Soil moisture (SM) plays a vital role in regulating the water and energy exchange between land surfaces and the atmosphere and is declared an essential climate variable by the Global Climate Observing System (GCOS). Vegetation optical depth (VOD) is a crucial parameter describing vegetation attenuation properties in microwave radiative transfer equation, and it has been proven to be a promising ecological indicator for studying plant hydraulics, carbon stocks, and vegetation phenology. A long-term SM and polarization-, frequency-dependent VODs (C/X/Ku) product was derived from the inter-calibrated AMSR-E/2 multi-frequency brightness temperature, using the multi-channel collaborative algorithm (MCCA). The MCCA comprehensively considers the physical relationship between multiple microwave channels and could simultaneously retrieve frequency- and polarization-dependent VODs and SM. The new MCCA AMSR-E/2 SM dataset was validated over 25 dense soil moisture networks from the International Soil Moisture Network (ISMN) and United States Department of Agriculture (USDA) watersheds. The results showed that MCCA performs best in terms of ubRMSE among the current publicly available SM datasets related to AMSR-E/2. In addition, polarization-, frequency-dependent VODs from MCCA may provide new insights for better understanding the water fluxes in plant physiology.
HU Lu, ZHAO Tianjie, JU Weimin , PENG Zhiqing , YAO Panpan, SHI Jiancheng
Normalized Difference Vegetation Index (NDVI) has been widely used for monitoring vegetation. This dataset employed all available Landsat 5/7/8 data on the Qinghai-Tibetan Plateau (QTP) (> 100,000 scenes), and reconstructed high spatiotemporal NDVI time-series data (30-m and 8-d) during 2000-2020 on the TP (QTP-NDVI30) by using the MODIS-Landsat fusion algorithm (gap filling and Savitzky–Golay filtering；GF-SG). For the details of GF-SG, please refer to Chen et al. (2021). This dataset has been evaluated carefully. The quantitative assessments show that the reconstructed NDVI images have an average MAE value of 0.02, correlation coefficient of 0.96, and SSIM value of 0.94. We compared the reconstructed images in some typical areas with the PlanetScope 3-m images and found that the spatial details were well preserved by QTP-NDVI30. The geographic coordinate system of this dataset is GCS_WGS_84. The spatial range covers the vegetation area of the QTP, which is defined as the areas with average NDVI during July- September larger than 0.15.
CAO Ruyin , XU Zichao , CHEN Yang , SHEN Miaogen , CHEN Jin
In this study，a vegetation classification system for the vegetation types in the Qinghai-Tibet Plateau was designed. The integrated classification method，taken into account of multi-source vegetation classification / land cover classification products， was used to produce the actual vegetation map. This integrated classification method followed the principle of data consistency，and the resultant vegetation map was superior over other vegetation maps in terms of reflection of current situation， classification system， and classification accuracy. This vegetation map is timely and could better reflect current vegetation distribution than earlier ones. This vegetation map could be conducive to fully extract vegetation information from multi-source data products with high reliability and consistency. Compared with previous data products，the overall accuracy （78.09%，kappa coefficient is 0.75） of this new vegetation map was found to increase by 18.84%-37.17%，especially for grassland and shrub.
ZHANG Hui, ZHAO Cenliang, ZHU Wenquan
Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.
ZHAO Tianjie, PENG Zhiqing , YAO Panpan, SHI Jiancheng
The normalized difference vegetation index (NDVI) can accurately reflect the surface vegetation coverage. At present, NDVI time series data based on spot / vegetation and MODIS satellite remote sensing images have been widely used in the research of vegetation dynamic change monitoring, land use / cover change detection, macro vegetation cover classification and net primary productivity estimation in various scale regions. The spatial distribution data set of 1km vegetation index (NDVI) in Southeast Tibet is in MODIS（ https://ladsweb.modaps.eosdis.nasa.gov/ ）Based on the 16 day 1km surface reflectance data (mod13), the monthly vegetation index data set since 2000 is generated by the maximum synthesis method. The data set effectively reflects the distribution and change of vegetation cover in Southeast Tibet on spatial and temporal scales. It has very important reference significance for the monitoring of vegetation change, the rational utilization of vegetation resources and other fields related to ecological environment. Monthly NDVI data is the maximum value of monthly NDVI data, and the data acquisition time is from February 2000 to December 2018. The downloaded data is in grid format with a spatial resolution of 1km.
WANG Hao
The vegetation type map was created by the random forest (RF) classification approach, based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. According to vegetation characteristics, four types include alpine swamp meadow (ASM), alpine meadow (AM), alpine steppe (AS), and alpine desert (AD) were classified in this map. Based on a spatial resolution of 30 m, the map can provide more detailed vegetation information.
ZHOU Defu, ZOU Defu, ZOU Defu, Zhao Lin, ZHAO Lin, Liu Guangyue, LIU Guangyue, Du Erji, DU Erji, LI Zhibin , LI Zhibin, Wu Tonghua, WU Xiaodong, CHEN Jie CHEN Jie
This data set is the spectral reflectance data of typical features in Ali during August to September in 2017, using ASD FieldSpec 4. The day of spectral data obtaining was sunny, we recorded the cloud condition during measuring. The white board was calibrated before measurement; The longitude and latitude coordinates are recorded by GPS. We measured the spectral reflectance data of different vegetation types and soil surrounding them. The DN value (.asd format) recorded by instrument can be read by ViewSpecPro, then converted into reflectance using EXCEL with the white board data. Spectral reflectance data is used to extract spectral characteristics of different vegetation types, vegetation classification, inversion of vegetation coverage and so on.
LIU Linshan, ZHANG Binghua
This vegetation water content data set is derived from the ground synchronous observation in the Luanhe River Basin soil moisture remote sensing experiment, including 55 sampled plots.The vegetation types involved in these sampled plots include grass, corn, potatoes, naked oats and carrots. The data measurement time is from September 13, 2018 to September 26, 2018.
ZHENG Xingming, JIANG Tao
Surface albedo is a critical parameter in land surface energy balance. This dataset provides the monthly land surface albedo of UAV remote sensing for typical ground stations in the middle reaches of Heihe river basin during the vegetation growth stage (June to October) in 2020 (The data of Huazhaizi station in August is not available because of technical problem). The algorithm for calculating albedo is an empirical method, which was developed based on a comprehensive forward simulation dataset based on 6S model and typical spectrums. This method can effectively transform the surface reflectance to the broadband surface albedo. The method was then applied to the surface reflectance acquired by UAV multi-spectral sensor and the broadband surface albedo with a 0.2-m spatial resolution was eventually obtained.
LIU Shaomin, ZHOU Ji, DONG Weishen
The seasonal synthetic satellite remote sensing image collection of the Qinghai-Tibet Plateau is obtained by de-clouding processing of the Sentinel-2 TOA time series product. It contains 4 bands (visible and near infrared bands), and has a spatial resolution of about 10 meters. Each year is divided into 4 quarters from January to March, April to June, July to September, and October to December, and the cloud mask of each image is obtained by synthesizing the visible band, cirrus band, aerosol band and NIR band information of Sentinel-2 data. Finally, the cloud-free satellite remote sensing images of the Tibetan Plateau region are obtained by synthesizes all the masked images in a quarter according to the median principle.
LONG Tengfei
This data set is hyperspectral observation data of typical vegetation along Sichuan Tibet Railway in September 2019, using the airborne spectrometer of Dajiang M600 resonon imaging system. Including the hyperspectral data observed in the grassland area of Lhasa in 2019, with its own latitude and longitude. The hyperspectral survey was mainly sunny. Before flight, whiteboard calibration was carried out; when data were collected, there was a target (that is, the standard reflective cloth suitable for the grass), which was used for spectral calibration; there were ground mark points (that is, letters with foam plates), and the longitude and latitude coordinates of each mark were recorded for geometric precise calibration. The DN value recorded by Hyperspectral camera of UAV can be converted into reflectivity by using Spectron Pro software. Hyperspectral data is used to extract spectral characteristics of different vegetation types, vegetation classification, inversion of vegetation coverage and so on.
ZHOU Guangsheng, JI Yuhe, LV Xiaomin, SONG Xingyang
Gwadar deep water port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and Red Sea in the West. It is a port with strategic position far away from Muscat, capital of Oman. This data is the land cover data of Gwadar and its surrounding areas. The data is from globeland30 with a spatial resolution of 30 meters and a data format of TIFF. The classification images used in the development of globeland30 data set mainly include Landsat's TM5, ETM +, oli multispectral images and HJ-1 multispectral images. Using the Pok based classification method, the total volume accuracy is 83.50%, and the kappa coefficient is 0.78.
WU Hua
The data set includes estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on the MODIS 16-day synthetic NDVI product (MOD13A2 collection 6). Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 2001 to 2014, and the spatial resolution is 1 km.
WANG Xufeng
The data set contains NPP products data produced by the maximum synthesis method of the three source regions of the Yellow River, the Yangtze River and the Lancang River. The data of remote sensing products MOD13Q1, MOD17A2, and MOD17A2H are available on the NASA website (http://modis.gsfc.nasa.gov/). The MOD13Q1 product is a 16-d synthetic product with a resolution of 250 m. The MOD17A2 and MOD17A2H product data are 8-d synthetic products, the resolution of MOD17A2 is 1 000 m, and the resolution of MOD17A2H is 500 m. The final synthetic NPP product of MODIS has a resolution of 1 km. The downloaded MOD13Q1, MOD17A2, and MOD17A2H remote sensing data products are in HDF format. The data have been processed by atmospheric correction, radiation correction, geometric correction, and cloud removal. 1) MRT projection conversion. Convert the format and projection of the downloaded data product, convert the HDF format to TIFF format, convert the projection to the UTM projection, and output NDVI with a resolution of 250 m, EVI with a resolution 250 m, and PSNnet with resolutions of 1 000 m and 500 m. 2) MVC maximum synthesis. Synthesize NDVI, EVI, and PSNnet synchronized with the ground measured data by the maximum value to obtain values corresponding to the measured data. The maximum synthesis method can effectively reduce the effects of clouds, the atmosphere, and solar elevation angles. 3) NPP annual value generated from the NASA-CASA model.
Kamel Didan*, Armando Barreto Munoz, Ramon Solano, Alfredo Huete
The data set includes the estimated data of the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on 10-day synthetic NDVI products from the SPOT satellite. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage is from 1999 to 2013, and the spatial resolution is 1 km.
WANG Xufeng
The data set includes the estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on GIMMS3g version 1.0, the latest version of the GIMMS NDVI data set. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 1982 to 2015, and the spatial resolution is 8 km.
WANG Xufeng
This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.
LI Fei, Fei Li, Zhijun Zhang
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
This dataset is the Fractional Vegetation Cover observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observations lasted for a vegetation growth cycle from May 2012 to September 2012 (UTC+8). Instruments and measurement method: Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. Details are described in the following: 0. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1. For row crop like corn, the plot is set to be 10×10 m2, and for the orchard, plot scale is 30×30 m2. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 2. The photographic method used depends on the species of vegetation and planting pattern: Low crops (<2 m) in rows in a situation with a small field of view (<30 ), rows of more than two cycles should be included in the field of view, and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. 3. High vegetation in rows (>2 m) Through the top-down photography of the low vegetation underneath the crown and the bottom-up photography beneath the tree crown, the FVC within the crown projection area can be obtained by weighting the FVC obtained from the two images. Next, the low vegetation between the trees is photographed, and the FVC that does not lie within the crown projection area is calculated. Finally, the average area of the tree crown is obtained using the tree crown projection method. The ratio of the crown projection area to the area outside the projection is calculated based on row spacing, and the FVC of the quadrat is obtained by weighting. 4. FVC extraction from the classification of digital images. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation.
MU Xihan, HUANG Shuai, MA Mingguo
The NDVI data set is the latest release of the long sequence (1981-2015) normalized difference vegetation index product of NOAA Global Inventory Monitoring and Modeling System (GIMMS), version number 3g.v1. The temporal resolution of the product is twice a month, while the spatial resolution is 1/12 of a degree. The temporal coverage is from July 1981 to December 2015. This product is a shared data product and can be downloaded directly from ecocast.arc.nasa.gov. For details, please refer to https://nex.nasa.gov/nex/projects/1349/.
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