CSTR:11738.11.ncdc.ZDYF.db1685.2022
10.12072/ncdc.ZDYF.db1685.2022
Considering the current situation of vegetation, the regional soil moisture distribution is simulated by remote sensing inversion, and the vegetation carrying capacity of soil moisture in different regions is simulated based on the vegetation water consumption model.
Firstly, the study area is divided into 28987 vegetation response units (VRU) based on drought index, soil texture, vegetation type, slope and aspect. Secondly, the NOAA-AVHRR data set is downscaled by using the method of mathematical statistics. The 16 day maximum synthetic NDVI data set of NOAA-AVHRR from 1982 to 2006 and the 16 day maximum synthetic NDVI data set of modis-mod13a2 from 2000 to 2017 are fused to obtain the annual NDVI data set with spatial resolution of 1km from 1982 to 2017. The Mann Kendall (MK) trend test analysis method is used to test the trend of the vegetation data set from 1982 to 2017, and the area with no obvious change in vegetation in the study area is obtained as the vegetation stability area. By nesting vrus with stable natural vegetation areas, 14431 vrus are obtained, including 1720 VRU categories.
The parameters of bridge event and continuous hydrological (Beach) soil moisture model are further calibrated by using the measured data, and the model is improved to simulate the soil moisture content of four layers (0-10, 10-40, 40-100 and 100-200 cm) at the same time.
Taking the four layers of soil moisture simulated by the above soil moisture model as the independent variable and the vegetation coverage as the dependent variable, multiple linear regression analysis was carried out in each vegetation response unit. A total of 1720 multivariate linear fitting functions were obtained.
According to the fitting function between soil moisture and vegetation coverage obtained in the study, taking the soil moisture in recent 10 years (2008-2017) as the independent variable, calculate the vegetation coverage in this period, and obtain the vegetation coverage that the soil moisture can carry in this period as the soil moisture vegetation carrying capacity. The maximum, minimum and average values of each pixel are extracted respectively to represent the upper and lower limits of vegetation coverage that soil moisture can carry in this period and the current vegetation coverage under natural conditions.
<p> Considering the current situation of vegetation, the regional soil moisture distribution is simulated by remote sensing inversion, and the vegetation carrying capacity of soil moisture in different regions is simulated based on the vegetation water consumption model
The NOAA-AVHRR data set is downscaled by using the method of mathematical statistics. The 16 day maximum synthetic NDVI data set of NOAA-AVHRR from 1982 to 2006 and the 16 day maximum synthetic NDVI data set of modis-mod13a2 from 2000 to 2017 are fused to obtain the annual NDVI data set with spatial resolution of 1km from 1982 to 2017. The Mann Kendall (MK) trend test analysis method is used to test the trend of the vegetation data set from 1982 to 2017, and the area with no obvious change in vegetation in the study area is obtained as the vegetation stability area. By nesting vrus with stable natural vegetation areas, 14431 vrus are obtained, including 1720 VRU categories. The regional soil moisture distribution is simulated by remote sensing inversion, and the vegetation carrying capacity of soil moisture in different regions is simulated based on the vegetation water consumption model.
According to the fitting function of soil moisture and vegetation coverage obtained in the study, taking the soil moisture in recent 10 years (2008-2017) as the independent variable, calculate the vegetation coverage in this period, and obtain the vegetation coverage that the soil moisture can carry in this period as the soil moisture vegetation carrying capacity. The maximum value, minimum value and average value of each pixel are extracted respectively to represent the upper limit, lower limit and current vegetation coverage that soil moisture can carry in this period under natural conditions.
<p> Good data quality
| # | number | name | type |
| 1 | 2016YFC0500900 | National key R & D plan |
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CC BY 4.0 (Creative Commons Attribution 4.0 International License).
