Meter resolution image data set of Hanbantota port area (2018-2019)

The meter resolution remote sensing image data of hanbantota area is composed of data fusion and splicing of different satellites. Multispectral remote sensing images with resolution between 0.5 m and 1 m from 2018 to 2019 are selected, and cloud free data with similar time are selected, and the result data set is formed by cutting and splicing according to the research area. The spatial resolution of the data is about 0.6 meters. The data is mainly used to study the high-precision extraction of disaster bearing body elements, such as port facilities, roads and so on. The extracted thematic elements will be used as the basic data of storm surge exposure and vulnerability analysis.

0 2021-01-08

8 km resolution vegetation net primary productivity data set of the Tibetan Plateau (1990-2015)

Net primary productivity (NPP), as the basis of ecosystem material and energy cycle, can reflect the carbon sequestration capacity of vegetation at regional and global scales, and is an important indicator to evaluate the quality of terrestrial ecosystem. Based on the principle of light use efficiency model, the productivity model of ecosystem in national barrier area was established by coupling remote sensing, meteorology, vegetation and soil type data. In the selection of parameters, the photosynthetic effective radiation (APAR) was calculated from GIMMS NDVI 3gv1.0 data, vegetation map of China, total solar radiation and temperature and humidity data. Compared with the soil water molecular model, the regional evapotranspiration model can simplify the parameters and enhance the operability of the model. The net primary productivity (NPP) of terrestrial vegetation in 1990-2015 over the Qinghai Tibet Plateau was estimated based on the parameterized model with par and actual light use efficiency as input variables of CASA model.

0 2021-01-05

High-Temporal and Landsat-Like surface evapotranspiration in Heihe River Basin (2010-2016) (HiTLL ET V1.0)

This data set mainly includes daily surface evapotranspiration products in Heihe River Basin (HRB) from 2010 to 2016, with a resolution of 100 meters. Based on multi-source remote sensing data (MODIS Landsat TM/ETM+ data) and regional meteorological data (China meteorological forcing dataset, CMFD), sensitivity parameters of the theoretically robust surface energy balance system (SEBS) model were determined through global sensitivity analysis, and then the parameterization scheme of the model was optimized to improve the estimation accuracy. At the same time, combined with spatial and temporal data fusion algorithm of remote sensing image. Finally, the High-Temporal and Landsat-Like surface evapotranspiration (ET) (HiTLL ET) was obtained over the Heihe Basin. It was validation by the EC measurements from the flux observation stations and ETMap, and the estimation results are consistent with the observation and the spatial and temporal distribution pattern of ETMap. This data set can provide data support for the study of water consumption law and scientific effective management of watershed water resources within HRB, especially for woodland and grassland in the upper stream regions, oasis farmland and desert vegetation in the midstream and downstream regions.

0 2021-01-04

Leaf area index (LAI) dataset of Tibetan Plateau (1982-2015)

The data set is based on the Lai 3g calculated by GIMMS AVHRR sensor, which represents the greenness of vegetation. The data is from Chen et al. (2019), and the specific calculation method is shown in the article. The source data range is global, and Tibetan plateau region is selected in this data set. This data integrates the original semi monthly scale data into the monthly data, and the processing method is to take the maximum value of two periods of Lai in a month, so as to achieve the effect of removing noise as much as possible. This data set is one of the most widely used Lai data, and is often used to evaluate the temporal and spatial patterns of vegetation greenness, which has practical significance and theoretical value.

0 2020-12-22

Nukus irrigation area crop planting structure-2019

1) Data content: planting structure refers to the problem of planting proportion of crops in a region or country. Generally, grain crops are the main crop, supplemented by other economic crops. This data describes the spatial distribution of planting structure of irrigation area with 10m resolution. 2) Data sources and processing methods: sentinel data, random forest method. 3) Data quality description: kappa coefficient 80%. 4) Results and prospects of data application: basic data of various hydrological and ecological simulation analysis, fine calculation of agricultural evapotranspiration, agricultural water demand, infiltration and irrigation demand, and agricultural structure reaching the field level. In order to promote the healthy development of agricultural planting, it is particularly important to adjust and optimize various factors, and determine the role of each factor in the agricultural planting structure. 5) The planting structure is calculated on the GEE platform by using the random forest algorithm and the collected sample point data. In order to distinguish conveniently, in the calculation process, we use an Arabic number to represent each similar crop type. The calculated. TIF results are linked to the extracted cultivated land by the way of partition statistics. In this process, we use the words to represent the crop type The segment remains, i.e. the max field, and the crop category corresponding to each Arabic numeral is shown in the instruction document.

0 2020-12-17

Urganch - crop planting structure - 2019

1) Data content: planting structure refers to the problem of planting proportion of crops in a region or country. Generally, grain crops are the main crop, supplemented by other economic crops. This data describes the spatial distribution of planting structure of irrigation area with 10m resolution. 2) Data sources and processing methods: sentinel data, random forest method. 3) Data quality description: kappa coefficient 80%. 4) Results and prospects of data application: basic data of various hydrological and ecological simulation analysis, fine calculation of agricultural evapotranspiration, agricultural water demand, infiltration and irrigation demand, and agricultural structure reaching the field level. In order to promote the healthy development of agricultural planting, it is particularly important to adjust and optimize various factors, and determine the role of each factor in the agricultural planting structure. 5) The planting structure is calculated on the GEE platform by using the random forest algorithm and the collected sample point data. In order to distinguish conveniently, in the calculation process, we use an Arabic number to represent each similar crop type. The calculated. TIF results are linked to the extracted cultivated land by the way of partition statistics. In this process, we use the words to represent the crop type The segment remains, i.e. the max field, and the crop category corresponding to each Arabic numeral is shown in the instruction document.

0 2020-12-17

HiWATER: COSMO-SkyMed dataset (2012)

This dataset includes three scenes, covering the artificial oasis eco-hydrology experimental area of the Heihe River Basin, which were acquired on (yy-mm-dd hh:mm, BJT) 2012-07-25 07:12, 2012-07-28 19:55, 2012-08-02 07:12. The data were all acquired at PingPong mode with product level of SLC, and these three images are of VV/VH, HH/HV and VV/VH polarization, respectively. COSMO-SkyMed dataset was acquired from Italian Space Agency (ASI) “COSMO-SkyMed project 1720: HYDROCOSMO” (Courtesy: Prof. Shi Jiancheng from the State Key Laboratory of Remote Sensing Science of China).

0 2020-10-13

HiWATER: SPOT dataset (2012)

This dataset includes one scene acquired on (yy-mm-dd) 2012-09-06, covering the natural oasis eco-hydrology experimental area in the lower reaches of the Heihe River Basin. This datum contains panchromatic and multi-spectral bands, with spatial resolution of 2.5 m and 10 m, respectively. The data product level of this image is Level 1. QuickBird dataset was acquired through purchase.

0 2020-10-13

HiWATER:WorldView dataset

This dataset includes one scene acquired on (yy-mm-dd) 2012-05-12, covering the Pailugou catchment. This datum is of panchromatic bands, with spatial resolution of 0.5 m. The data product level of this image is L2. WorldView dataset was acquired through purchase.

0 2020-10-13

HiWATER: ZiYuan-3 (ZY-3) dataset

This dataset includes 44 scenes, covering the whole Heihe River Basin, which were acquired on (yy-mm-dd) 2012-08-25, 2012-09-03, 2012-09-08, 2012-09-13, 2012-09-18, 2012-09-23, 2012-09-28, 2012-10-03, 2012-10-13, 2012-10-18, 2012-10-22, 2012-11-01, 2012-11-11, 2012-11-21. The data are of multi-spectral bands with data product of Level 1. The spatial resolution is 1 m. ZY-3 dataset was acquired from purchase.

0 2020-10-13