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

Method for obtaining all-weather effective soil moisture

Technical Field

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

Background

Effective soil moisture is the moisture in the soil which can be utilized by crops, is very important for the growth of the crops, and is also an important index for drought monitoring. On a regional scale, optical and thermal infrared remote sensing technologies are generally used to acquire effective soil moisture continuously in a region. However, when adverse weather conditions such as rain, cloud and the like are met, the optical and thermal infrared pixels may not obtain effective observed values, so that inversion of the remote sensing method is invalid, and all-weather effective soil moisture cannot be obtained. Therefore, the method fully excavates the potential of meteorological data, utilizes the existing all-weather meteorological data product information to make up the inconvenience brought by remote sensing data loss to all-weather effective soil moisture inversion, provides a method for acquiring all-weather effective soil moisture, provides reliable soil moisture information for guiding agricultural production activities and crop growth, and has important application value and practical significance.

The traditional regional effective soil moisture is generally obtained by utilizing an optical and thermal infrared remote sensing technology and can only be used under a clear sky condition, and for a non-clear sky pixel, an effective observed value cannot be generally obtained, so that all-weather effective soil moisture cannot be inverted.

Disclosure of Invention

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

In order to solve the technical problem, the invention discloses a method for obtaining all-weather effective soil moisture, which comprises the following steps:

step 1, collecting CLDAS meteorological data: CLDAS meteorological data includes air temperature, wind speed, short wave radiation and specific humidity;

step 2, collecting MODIS data, preprocessing by utilizing ENVI software, and finally outputting in tiff format as input data of the invention: MODIS data includes surface temperature products, 8-day-synthesized leaf area index products, and 16-day-synthesized normalized vegetation index products;

and 3, judging the MODIS cloud, dividing the MODIS cloud image into an MODIS cloud-free image element and an MODIS cloud image element, and estimating the effective soil moisture of the MODIS cloud-free image element and the MODIS cloud image element based on CLDAS meteorological data and MODIS data.

Furthermore, the MODIS cloud-free pixel and the MODIS cloud pixel are the attributes of the MODIS product data, and the information can be intuitively obtained only after the data is downloaded.

Further, the step 3 of estimating the effective soil moisture of the MODIS cloud-pixel-free soil specifically comprises the following steps:

calculating the temperature T of the dry bare soil_s,maxTemperature T of wet bare soil_s,minWater stressedFull vegetation coverage temperature T_c,maxAnd a full vegetation coverage temperature T with good moisture status_c,minThe four temperature values are respectively calculated by the following formulas:

wherein Δ is a slope of a change in saturated vapor pressure with temperature, expressed as a function of air temperature;

wherein, T_aIs air temperature, read from CLDAS data;

C_vis the air specific heat, and takes the value of 1295.16;

r_cxand r_cpRespectively the maximum canopy impedance and the canopy impedance at potential evaporation, r_cxA value of 2000, r_cpCalculated according to LAI, r_cp100/LAI, wherein LAI is read from MODIS data;

gamma is the dry-wet table constant (value 0.066);

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

R_nis a net radiation, read with CLDAS dataLine short wave radiation (S)_d) Temperature (T)_a) Calculating the albedo under the four extreme conditions; r_n＝(1-α_s)S_d+_{s a}σT_a ⁴-_sσT_a ⁴Wherein, α_sThe earth surface albedo is 0.20, 0.10, 0.25 and 0.15 respectively under the conditions of dry bare soil, wet bare soil, full vegetation coverage stressed by water, covering and full vegetation coverage with good water;_sthe surface emissivity is 0.96 and 0.97 for dry and wet bare soil, 0.975 and 0.985 for water stressed and good vegetation, respectively, and the value sigma is the Stefan-Boltzmann constant, 5.67 × 10^-8；_aIs sky emissivity expressed as a function of air temperature_a＝9.2×10^-6×T_a ²；

G is the soil heat flux, denoted R_nG ═ 0.05+0.265 × (1-FVC)]R_nWherein, FVC is vegetation coverage, calculated using normalized vegetation index (NDVI) product (MOD13a 2):

wherein NDVI is directly read from MODIS data, NDVI_maxAnd NDVI_minNDVI respectively corresponding to the full vegetation cover and the bare soil is obtained from MOD13A2 data or obtained according to an empirical formula, and is respectively given as 0.86 and 0.2;

r_ais the aerodynamic roughness, and the calculation formula is:

wherein Z is_0MAnd Z_0HRespectively, the roughness lengths of momentum transfer and energy transfer, d is the zero plane displacement, z₀Is the surface roughness length, u₂Is 2m of windSpeed; if the vegetation height is h, d is 0.65h, z₀＝0.1h，Z_0M＝0.1h，Z_0H＝0.1Z_0M(ii) a Wind speed u at 2m₂The conversion is carried out by the wind speed U at 10m of the CLDAS data, and the calculation formula is as follows:

for MODIS cloud-free pixels, the calculation formula of the effective soil moisture is as follows:

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

Further, the step 3 of estimating the effective soil moisture of the MODIS with cloud pixels specifically comprises the following steps:

calculating the earth surface impedance:

wherein u is_sThe wind speed at the height with the least influence of the roughness of the earth surface is estimated by using the leaf area index, the leaf diameter and the vegetation height, and if the vegetation height is h and the leaf diameter is s, u is_sThe approximation is:

wherein, LAI is leaf area index, and is directly read from MODIS data;

MODIS cloud pixel effective soil moisture M₀The approximate calculation is:

compared with the prior art, the invention can obtain the following technical effects:

1) on the basis of the existing remote sensing effective soil moisture inversion, the method for estimating by using meteorological data information is provided aiming at the condition that the cloud pixel cannot normally invert the effective soil moisture, and the defect that the remote sensing method is used alone to invert the effective soil moisture under the cloud condition is overcome.

2) The method integrates remote sensing and meteorological data information, can acquire continuous all-weather effective soil moisture in space, and provides complete soil moisture data for regional research;

3) the invention meets the requirement of research in the field of quantitative remote sensing on the integrity of ground data information.

Of course, it is not necessary for any one product in which the invention is practiced to achieve all of the above-described technical effects simultaneously.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:

FIG. 1 is a flow chart of a method of the present invention for obtaining all-weather effective soil moisture;

FIG. 2 shows the MODIS surface temperature of Henan province, 9.20.2015;

FIG. 3 is the MODIS normalized vegetation index (NDVI) of Henan province, Henan, 9.month and 20.2015 of the present invention;

fig. 4 is the data of the present invention in the state of henan province at 9/20/2015, wherein (a) is specific humidity and (b) is wind speed;

fig. 5 shows the effective soil moisture in henan province at 9/20/2015, wherein (a) is a traditional remote sensing inversion method, and (b) is the method of the present invention.

Detailed Description

The following embodiments are described in detail with reference to the accompanying drawings, so that how to implement the technical features of the present invention to solve the technical problems and achieve the technical effects can be fully understood and implemented.

The Data required by the invention for acquiring all-weather effective soil moisture are China Meteorological Admission Land Data Assimilation System (CLDAS) real-time Meteorological Data and American MODIS (model Resolution Imaging Spectroscopeter) Data, and other auxiliary Data is not required.

The invention provides a method for obtaining all-weather effective soil moisture, which comprises the following steps as shown in figure 1:

step 1: CLDAS meteorological data:

downloading CLDAS meteorological element products including air temperature, wind speed, short wave radiation and specific humidity from a China meteorological data sharing network, wherein the downloading website is as follows: http:// data. cma. cn/data/detail/dataCode/NAFP _ CLDAS _ rt. html. The data can be directly used as input data of the invention without any preprocessing operation.

Step 2: MODIS data

Product data including surface temperature (LST) product (MOD11A1), Leaf Area Index (LAI) product synthesized for 8 days (MOD15A2), and normalized vegetation index (NDVI) product synthesized for 16 days (MOD13A2) were downloaded from MODIS data website https:// lads web. The method comprises the following steps of preprocessing by utilizing ENVI software, and mainly comprises the following steps: projection, splicing, clipping and the like, and finally outputting the data in a tiff format as input data of the invention.

And step 3: estimation of MODIS Pixel effective soil moisture

The method comprises the following steps of judging the MODIS cloud, wherein the MODIS cloud is divided into an MODIS cloud-free pixel and an MODIS cloud pixel, the MODIS cloud-free cloud generally has a normal pixel temperature value, and the remote sensing data can be directly utilized for inverting the effective soil moisture; and the MODIS has no pixel temperature value if the MODIS has a cloud pixel, so that the MODIS cannot directly invert the effective soil moisture. The data is the self-contained attribute of the MODIS product data, the information can be intuitively obtained only after the data is downloaded, and no additional method or process is needed for judgment; the invention processes according to different situations:

(1) non-cloud pixel

For a MODIS cloud-free pixel, four temperature values are first calculated, namely: temperature T of dry bare soil_s,maxTemperature T of wet bare soil_s,minFull vegetation cover temperature T under water stress_c,maxAnd a full vegetation coverage temperature T with good moisture status_c,minThe calculation formulas are respectively as follows:

where Δ is the slope of saturated water vapor pressure as a function of temperature, and may be expressed as a function of air temperature,wherein, T_aIs the air temperature; c_vIs the air specific heat (value 1295.16); r is_cxAnd r_cpRespectively the maximum canopy impedance and the canopy impedance at potential evaporation, r_cxCan give a certain value (2000), r_cpCan be calculated from the LAI (r)_cp100/LAI), wherein the LAI can be read from MODIS data; gamma is the dry-wet table constant (value 0.066); t is_aIs air temperature, read from CLDAS data; VPD is the saturated vapor pressure difference and can be calculated by using the air temperature and specific humidity data of CLDAS; r_nIs a net radiation, using CLDAS data to read the downlink short wave radiation (S)_d)、

Air temperature (T)_a) And calculating albedo under four extreme conditions, R_n＝(1-α_s)S_d+_{s a}σT_a ⁴-_sσT_a ⁴Wherein, α_sIs the earth surface albedo, and 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 stressed by water and full vegetation coverage with good water respectively._sFor surface emissivity, dry and wet bare soil conditions were 0.96 and 0.97 respectively, for water stressed and good vegetation 0.975 and 0.985 respectively, σ is the Stefan-Boltzmann constant (5.67 × 10)^-8)。_aIs sky emissivity, expressed as air temperature

Function of (2)_a＝9.2×10^-6×T_a ². G is the soil heat flux and can be expressed as R_nG ═ 0.05+0.265 × (1-FVC)]R_nWhere FVC is vegetation coverage, normalized vegetation may be utilized

Index (NDVI) product (MOD13a2) calculation:

the NDVI can be directly read from the MODIS data, and the NDVI_maxAnd NDVI_minNDVI corresponding to the whole vegetation cover and bare soil, respectively, canObtained from the MOD13A2 data, which can also be given as 0.86 and 0.2, respectively, based on empirical formulas.

r_aIs the aerodynamic roughness, and the calculation formula is:

wherein,_Z0Mand_Z0Hrespectively, the roughness lengths of momentum transfer and energy transfer, d is the zero plane displacement, z₀Is the surface roughness length, u₂Is the wind speed at 2 m. Assuming the vegetation height is h, d is 0.65h, z₀＝0.1h，Z_0M＝0.1h，Z_0H＝0.1Z_0M(ii) a Wind speed u at 2m₂The conversion is carried out by the wind speed U at 10m of the CLDAS data, and the calculation formula is as follows:

for MODIS cloud-free pixels, the calculation formula of the effective soil moisture is as follows:

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

(2) Cloud picture element

For a cloud pixel, the surface impedance is first calculated:

wherein u is_sIs the wind speed at the height where the influence of the roughness of the earth's surface is minimal and can be determined by the leaf area index, leaf diameter and vegetation heightAnd (6) estimating. Assuming a vegetation height of h and a leaf diameter of s, u_sCan be approximated as:

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

MODIS cloud pixel effective soil moisture M₀Can be approximately calculated as:

according to the formula (8) and the formula (11), the effective soil moisture with cloud pixels and the effective soil moisture without cloud pixels can be calculated respectively, so that all-weather effective soil moisture can be obtained.

Example 1

Taking Henan province as a research area, and fig. 2 shows the surface temperature of MODIS in Henan province on 20 th of 9 th of 2015, wherein data of northern regions, eastern regions and western regions of Henan province are missing and a large number of blank regions are shown; fig. 3 is a normalized vegetation index. These two data are the direct input data of the present invention. FIG. 4 is another set of direct input data of the present invention, CLDAS meteorological data.

Fig. 5 is the result of the effective soil moisture inversion. Fig. 5(a) is the result of inversion by a conventional remote sensing method, and fig. 5(b) is the method of the present invention. It can be seen from the comparison of the two graphs that the traditional remote sensing inversion method cannot invert the effective soil moisture under the condition of no effective MODIS pixel value, but the method can make up for the defect, realize the all-weather inversion of the effective soil moisture, and the inversion result shows good spatial continuity.

While the foregoing description shows and describes several preferred embodiments of the invention, it is to be understood, as noted above, that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. A method for obtaining all-weather effective soil moisture is characterized by comprising the following steps:

step 1, collecting CLDAS meteorological data: CLDAS meteorological data includes air temperature, wind speed, short wave radiation and specific humidity;

step 2, collecting MODIS data, preprocessing by utilizing ENVI software, and finally outputting in tiff format as input data of the invention: MODIS data includes surface temperature products, 8-day-synthesized leaf area index products, and 16-day-synthesized normalized vegetation index products;

and 3, judging the MODIS cloud, dividing the MODIS cloud image into an MODIS cloud-free image element and an MODIS cloud image element, and estimating the effective soil moisture of the MODIS cloud-free image element and the MODIS cloud image element based on CLDAS meteorological data and MODIS data.

2. The method for obtaining all-weather effective soil moisture as claimed in claim 1, wherein the MODIS non-cloud pixel and the MODIS cloud pixel are the self-owned attributes of the MODIS product data, and the information can be visually obtained only after the data is downloaded.

3. The method for obtaining all-weather effective soil moisture according to claim 2, wherein the step 3 of estimating the effective soil moisture of MODIS cloud-pixel-free soil moisture is specifically:

calculating the temperature T of the dry bare soil_s,maxTemperature T of wet bare soil_s,minFull vegetation cover temperature T under water stress_c,maxAnd a full vegetation coverage temperature T with good moisture status_c,minThe four temperature values are respectively calculated by the following formulas:

$T_{s,max}=\[\frac{r_a(R_n-G)}{C_v}\]+T_a---\left(1\right)$

$T_{s,min}=\[\frac{r_a(R_n-G)}{C_v}\]\·\[\mathrm{\γ}(\mathrm{\Δ}+\mathrm{\γ})\]-\frac{VPD}{\mathrm{\Δ}+\mathrm{\γ}}+T_a---\left(2\right)$

$T_{c,max}=\[\frac{r_a(R_n-G)}{C_v}\]\·\[\frac{\mathrm{\γ}(1+\frac{r_{cx}}{r_a})}{\mathrm{\Δ}+\mathrm{\γ}(1+\frac{r_{cx}}{r_a})}\]-\frac{VPD}{\mathrm{\Δ}+\mathrm{\γ}(1+\frac{r_{cx}}{r_a})}+T_a--\left(3\right)$

$T_{c,min}=\[\frac{r_a(R_n-G)}{C_v}\]\·\[\frac{\mathrm{\γ}(1+\frac{r_{cp}}{r_a})}{\mathrm{\Δ}+\mathrm{\γ}(1+\frac{r_{cp}}{r_a})}\]-\frac{VPD}{\mathrm{\Δ}+\mathrm{\γ}(1+\frac{r_{cp}}{r_a})}+T_a---\left(4\right)$

wherein Δ is a slope of a change in saturated vapor pressure with temperature, expressed as a function of air temperature;

wherein, T_aIs air temperature, read from CLDAS data;

C_vis the air specific heat, and takes the value of 1295.16;

r_cxand r_cpRespectively the maximum canopy impedance and the canopy impedance at potential evaporation, r_cxA value of 2000, r_cpCalculated according to LAI, r_cp100/LAI, wherein LAI is read from MODIS data;

gamma is the dry-wet table constant (value 0.066);

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

R_nis a net radiation, using CLDAS data to read the downlink short wave radiation (S)_d) Temperature (T)_a) Calculating the albedo under the four extreme conditions; r_n＝(1-α_s)S_d+_{s a}σT_a ⁴-_sσT_a ⁴Wherein, α_sThe earth surface albedo is 0.20, 0.10, 0.25 and 0.15 respectively under the conditions of dry bare soil, wet bare soil, full vegetation coverage stressed by water, covering and full vegetation coverage with good water;_sthe surface emissivity is 0.96 and 0.97 for dry and wet bare soil, 0.975 and 0.985 for water stressed and good vegetation, respectively, and the value sigma is the Stefan-Boltzmann constant, 5.67 × 10^-8；_aIs sky emissivity expressed as a function of air temperature_a＝9.2×10^-6×T_a ²；

G is the soil heat flux, denoted R_nG ═ 0.05+0.265 × (1-FVC)]R_nWherein, FVC is vegetation coverage, calculated using normalized vegetation index (NDVI) product (MOD13a 2):

$FVC={\[\frac{NDVI-{\mathrm{NDVI}}_{\min }}{{\mathrm{NDVI}}_{max}-{\mathrm{NDVI}}_{\min }}\]}^2---\left(5\right)$

wherein NDVI is directly read from MODIS data, NDVI_maxAnd NDVI_minNDVI respectively corresponding to the full vegetation cover and the bare soil is obtained from MOD13A2 data or obtained according to an empirical formula, and is respectively given as 0.86 and 0.2;

r_ais the aerodynamic roughness, and the calculation formula is:

$r_a=\frac{ln\left(\frac{2-d}{Z_{0M}}\right)\·ln\left(\frac{2-d}{Z_{0H}}\right)}{K^2{u}_2}---\left(6\right)$

wherein Z is_0MAnd Z_0HRespectively, the roughness lengths of momentum transfer and energy transfer, d is the zero plane displacement, z₀Is the surface roughness length, u₂Is the wind speed at 2 m; if the vegetation height is h, d is 0.65h, z₀＝0.1h，Z_0M＝0.1h，Z_0H＝0.1Z_0M(ii) a Wind speed u at 2m₂The conversion is carried out by the wind speed U at 10m of the CLDAS data, and the calculation formula is as follows:

$u_2=\frac{U\[ln\left(\frac{2-d}{z_0}\right)\]}{\[\ln \left(\frac{10-d}{z_0}\right)\]}---\left(7\right)$

for MODIS cloud-free pixels, the calculation formula of the effective soil moisture is as follows:

$M_0=\frac{(T_{c,\max }-T_{s,\max })\·FVC+T_{s,\max })-T_s}{\[(T_{c,\max }-T_{s,\max })\·FVC+T_{s,\max }\]-\[(T_{c,\min }-T_{s,\min })\·FVC+T_{s,\min }\]}---\left(8\right)$

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

4. The method for obtaining all-weather effective soil moisture according to claim 3, wherein the step 3 of estimating the MODIS cloud pixel effective soil moisture is specifically:

calculating the earth surface impedance:

$r_s=\frac{1}{0.004+0.012\×u_s}---\left(9\right)$

wherein u is_sThe wind speed at the height with the least influence of the roughness of the earth surface is estimated by using the leaf area index, the leaf diameter and the vegetation height, and if the vegetation height is h and the leaf diameter is s, u is_sThe approximation is:

$u_s=u_2\·{\exp }^{-0.28\·{\mathrm{LAI}}^{\frac{2}{3}}\·s^{-\frac{1}{3}}\·h^{-\frac{1}{3}}\[1-0.05/h\]}---\left(10\right)$

wherein, LAI is leaf area index, and is directly read from MODIS data;

MODIS cloud pixel effective soil moisture M₀The approximate calculation is:

$M_0=\frac{r_a}{r_s+r_a}---\left(11\right).$