CN-121561270-B - Remote sensing water index construction method combining surface temperature and short wave infrared information

Abstract

The invention discloses a remote sensing water index construction method combining surface temperature and short wave infrared information, which comprises the following steps of step 1, collecting satellite remote sensing data in a research area, step 2, collecting meteorological radiation data in the research area and carrying out spatial interpolation, step 3, carrying out remote sensing inversion on surface biophysical parameters, step 4, calculating surface net radiation and soil heat flux, step 5, constructing the remote sensing water index combining surface temperature and short wave infrared information, and step 6, carrying out space-time reconstruction on the remote sensing water index. The remote sensing moisture index combining the surface temperature and the short wave infrared information is constructed, the key defect that the traditional moisture index based on the surface temperature information is greatly influenced by the albedo is overcome, the supersaturation effect of the moisture index based on the short wave infrared information in a high vegetation coverage area is optimized, the satellite thermal infrared and short wave infrared visual measurement information can be fully utilized, and the moisture index calculation of a large-scale, non-data area and complex and non-uniform underlying surface is realized.

Inventors

• YANG YONGMIN
• LI CHENHAO
• YAN DENGHUA
• XIE LEI
• FU JUNE
• QU WEI
• LI XINGDONG
• FANG HAORAN
• SUN YINGWEI
• WANG YUJING

Assignees

• 中国水利水电科学研究院

Dates

Publication Date

20260508

Application Date

20251124

Claims (5)

1. 1. The remote sensing water index construction method combining the surface temperature and the short wave infrared information is characterized by comprising the following steps of: Step 1, collecting satellite remote sensing data of a research area, wherein the satellite remote sensing data of the research area simultaneously comprises thermal infrared and short wave infrared bands; Step 2, collecting and spatially interpolating the meteorological radiation data of the research area, wherein the collected meteorological radiation data of the research area comprises daily average air temperature, atmospheric pressure and relative humidity of the research area, and land atmospheric driving data provided by a land data assimilation system of the China meteorological bureau is used for the area with sparse site or lacking site observation data; step 3, performing remote sensing inversion on the surface biophysical parameters, namely obtaining the surface biophysical parameters including normalized vegetation index, surface albedo and vegetation coverage through remote sensing inversion; the vegetation coverage is calculated by adopting a normalized vegetation index and soil vegetation bipartite model method: (1) Wherein: NDVI is normalized vegetation index; the NDVI value of the pure vegetation pixels is set to be 0.85; The NDVI value of the pure bare soil pixel is set to be 0.125; step4, calculating the surface net radiation and the soil heat flux: The net surface radiation is calculated as follows: (2) Wherein: Clean radiation for the earth's surface; Is the earth surface albedo; for incident short wave radiation; Is incident long wave radiation; is emergent long-wave radiation; is the emissivity of the earth surface; Incident short wave radiation The calculation is as follows: (3) Wherein: is a solar constant; Is the angle between the solar ray and the surface normal; is the relative distance between the sun and the earth; for the atmospheric transmittance, the following is calculated: (4) Wherein: atmospheric pressure, kPa; is the equivalent water content of the atmosphere, and is mm; is the solar altitude; The value of the parameter for expressing the atmospheric turbidity is 0.25; Emitting long wave radiation The calculation is as follows: (5) Wherein: is the emissivity of the earth surface; is the St. Van Boltzmann constant; Is the surface temperature, K; parameterization using vegetation index: (6) Incident long wave radiation The calculation is as follows: (7) Wherein: k is the near-surface air temperature; for the effective emissivity of the atmosphere, the following formula is used for calculation: (8) the soil heat flux was calculated as follows: (9) Wherein: is soil heat flux; And 5, constructing remote sensing moisture indexes combining surface temperature and short wave infrared information, wherein the construction method specifically comprises the following steps of: 1) Normalized surface temperature moisture index calculation: the normalized surface temperature moisture index TSI is calculated as follows: (10) Wherein LST is satellite observation earth surface temperature, K; The temperature of the air when the satellite passes through the environment; Is the temperature difference of the earth and the air under the limit condition of wetting and the temperature of the air when the satellite passes the environment The daily average temperature was calculated as follows: (11) Wherein: Is the daily average temperature; The ratio of the temperature of the satellite before and after passing the border to the daily average temperature is shown; Wherein, the For the ground air temperature difference under the extreme dry limit, the maximum ground air temperature difference under the clear sky condition is calculated as follows based on the earth surface energy balance equation: (12) Wherein: Air density, kg/m3; Constant pressure specific heat for air, J/kg ∙ o C; for aerodynamic impedance, s/m, the following is calculated: (13) 2) Normalized shortwave infrared water loss index calculation: The normalized shortwave infrared water loss index WSI is calculated as follows: (14) Wherein NDSMI is normalized shortwave infrared moisture index, and is calculated as follows: (15) Wherein: And The reflectivity of the MODIS 5 th wave band and the MODIS 7 th wave band respectively; And The maximum value and the minimum value of NDSMI are respectively 0.667 and 0.035; 3) Remote sensing moisture index construction combining surface temperature and short wave infrared information: the remote sensing moisture index TSWI combining the surface temperature and the short wave infrared information is calculated as follows: (16) Wherein: Constructing based on normalized shortwave infrared water loss index and normalized surface temperature water loss index characteristic space Euclidean distance ratio; the diagonal line in the feature space is taken as 1.414, wherein, For the distance between the point to be calculated and the wetting limit point in the normalized short wave infrared water shortage index and normalized surface temperature water index space, The calculation is as follows: (17) Wherein: And Respectively normalizing the surface temperature water index and the shortwave infrared water shortage index of points to be calculated; And 6, reconstructing the remote sensing moisture index in time and space, namely interpolating TSWI by adopting a linear interpolation method of data before and after time sequence, and replacing time sequence data which is still missing after linear interpolation by adopting a seasonal average value, so as to obtain the remote sensing moisture index of the space-time continuous region.
2. 2. The method for constructing a remote sensing water index combining surface temperature and short wave infrared information according to claim 1, wherein the satellite remote sensing data in the step 1 comprises MODIS remote sensing product data and Landsat8 and Landsat9 satellite data.
3. 3. The remote sensing water index construction method combining surface temperature and short wave infrared information is characterized in that land surface atmosphere driving data provided by a land surface data assimilation system of the China weather department in the step 2 comprises six elements of 2m air temperature, 2m specific humidity, 10m wind speed, ground air pressure, precipitation and short wave radiation, and real-time products of the land surface data assimilation system of the China weather department are obtained through a national weather science data center.
4. 4. The method for constructing the remote sensing water index by combining the surface temperature and the short wave infrared information according to claim 1, wherein the method adopted by the spatial interpolation of the collected meteorological radiation data of the research area in the step 2 is an inverse distance weight or a kriging method.
5. 5. The method for constructing the remote sensing water index by combining surface temperature and short wave infrared information according to claim 1, wherein the normalized vegetation index and the surface albedo in the step 3 are obtained through a MODIS vegetation index data product.

Description

Remote sensing water index construction method combining surface temperature and short wave infrared information Technical Field The invention belongs to the technical field of remote sensing surface moisture monitoring, and particularly relates to a remote sensing moisture index construction method combining surface temperature and short wave infrared information. Background Remote sensing moisture index is an important tool for monitoring surface moisture conditions, and plays a key role in the fields of agriculture, hydrology, weather, ecology and the like. The vegetation water stress directly affects the physiological and biochemical processes, morphological structures and growth and development of vegetation, and timely and accurately monitors the water condition of the vegetation, thereby having important significance in improving the water management level of crops, guiding the agricultural water-saving production and the like. Meanwhile, due to the non-uniformity of the underlying surface, the moisture distribution of the underlying surface is often caused to be obviously non-uniform, and the construction of the moisture index based on remote sensing can serve the business application fields of regional evapotranspiration estimation, irrigation monitoring, land hydrologic model simulation and assimilation, regional drought monitoring and evaluation, ecological vegetation restoration in arid regions and the like. A direct means of monitoring vegetation water stress using remote sensing is to monitor changes in vegetation index, crop water content, canopy temperature and soil moisture. The remote sensing water index monitoring method mainly comprises (1) a water stress monitoring method based on a greenness vegetation index, wherein the method uses the correlation between chlorophyll and the vegetation index to indirectly reflect the vegetation water stress condition under drought conditions, such as TCI and VCI indexes. Although such methods can reveal the physiological state of vegetation to some extent, there are significant shortcomings in capturing the dynamic response of water stress. (2) The water stress monitoring method based on thermal infrared can monitor the water content of the canopy and the water stress condition of the vegetation by combining thermal infrared data. Tanner proposes a theoretical framework of CWSI (loop WATER STRESS index) based on a canopy energy balance principle and Penman-Monteith formula by Jackson et al since canopy temperature is used for indicating vegetation water deficiency, and provides an important basis for vegetation water stress diagnosis based on thermal infrared. Moran et al propose a Water Deficit Index (WDI) based on vegetation index/surface temperature feature space, further applying CWSI expansion to sparse vegetation coverage. Other canopy temperature-based water stress methods include canopy temperature variation, reference temperature generation, canopy-air temperature difference, and the like. However, the water stress monitoring method based on thermal infrared is strongly influenced by environmental factors such as incident solar radiation, albedo and the like, and has poor stability. (3) The water stress monitoring method based on passive microwave remote sensing soil water and vegetation optical thickness is free from the influence of atmosphere and cloud coverage, has potential in areas with more cloud coverage, but the current passive microwave remote sensing resolution is thicker and is a main factor limiting the wide application of the passive microwave remote sensing resolution. (4) The water stress detection method based on the chlorophyll fluorescence of the vegetation is closely related to the photosynthesis of the vegetation, can be used for monitoring the physiological state of the vegetation and the water stress condition, and is applied to water stress detection in intercontinental and global scale. The water stress detection method based on vegetation chlorophyll fluorescence has great potential, but the resolution of the current satellite vegetation chlorophyll fluorescence products is thicker, and the water stress detection requirement of the regional scale is difficult to meet. (5) The method for monitoring the water stress based on the short wave infrared has the advantages that the short wave infrared spectrum is extremely sensitive to the change of vegetation and soil water content, and the potential of the method for monitoring the water stress based on the short wave infrared spectrum is huge in the aspect of describing vegetation water stress. Based on short-wave infrared information researchers construct a series of remote sensing water stress indexes for describing the water stress condition of the underlying surface, such as a humidity stress index (MSI), a global vegetation water index (GVMI), a short-wave infrared water stress index (SIWSI), a short-wave infrared angle normalization index SANI, a short-wave infrared