The data sets of 2005-2007, heihe river middle reaches area of underground water level monitoring value, contains the shandan bridge, children's pawn, fountain, the king of the brake, big full, PCCW main canal, under the new ditch, Shi Gangdun, Ann, under the qin dynasty, the stockade, taiping fort, yue jia pfe, zhang ye, liao home fort, Yang's farm village, railway stations, three gates, tile kiln, xiejiawan, under the cliff, meteor smoke, oasis, xiguan, ShaJingZi, river hydrological station 3 years of monthly average water level.The data are from the hydrological yearbook. Due to the lack of data, the average water level data of some hydrological stations are missing.
HU Litang, XU Zongxue
This data set includes the 2015 observation data of 9 water net nodes in the 5.5km × 5.5km observation matrix (red box in the thumbnail) of Yingke / Daman irrigation area in the middle reaches of Heihe River. The nine nodes contain 4cm and 10cm two-layer hydro probe II probes to observe the main variables such as soil moisture, soil temperature, conductivity and complex permittivity; the si-111 infrared temperature probe is set up at 4m height to observe the surface radiation infrared temperature of the underlying surface. The observation time frequency is 5 minutes. This data set can provide spatiotemporal continuous observation data set for remote sensing estimation of key water and heat variables of heterogeneous surface, remote sensing authenticity test, ecological hydrology research, irrigation optimization management and other research.
KANG Jian, LI Xin, MA Mingguo
1. The data set is the soil water content data set of the upper reaches of Heihe River Basin, and the data is the measured data of location points from 2013 to 2014. 2. The infiltration data is measured with ech2o. Including 5 layers of soil moisture content and soil temperature 3. Some instruments lack of data due to insufficient battery life, broken roads, stolen instruments and other reasons
HE Chansheng
The dataset is the field soil measurement and analysis data of the upstream of Heihe River Basin from 2013 to 2014, including soil particle analysis, water characteristic curve, saturated water conductivity, soil porosity, infiltration analysis, and soil bulk density I. Soil particle analysis 1. The soil particle size data were measured in the particle size laboratory of the Key Laboratory of the Ministry of Education, West Ministry of Lanzhou University.The measuring instrument is Marvin laser particle size meter MS2000. 2. Particle size data were measured by laser particle size analyzer.As a result, sample points with large particles cannot be measured, such as D23 and D25 cannot be measured without data.Plus partial sample missing. Ii. Soil moisture characteristic curve 1. Centrifuge method: The unaltered soil of the ring-cutter collected in the field was put into the centrifuge, and the rotor weight of each time was measured with the rotation speed of 0, 310, 980, 1700, 2190, 2770, 3100, 5370, 6930, 8200 and 11600 respectively. 2. The ring cutter is numbered from 1 to the back according to the number. Since three groups are sampled at different places at the same time, in order to avoid repeated numbering, the first group is numbered from 1, the second group is numbered from 500, and the third group is numbered from 1000.It's consistent with the number of the sampling point.You can find the corresponding number in the two Excel. 3. The soil bulk density data in 2013 is supplementary to the sampling in 2012, so the data are not available at every point.At the same time, the soil layer of some sample points is not up to 70 cm thick, so the data of 5 layers cannot be taken. At the same time, a large part of data is missing due to transportation and recording problems.At the same time, only one layer of data is selected by random points. 4. Weight after drying: The drying weight of some samples was not measured due to problems with the oven during the experiment. 3. Saturated water conductivity of soil 1. Description of measurement method: The measurement method is based on the self-made instrument of Yiyanli (2009) for fixing water hair.The mariot bottle was used to keep the constant water head during the experiment.At the same time, the measured Ks was finally converted to the Ks value at 10℃ for analysis and calculation.Detailed measurement record table refer to saturation conductivity measurement description.K10℃ is the data of saturated water conductivity after conversion to 10℃.Unit: cm/min. 2. Data loss explanation: The data of saturated water conductivity is partly due to the lack of soil samples and the insufficient depth of the soil layer to obtain the data of the 4th or 5th layer 3. Sampling time: July 2014 4. Soil porosity 1. Use bulk density method to deduce: according to the relationship between soil bulk density and soil porosity. 2. The data in 2014 is supplementary to the sampling in 2012, so the data are not available at every point.At the same time, the soil layer of some sample points is not up to 70 cm thick, so the data of 5 layers cannot be taken. At the same time, a large part of data is missing due to transportation and recording problems.At the same time, only one layer of data is selected by random points. 5. Soil infiltration analysis 1. The infiltration data were measured by the "MINI DISK PORTABLE specific vector INFILTROMETER".The approximate saturation water conductivity under a certain negative pressure is obtained.The instrument is detailed in website: http://www.decagon.com/products/hydrology/hydraulic-conductivity/mini-disk-portable-tension-infiltrometer/ 2.D7 infiltration tests were not measured at that time because of rain. Vi. Soil bulk density 1. The bulk density of soil in 2014 refers to the undisturbed soil taken by ring cutter based on the basis of 2012. 2. The soil bulk density is dry soil bulk density, which is measured by drying method.The undisturbed ring-knife soil samples collected in the field were kept in an oven at 105℃ for 24 hours, and the dry weight of the soil was divided by the soil volume (100 cubic centimeters). 3. Unit: G /cm3
HE Chansheng
This data is the longitude and latitude information of soil water sampling points in the "observation experiment of Soil Hydrological heterogeneity in the upper reaches of Heihe River and its impact on the hydrological process in mountainous areas" (91125010) of Heihe project, which is mainly used to express the spatial distribution of soil water sampling points in this project.
HE Chansheng
The dataset includes the saturated hydraulic conductivity data of typical soil samples in Heihe River Basin from July 2012 to August 2013. The collection method of typical soil sample points in Heihe River Basin is representative sampling, which means that the typical soil types in the landscape area can be collected, and the sample points with higher representativeness can be collected as much as possible, and the saturated hydraulic conductivity of each type of soil can be measured three times for the average value.
ZHANG Ganlin,
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 fraction of absorbed photosynthetically active radiation data set of the Heihe River Basin provides the fraction of absorbed photosynthetically active radiation data products from 2013 to 2014. The fraction of absorbed photosynthetically active radiation is the the ratio of photosynthetically active radiation absorbed by the canopy that passes through the canopy and then reflected from the canopy during the passage of the canopy to total photosynthetically active radiation. It is determined by the physiological and ecological characteristics and structural characteristics of vegetation canopy. This data set algorithm is developed on the basis of the energy conservation-based FPAR inversion method, in order to reflect the different path and the absorption probability of direct radiation and scattered radiation in the canopy, a FPAR inversion model is developed, which can distinguish direct radiation from scattering radiation. The algorithm can invert the direct FPAR, scattered FPAR and total FPAR of the canopy of the vegetation. The RMSE obtained from the inversion between the instantaneous FPAR and the observed FPAR is 0.0289, and the R2 is 0.8419.
LI Li, ZHONG Bo, WU Junjun, WU Shanlong, XIN Xiaozhou
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product from 2011 to 2014. The data uses the characteristics of FY-3 data, a domestic satellite, with high time resolution (1 day) and spatial resolution (1km), to construct a multi angle observation data set, which is the basis for analyzing multi-source data sets and existing composite vegetation index products and algorithms On the basis of this, an algorithm system of global composite vegetation index production based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source 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 system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
The datasets of “Land Cover Map of Heihe River Basin” provide monthly land cover classification data in 2012-2013. The HJ-1/CCD data with both high spatial resolution (30 m) and high temporal (2 days) frequency was used to construct the time series data. The NDVI curves from the time series HJ-1/CCD data can depict the variation of typical land surface. Different land use type has different NDVI curve. Rules were set to extract every land use type information. The datasets of “Land Cover Map of Heihe River Basin” hold the traditional land use types including water bodies, urban and built-up, croplands, evergreen coniferous forests, deciduous broadleaf forests and so on. Crop type classification (including maize, spring wheat, highland barely, rape and so on), snow and ice and glaciers information updates, make the datasets more detailed. Compared with previous land cover map and other products, the classification result of the datasets is visually bette. Especially in middle stream, the accuracy of crop classification is quite high compared with the data from the ground campaign. The accuracy of land cover map of the datasets in 2012 was evaluated using very high spatial resolution remote sensing data within Google Earth and data from campaign, and the overall accuracy can be as high as 92.19%. In a word, the datasets of “Land Cover Map of Heihe River Basin” is not only high in overall accuracy, but also more detailed in crop fine classification. Furthermore, it updated some new classes like glaciers and snow. The datasets of “Land Cover Map of Heihe River Basin” are consequently the classification datasets with the highest accuracy and most detailed information up to now.
ZHONG Bo, YANG Aixia
The 30 m / month synthetic photosynthetic effective radiation absorption ratio (fAPAR) data set of Heihe River basin provides the monthly Lai synthetic 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, considering different vegetation types, based on land cover classification map, combined with 30 m /Monthly synthetic leaf area index (LAI) products were produced by fapar-p model based on energy conservation. Based on the principle of energy conservation, the algorithm considers the multiple bounces between vegetation, soil and vegetation, as well as the influence of various factors such as sky scattered light. By analyzing the process of the interaction between photons and canopy, from the point of view that the movement of photons in the canopy is equal to the probability of re collision when multiple scattering occurs, a uniform and continuous vegetation fAPAR model is established. In addition, the effects of various factors on the fAPAR model were analyzed, including soil and leaf reflectance, aggregation index, and G function. The algorithm is highly dynamic, and can get better results for different soil background, vegetation type, radiation conditions, light and observation geometry, weather conditions. Compared with the data of corn canopy par measurement in Yingke irrigation area of Zhangye City, Gansu Province on July 8, 2012, the 30 m / month fAPAR product has a high consistency with the ground observation data, and the error with the observation value is less than 5%. In a word, the 30 m / month synthetic photosynthetic effective radiation absorption ratio (fAPAR) data set of Heihe River Basin comprehensively uses the multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, and better serves the application of remote sensing data products.
FAN Wenjie, 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
一. Data overview This data interchange is the second data interchange of "genomics research on drought tolerance mechanism of typical desert plants in heihe basin", a key project of the major research program of "integrated research on eco-hydrological processes in heihe basin".The main research goal of this project is a typical desert sand Holly plants as materials, using the current international advanced a new generation of gene sequencing technology to the whole genome sequence and gene transcription of Holly group sequence decoding, so as to explore related to drought resistance gene and gene groups, and transgenic technology in model plants such as arabidopsis and rice) verify its drought resistance. 二, data content 1.Sequencing of the genome and transcriptome of lycophylla SPP. The genome size of Mongolian Holly was about 926 Mb, GC content 36.88%, repeat sequence proportion 66%, genome heterozygosity rate 0.56%, which indicated that the genome has many repeat sequences, high heterozygosity and belongs to a complex genome.Based on the predicted sequence results, we subsequently carried out in-depth sequencing of the genome of lysiopsis SPP. The obtained data were assembled to obtain a 937 Mb genome sequence (table 1), which was basically the same as the predicted genome size.Through to the sand Holly transcriptome sequencing and sequence assembly (table 2), received more than 77000 genes coding sequence (Unigene), these sequences are comments found that most of the gene sequence and legumes and soybean, garbanzo beans and bean has a higher similarity (figure 1), consistent with the fact of sand ilex leguminous plants. 一), and the sand Holly is a leguminous plants consistent with the fact. 2.Discovery of simple repeat sequence (SSR) molecular markers of sand Holly: There is a transcriptome data set of sand Holly in the network public database, and the sample collection site is zhongwei city, ningxia.But this is the location of the project team samples in minqin county, gansu province, in order to study whether this sand in different areas of the Holly sequence has sequence polymorphism, we first identify the minqin county plant samples in the genomes of simple sequence repeat (SSR) markers (table 3), and then, compares the transcriptome sequences of plant sample, found in part of SSR molecular marker polymorphism (table 4), these molecular markers could be used for the species of plant genetic map construction, QTL mapping and genetic diversity analysis in the study. 三, data processing instructions Sample collection place: minqin county, gansu province, latitude and longitude: N38 ° 34 '25.93 "E103 ° 08' 36.77".Genome sequencing: a total of 8 genomic DNA libraries of different sizes were constructed and determined by Illumina HiSeq 2500 instrument.Transcriptome sequencing: a library of 24 transcriptome mrnas was constructed and determined by Illumina HiSeq 4000. 四, the use of data and meaning We selected a typical desert plant as the research object, from the Angle of genomics, parse the desert plant genome and transcriptome sequences, excavated its precious drought-resistant gene resources, and to study their drought resistance mechanism of favorable sand Holly this ancient and important to the utilization of plant resources, as well as the heihe river basin of drought-resistant plant genetic breeding, ecological restoration and sustainable development.
HE Junxian, FENG Lei
According to the sample survey data, in August 2013, 30 forest plots were set up in the Tianlaochi watershed, with a plot size of 10 m×20 m. The long side of the plot was parallel to the slope of the hillside, including 26 blocks of Picea crassifolia forest. 2 blocks of Sabina Przewalsskii forest and 2 mixed forests of Picea and Sabina. In the plot, the diameter of the breast of each tree (the diameter of the trunk at a height of 1.3 m) is measured by a diameter tape, and the height of each tree and the height under the branches (the height of the first live branch at the lower end of the canopy) is measured by a hand-held ultrasonic altimeter. The north-south direction and the east-west crown width are measured with a tape measure, and the sample site is positioned by differential GPS. The parallel version of HASM-AD algorithm is used to simulate the classified LIDAR point cloud data. DEM is generated from ground points, DSM is generated from all points, and the height of surface features is obtained by differential operation between DSM and DEM. In forest area, it is called Canopy Height Model (CHM). A circular window with a given search radius is used to find the local maximum value on CHM. If the central pixel value is the maximum value, it is determined as the crown vertex. The pixel attribute value of the tree vertex is the tree height, and the spatial resolution is 1m.
YUE Tianxiang, WANG Yifu
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 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
Based on the downscaling temperature result data in the historical period of CMIP5 (Coupled Model Intercomparison Project Phase 5), the future multi-year average temperature in the three periods of 2011-2040, 2041-2070, and 2071-2100 was predicted. Under the scenarios of rcp2.6, rcp4.5, and rcp8.5, the method of combining ordinary least squares regression with HASM (High Accuracy Surface Modeling Method) was used to downscaling simulate and predict, and the 1km downscaling results of the multi-year average temperature in the three scenarios of 2011-2040, 2041-2070 and 2071-2100 were obtained.
YUE Tianxiang, ZHAO Na
This dataset includes soil moisture and soil temperature observations of 75 BNUNET nodes during the period from May to September 2012 (UTC+8), which is one type of WSN nodes in the Heihe eco-hydrological wireless sensor network (WSN). The BNUNET located in the observation matrix of the HiWATER artificial oasis eco-hydrology experimental area. Each BNUNET node observes the soil temperature at 4 cm, 10 cm and 20 cm depth, and soil moisture at 4 cm depth with 10 minutes interval. This dataset can be used in the estimation of surface hydrothermal variables and their validation, eco-hydrological research, irrigation management and so on. The detail description please refers to "Data introduction.docx".
Liu Jun, KOU Xiaokang, MA Mingguo
Based on the data of downscaling results in the precipitation historical period of CMIP5 (Coupled Model Intercomparison Project Phase 5), the combined Method of geographical weighted regression and HASM (High Accuracy Surface Modeling Method) was used to analyze the annual mean precipitation in the future three periods of 2011-2040, 2041-2070 and 2071-2100 in the scenario of rcp2.6, rcp4.5 and rcp8.5. Through downscaling simulation and prediction, the 1km downscaling results of the multi-year average precipitation in the three periods of 2011-2040, 2041-2070 and 2071-2100 are obtained.
YUE Tianxiang, ZHAO Na
The vegetation phenology data set of Heihe River basin provides remote sensing phenology products from 2012 to 2015. The spatial resolution is 1km and the projection type is sinusoidal. MODIS Lai product mod15a2 is used as the phenological remote sensing monitoring data source, and MODIS land cover classification product mcd12q1 is used as the auxiliary data set for extraction. The product algorithm first uses the time series data reconstruction method (bise method) to control the data quality of the input time series; then uses the main algorithm (logistic function fitting method) and the backup algorithm (piecewise linear fitting method) to extract the vegetation phenological parameters, realizes the complementary calculation method, guarantees the accuracy and improves the inversion rate. The algorithm can extract up to three growth cycles in a year, each growth cycle contains six data sets, including the start point of vegetation growth, the start point of growth peak, the end point of growth peak, the end point of growth, the fastest growth and the fastest decline. At the same time, it records the growth cycle type, growth season length, quality identification, etc., a total of 25 data sets. The phenology product reduces the missing rate of inversion, improves the stability of the product, and the data set is relatively reliable with rich information.
LI Jing
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