CN106991412A - A kind of method for obtaining round-the-clock effective soil moisture - Google Patents

Abstract

The invention discloses a kind of method for obtaining round-the-clock effective soil moisture, comprise the following steps：Gather CLDAS meteorological datas；MODIS data are gathered, is pre-processed using ENVI softwares, is finally output as tiff forms；To MODIS as cloud differentiates, being divided into the cloudless pixels of MODIS and MODIS has cloud pixel, and carrying out the estimation cloudless pixels of MODIS and MODIS based on CLDAS meteorological datas and MODIS data has effective soil moisture of cloud pixel.This hair on the basis of the effective soil moisture of existing remote-sensing inversion, for have cloud pixel can not the normal effective soil moisture of inverting situation, the method for proposing to be estimated using meteorological data information obtains round-the-clock effective soil moisture data so as to realize.

Description

A method of obtaining available soil moisture around the clock

technical field

The invention belongs to the technical field of quantitative remote sensing, and in particular relates to a method for obtaining all-weather effective soil moisture.

Background technique

Effective soil moisture is the water in the soil that can be used by crops. It is very important for crop growth and is also an important indicator for drought monitoring. On a regional scale, optical and thermal infrared remote sensing techniques are usually used to obtain regional continuous effective soil moisture. However, when encountering unfavorable weather conditions such as rain clouds, optical and thermal infrared pixels may not be able to obtain effective observation values, resulting in the inversion of remote sensing methods to fail, so that it is impossible to obtain effective soil moisture around the clock. In this regard, the present invention fully taps the potential of meteorological data, uses the existing all-weather meteorological data product information to make up for the inconvenience caused by the lack of remote sensing data to the all-weather effective soil moisture inversion, and proposes a method for obtaining all-weather effective soil moisture. Production activities and crop growth provide reliable soil moisture information, which has important application value and practical significance.

Traditional regional effective soil moisture is generally obtained by optical and thermal infrared remote sensing technology, which can only be used under clear sky conditions. For non-clear sky pixels, it is generally impossible to obtain effective observation values, so it is impossible to retrieve all-weather effective soil moisture.

Contents of the invention

In view of this, the present invention provides a method for obtaining all-weather effective soil moisture for the problem that the non-clear sky pixel in the existing optical and thermal infrared remote sensing technology cannot directly obtain effective soil moisture, so as to realize all-weather effective soil moisture of acquisition.

In order to solve the above technical problems, the present invention discloses a method for obtaining all-weather effective soil moisture, comprising the following steps:

Step 1. Collect CLDAS meteorological data: CLDAS meteorological data include air temperature, wind speed, shortwave radiation and specific humidity;

Step 2, collect MODIS data, utilize ENVI software to carry out preprocessing, finally output is tiff format, as the input data of the present invention: MODIS data comprises the leaf area index product of surface temperature product, 8 days synthesis and 16 days synthesis normalized vegetation index products;

Step 3. Discriminate the MODIS image cloud, divide it into MODIS cloudless pixel and MODIS cloudy pixel, and estimate the effective soil moisture of MODIS cloudless pixel and MODIS cloudy pixel based on CLDAS meteorological data and MODIS data.

Furthermore, MODIS cloudless pixels and MODIS cloudy pixels are the attributes of MODIS product data, and this information can be obtained directly after downloading the data.

Further, the estimated effective soil moisture of MODIS cloud-free pixel in step 3 is specifically:

Calculate the four temperature values of dry bare soil temperature T _s,max , wet bare soil temperature T _s,min , full vegetation coverage temperature T _{c,max under water stress and full vegetation coverage temperature T c,min with} good water conditions, and calculate The formulas are:

Among them, Δ is the slope of the saturated water vapor pressure with temperature, expressed as a function of air temperature;

where T _a is the air temperature, read from the CLDAS data;

C _v is the specific heat of air, the value is 1295.16;

r _cx and r _cp are the maximum canopy impedance and the canopy impedance of potential evaporation respectively, the value of r _cx is 2000, r _cp is calculated according to LAI, r _cp =100/LAI, where LAI is read from MODIS data ;

γ is the psychrometer constant (value 0.066);

VPD is the saturated water vapor pressure difference, calculated using the air temperature and specific humidity data of CLDAS;

R _n is the net radiation, which is calculated by reading the downgoing shortwave radiation (S _d ), air temperature (T _a ) and albedo under four extreme conditions using CLDAS data; R _n = (1-α _s ) S _d +ε _s ε _a σT _a ⁴ -ε _s σT _a ⁴ Among them, α _s is the surface albedo, which is taken under the conditions of dry bare soil, wet bare soil, full vegetation coverage under water stress, and full vegetation coverage with good water are 0.20, 0.10, 0.25 and 0.15; ε _s is the specific emissivity of the surface, which is 0.96 and 0.97 for dry and wet bare soil conditions, and 0.975 and 0.985 for water-stressed and well-watered vegetation; σ is Stefan-Boltzmann constant, 5.67×10 ^-8 ; ε _a is the emissivity of the sky, expressed as a function of air temperature ε _a =9.2×10 ^-6 ×T _a ² ;

G is the soil heat flux, expressed as a function of R _n ; G=[0.05+0.265×(1-FVC)]R _n , where FVC is the vegetation coverage, using the normalized difference vegetation index (NDVI) product (MOD13A2 )calculate:

Among them, NDVI is directly read from the MODIS data, NDVI _max and NDVI _min correspond to the NDVI of full vegetation coverage and bare soil, respectively, and are obtained from MOD13A2 data, or according to empirical formulas, and are given as 0.86 and 0.2, respectively;

r _a is the aerodynamic roughness, the calculation formula is:

Among them, Z _0M and Z _0H are the roughness lengths of momentum transmission and energy transmission respectively, d is the zero plane displacement, z ₀ is the surface roughness length, u ₂ is the wind speed at 2m; if the vegetation height is h, then d=0.65h, z ₀ =0.1h, Z _0M =0.1h, Z _0H =0.1Z _0M ; the wind speed u ₂ at 2m is converted from the wind speed U at 10m from CLDAS data, and the calculation formula is:

For MODIS cloudless pixels, the calculation formula of effective soil moisture is:

where Ts is the data read directly from the MODIS temperature product.

Further, the estimated effective soil moisture of MODIS cloudy pixel in step 3 is specifically:

Calculate the ground impedance:

Among them, u _s is the wind speed at the height where the influence of surface roughness is the smallest. It is estimated by using the leaf area index, leaf diameter and vegetation height. Assuming that the vegetation height is h and the leaf diameter is s, then u _s is approximated as:

Among them, LAI is the leaf area index, read directly from the MODIS data;

The approximate calculation of the effective soil moisture M0 of the cloudy pixel in MODIS is _:

Compared with prior art, the present invention can obtain and comprise following technical effect:

1) On the basis of the existing remote sensing inversion of effective soil moisture, this paper proposes a method for estimating using meteorological data information for the situation that cloudy pixels cannot normally invert the effective soil moisture, which makes up for the simple use of remote sensing methods in effective soil moisture. The deficiency of effective soil moisture cannot be retrieved under cloud conditions.

2) The present invention integrates remote sensing and meteorological data information, can obtain spatially continuous all-weather effective soil moisture, and provides complete soil moisture data for regional research;

3) The present invention satisfies the requirement for the integrity of ground data information in the field of quantitative remote sensing.

Of course, implementing any product of the present invention does not necessarily need to achieve all the technical effects described above at the same time.

Description of drawings

The accompanying drawings described here are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute improper limitations to the present invention. In the attached picture:

Fig. 1 is the flowchart of the method that the present invention obtains all-weather effective soil moisture;

Fig. 2 is the MODIS surface temperature of Henan Province on September 20, 2015 of the present invention;

Fig. 3 is the Henan province MODIS normalized difference vegetation index (NDVI) on September 20, 2015 of the present invention;

Fig. 4 is the meteorological data of Henan Province on September 20, 2015 of the present invention, wherein, (a) is specific humidity, and (b) is wind speed;

Figure 5 shows the effective soil moisture in Henan Province on September 20, 2015, where (a) is the traditional remote sensing inversion method, and (b) is the method of the present invention.

detailed description

The implementation of the present invention will be described in detail below with examples, so as to fully understand and implement the implementation process of how the present invention uses technical means to solve technical problems and achieve technical effects.

The data required by the present invention to obtain all-weather effective soil moisture are the real-time meteorological data of the China Meteorological Administration Land Data Assimilation System (CLDAS) and the MODIS (Moderate Resolution Imaging Spectroradiometer) data of the United States, without other auxiliary data .

The present invention provides a method for obtaining all-weather effective soil moisture, as shown in Figure 1, comprising the following steps:

Step 1: CLDAS Meteorological Data:

Download the CLDAS meteorological element products from the China Meteorological Data Sharing Network, including temperature, wind speed, short-wave radiation and specific humidity. The download URL is: http://data.cma.cn/data/detail/dataCode/NAFP_CLDAS_RT.html. Without any preprocessing operation, it can be directly used as the input data of the present invention.

Step 2: MODIS Data

Download product data from the MODIS data website https://ladsweb.nascom.nasa.gov/search/, including the Land Surface Temperature (LST) product (MOD11A1), the 8-day synthetic Leaf Area Index (LAI) product (MOD15A2), and the 16-day Synthetic Normalized Difference Vegetation Index (NDVI) product (MOD13A2). Utilize ENVI software to carry out preprocessing, mainly include: projecting, splicing, cropping etc., finally output is tiff format, as the input data of the present invention.

Step 3: Estimate MODIS Pixel Effective Soil Moisture

Distinguishing MODIS image cloud is divided into MODIS cloudless pixel and MODIS cloudy pixel. MODIS cloudless image cloud generally has a normal pixel temperature value, and remote sensing data can be directly used to retrieve effective soil moisture; MODIS cloud image There is no pixel temperature value, so the effective soil moisture cannot be directly retrieved. This is an attribute of the MODIS product data. As long as the data is downloaded, this information can be obtained intuitively, without additional methods and processes for judgment; the present invention handles it according to different situations:

(1) Cloudless pixel

For MODIS cloudless pixels, first calculate four temperature values, namely: dry bare soil temperature T _s,max , wet bare soil temperature T _s,min , temperature of full vegetation coverage under water stress T _c,max and good water condition The full vegetation coverage temperature T _c,min , the calculation formulas are:

Among them, Δ is the slope of the saturated water vapor pressure changing with temperature, which can be expressed as a function of air temperature, Among them, T _a is the air temperature; C _v is the air specific heat (1295.16); r _cx and r _cp are the maximum canopy impedance and the canopy impedance of potential evaporation, respectively, r _cx can give a certain value (2000), r _cp can be calculated according to LAI (r _cp =100/LAI), where LAI can be read from MODIS data; γ is the psychrometer constant (value 0.066); T _a is air temperature, read from CLDAS data; VPD is the saturated water vapor pressure difference, which can be calculated by using the air temperature and specific humidity data of CLDAS; R _n is the net radiation, which can be read by using the CLDAS data to read the downgoing shortwave radiation (S _d ),

Air temperature (T _a ) and albedo under four extreme conditions are calculated, R _n = (1-α _s )S _d +ε _s ε _a σT _a ⁴ -ε _s σT _a ⁴ , where α _s is the surface albedo The values are 0.20, 0.10, 0.25 and 0.15 under the conditions of dry bare soil, wet bare soil, full vegetation coverage under water stress and full vegetation coverage with good water, respectively. ε _s is the surface specific emissivity, which is 0.96 and 0.97 for dry and wet bare soil conditions, and 0.975 and 0.985 for water-stressed whole vegetation and well-watered whole vegetation, respectively. σ is a Stefan-Boltzmann constant (5.67×10 ^-8 ). ε _a is the sky emissivity expressed as air temperature

The function of ε _a =9.2×10 ^-6 ×T _a ² . G is the soil heat flux, which can be expressed as a function of _Rn . G=[0.05+0.265×(1-FVC)]R _n , where FVC is the vegetation coverage, and the normalized vegetation can be used

Index (NDVI) product (MOD13A2) calculation:

Among them, NDVI can be read directly from MODIS data. NDVI _max and NDVI _min correspond to the NDVI of full vegetation coverage and bare soil, respectively, which can be obtained from MOD13A2 data, and can also be given as 0.86 and 0.2 according to empirical formulas.

r _a is the aerodynamic roughness, the calculation formula is:

Among them, _Z0M and _Z0H are the roughness lengths of momentum transfer and energy transfer respectively, d is the zero plane displacement, _z0 is the surface roughness length, and u2 is the wind speed at _2m . Assuming that the vegetation height is h, then d=0.65h, z ₀ =0.1h, Z _0M =0.1h, Z _0H =0.1Z _0M ; the wind speed u ₂ at 2m is converted from the wind speed U at 10m from CLDAS data, and the calculation formula is :

For MODIS cloudless pixels, the calculation formula of effective soil moisture is:

where Ts is the data read directly from the MODIS temperature product.

(2) There are cloud pixels

For cloudy pixels, the ground surface impedance is calculated first:

where u _s is the wind speed at the height at which the influence of surface roughness is minimal, which can be estimated using leaf area index, leaf diameter, and vegetation height. Assuming that the vegetation height is h and the leaf diameter is s, u _s can be approximated as:

Among them, LAI is the leaf area index, which can be read directly from MODIS data.

The effective soil moisture M0 of _MODIS cloudy pixel can be approximated as:

According to formula (8) and formula (11), the effective soil moisture of cloudy pixel and cloudless pixel can be calculated respectively, so as to realize the acquisition of effective soil moisture all-weather.

Example 1

Taking Henan Province as the research area, Figure 2 shows the MODIS surface temperature of Henan Province on September 20, 2015. It can be seen from the figure that the data in the northern, eastern and western parts of Henan Province are missing, showing a large number of blank areas; Figure 3 is Normalized Difference Vegetation Index. These two data are direct input data of the present invention. Fig. 4 is another set of direct input data of the present invention, namely CLDAS meteorological data.

Figure 5 is the inversion result of effective soil moisture. Fig. 5(a) is the inversion result of the traditional remote sensing method, and Fig. 5(b) is the method of the present invention. It can be seen from the comparison of the two figures that the traditional remote sensing inversion method cannot invert the effective soil moisture without effective MODIS pixel values, but the method of the present invention can make up for this shortcoming and realize the all-weather inversion of effective soil moisture and the inversion results show good spatial continuity.

The above description shows and describes several preferred embodiments of the invention, but as previously stated, it should be understood that the invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other embodiments. Combinations, modifications and circumstances, and can be modified within the scope of the inventive concept described herein, by the above teachings or by skill or knowledge in the relevant field. However, changes and changes made by those skilled in the art do not depart from the spirit and scope of the invention, and should be within the protection scope of the appended claims of the invention.

Claims (4)

1. a method for obtaining all-weather effective soil moisture, is characterized in that, comprises the following steps: Step 1. Collect CLDAS meteorological data: CLDAS meteorological data include air temperature, wind speed, shortwave radiation and specific humidity; Step 2, collect MODIS data, utilize ENVI software to carry out preprocessing, finally output is tiff format, as the input data of the present invention: MODIS data comprises the leaf area index product of surface temperature product, 8 days synthesis and 16 days synthesis normalized vegetation index products; Step 3. Discriminate the MODIS image cloud, divide it into MODIS cloudless pixel and MODIS cloudy pixel, and estimate the effective soil moisture of MODIS cloudless pixel and MODIS cloudy pixel based on CLDAS meteorological data and MODIS data. 2. the method for obtaining all-weather effective soil moisture according to claim 1, characterized in that, MODIS cloudless pixel and MODIS cloudy pixel are the attributes of MODIS product data, as long as the data is downloaded, the data can be directly obtained information. 3. the method for obtaining all-weather effective soil moisture according to claim 2, is characterized in that, the effective soil moisture of the estimation MODIS cloudless pixel in described step 3 is specifically: Calculate the four temperature values of dry bare soil temperature T _s,max , wet bare soil temperature T _s,min , full vegetation coverage temperature T _{c,max under water stress and full vegetation coverage temperature T c,min with} good water conditions, and calculate The formulas are: ${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ ${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; V</span>}\mathrm{<span\; class="notranslate">\; P</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ ${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&lsqb;</span>\frac{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; x</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; x</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; V</span>}\mathrm{<span\; class="notranslate">\; P</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; x</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$ ${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&lsqb;</span>\frac{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; V</span>}\mathrm{<span\; class="notranslate">\; P</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$ Among them, Δ is the slope of the saturated water vapor pressure with temperature, expressed as a function of air temperature; where T _a is the air temperature, read from the CLDAS data; C _v is the specific heat of air, the value is 1295.16; r _cx and r _cp are the maximum canopy impedance and the canopy impedance of potential evaporation respectively, the value of r _cx is 2000, r _cp is calculated according to LAI, r _cp =100/LAI, where LAI is read from MODIS data ; γ is the psychrometer constant (value 0.066); VPD is the saturated water vapor pressure difference, calculated using the air temperature and specific humidity data of CLDAS; R _n is the net radiation, which is calculated by reading the downgoing shortwave radiation (S _d ), air temperature (T _a ) and albedo under four extreme conditions using CLDAS data; R _n = (1-α _s ) S _d +ε _s ε _a σT _a ⁴ -ε _s σT _a ⁴ Among them, α _s is the surface albedo, which is taken under the conditions of dry bare soil, wet bare soil, full vegetation coverage under water stress, and full vegetation coverage with good water are 0.20, 0.10, 0.25 and 0.15; ε _s is the specific emissivity of the surface, which is 0.96 and 0.97 for dry and wet bare soil conditions, and 0.975 and 0.985 for water-stressed and well-watered vegetation; σ is Stefan-Boltzmann constant, 5.67×10 ^-8 ; ε _a is the emissivity of the sky, expressed as a function of air temperature ε _a =9.2×10 ^-6 ×T _a ² ; G is the soil heat flux, expressed as a function of R _n ; G=[0.05+0.265×(1-FVC)]R _n , where FVC is the vegetation coverage, using the normalized difference vegetation index (NDVI) product (MOD13A2 )calculate: $\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; V</span>}\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&lsqb;</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; V</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}{{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}<span\; class="notranslate">\; \&rsqb;</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ Among them, NDVI is directly read from the MODIS data, NDVI _max and NDVI _min correspond to the NDVI of full vegetation coverage and bare soil, respectively, and are obtained from MOD13A2 data, or according to empirical formulas, and are given as 0.86 and 0.2, respectively; r _a is the aerodynamic roughness, the calculation formula is: ${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; d</span>}}{{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; d</span>}}{{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; h</span>}}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; K</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$ Among them, Z _0M and Z _0H are the roughness lengths of momentum transmission and energy transmission respectively, d is the zero plane displacement, z ₀ is the surface roughness length, u ₂ is the wind speed at 2m; if the vegetation height is h, then d=0.65h, z ₀ =0.1h, Z _0M =0.1h, Z _0H =0.1Z _0M ; the wind speed u ₂ at 2m is converted from the wind speed U at 10m from CLDAS data, and the calculation formula is: ${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; d</span>}}{{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span>}{<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; ln</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; d</span>}}{{\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$ For MODIS cloudless pixels, the calculation formula of effective soil moisture is: ${\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; V</span>}\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{<span\; class="notranslate">\; \&lsqb;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; V</span>}\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; \&lsqb;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; V</span>}\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; \&rsqb;</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$ where Ts is the data read directly from the MODIS temperature product. 4. the method for obtaining all-weather effective soil moisture according to claim 3, is characterized in that, the effective soil moisture of the estimated MODIS cloud pixel in the described step 3 is specifically: Calculate the ground impedance: ${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 0.004</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.012</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$ Among them, u _s is the wind speed at the height where the influence of surface roughness is the smallest. It is estimated by using the leaf area index, leaf diameter and vegetation height. Assuming that the vegetation height is h and the leaf diameter is s, then u _s is approximated as: ${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; exp</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.28</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; LAI</span>}}^{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}^{<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.05</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \&rsqb;</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$ Among them, LAI is the leaf area index, read directly from the MODIS data; The approximate calculation of the effective soil moisture M0 of the cloudy pixel in MODIS is _: ${\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$