolution (30 m), to construct multi angle observation data set. The average composite MC method is used as the main algorithm for synthesis, and the backup algorithm uses VI method. At the same time, the main observation angles of the multi-source data set are used as part of the quality descriptor to help analyze the angle effect of the composite vegetation index residue. The remote sensing data acquired every month can provide more angles and more observations than the single day sensor data, but the quality of multi-phase and multi angle observation data is uneven due to the difference of on orbit operation time and performance of the sensor. Therefore, in order to effectively use the multi-temporal and multi angle observation data, before using the multi-source data set to synthesize the vegetation index, the algorithm designs the data quality inspection of the multi-source data set, removing the observation with large error and inconsistent observation. The verification results in the middle reaches of Heihe River show that the NDVI / EVI composite results of the combined multi temporal and multi angle observation data are in good agreement with the ground measured data (R2 = 0.89, RMSE = 0.092). In a word, the 30 m / month NDVI / EVI data set of Heihe River Basin comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, so as to realize the stable standardized products from scratch and better serve the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, WU Junjun, WU Shanlong
The 1 km / 5-day FVC data set of Heihe River basin provides the 5-day FVC synthesis results from 2011 to 2014. The data uses the data of Terra / MODIS, Aqua / MODIS, and domestic satellites fy3a / MERSI and fy3b / MERSI to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. The whole country is divided into different vegetation divisions and land types, and the conversion coefficient of NDVI and FVC is calculated respectively. The conversion coefficient look-up table and 1km / 5-day synthetic NDVI product production area 1km / 5-day synthetic FVC product are used. In the Heihe River Basin, 1 km / 5-day synthetic FVC products can directly obtain vegetation coverage ratio through high-resolution data to reduce the impact of low-resolution data heterogeneity; in addition, select the typical period of vegetation growth and change, obtain the corresponding growth curve parameters of each pixel by fitting the vegetation index of each pixel time series; and then cooperate with land use map and vegetation classification map, To find the representative uniform pixel of the region to train the conversion coefficient of vegetation index. Compared with the results of high-resolution aster reference FVC in Heihe River Basin, the first step is to aggregate the aster products in Heihe River basin to 1km scale by combining the measured ground data and using the scale up method, and to obtain the aster aggregate FVC data, which is based on spot vegetation remote sensing data released by geoland 2 project (geov1 for short) The results show that the results of geov1 are higher than those of ASTER image combined with ground measurement, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin are between the two, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin in the experimental area are better than those of geov1 products. In a word, the comprehensive utilization of multi-source remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products can better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, LIU Qinhuo
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Soil relative humidity index is one of the indicators to characterize soil drought and can directly reflect the status of crops' available water.
FAN Wenjie
The Sentinel-1A/B satellite uses a near-polar sun-synchronous orbit with an orbital altitude of 693 km, an orbital inclination of 98.18°, and an orbital period of 99 minutes. It is equipped with a C-band Synthetic Aperture Radar (SAR) with a designed service life of 7 years (12 years expected). Sentinel-l has a variety of imaging methods that enable different polarization modes such as single-polarization and dual-polarization. Sentinel-1A SAR has four working modes: Strip Map Mode (SM), Extra Wide Swath (EW), Interferometric Wide Swath (IW) and Wave Mode (WV). Satellite A was successfully launched in April 2014. The revisit period of the same region was 12 days. Satellite B successfully operated on orbit in April 2016. The current revisiting period reached 3 to 6 days. After the operation of two satellites, the S1 data acquisition frequency in the Antarctic region increased greatly. This data set comprises the Sentinel-1 SAR data for the Antarctic ice sheet and the Greenland Ice Sheet area. The data band comprises C-band extra wide multiview data with a resolution of 20 m*40 m. The temporal resolution is 12 days and is related to the round-trip period, the width is 400 km, the noise level is -25 dB, and the radiation measurement accuracy is 1.0 dB. The annual temporal coverage of these data is October to the next March in the Antarctic and April to September in Greenland, and the spatial coverage comprises the Antarctic ice sheet ice shelf area and Greenland ice sheet.
Lu Zhang
Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.
SONG Peilin, ZHANG Yongqiang
QuickBird satellite was launched by Digital Globe corporation on October 18, 2001. It has 4 multi-spectral bands and 1 panchromatic band, with a spatial resolution of 0.61m for panchromatic bands and a spatial resolution of 2.5m for multi-spectral bands and a width of 16.5 * 16.5 km. There are two QuickBird remote sensing images in heihe river basin.The acquisition time and coverage were: 2004-03-23, covering zhangye area;2004-08-08, covering danokou and drainage ditch drainage basin. The product level is level L2 and has been geometrically corrected by the system.
LI Xin, GUO Jianwen
The 30 m / month synthetic leaf area index (LAI) data set of Heihe River basin provides the monthly Lai synthetic products from 2011 to 2014. This data uses the domestic satellite HJ / CCD data with high time resolution (2 days after Networking) and spatial resolution (30 m) to construct the multi angle observation data set. Considering the impact of surface classification and terrain fluctuation, the algorithm is selected according to the characteristics of different vegetation types Choosing a suitable parameterization scheme of integrated model, inversion Lai based on look-up table method. The remote sensing data acquired every month can provide more angles and more observations than the single day sensor data, but the quality of multi-phase and multi angle observation data is uneven due to the difference of on orbit operation time and performance of the sensor. Therefore, in order to effectively use multi temporal and multi angle observation data, a data quality inspection scheme is designed. Using the Lai ground observation data of 9 forest quadrats, 20 farmland quadrats and 14 savanna quadrats from dayokou area in the upper reaches of Heihe River and Yingke and Linze areas in the middle reaches to verify the Lai in July, the inversion results are in good agreement with the measurement results, and the average error is less than 1; in addition, the Lai inversion results of the combined multi temporal and multi angle observation data are in good agreement with the ground measurement data （R2=0.9，RMSE=0.42）。 In a word, the 30 m / month synthetic leaf area index (LAI) data set of Heihe River Basin comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, so as to better serve the application of remote sensing data products.
LIU Qinhuo, FAN Wenjie, ZHONG Bo
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