A method for constructing a remote sensing moisture index combining land surface temperature and shortwave infrared information

Abstract

The application discloses a kind of remote sensing moisture index construction methods of joint ground surface temperature and short-wave infrared information, the method includes the following steps: step 1, research area satellite remote sensing data collection;Step 2, research area meteorological radiation data collection and spatial interpolation;Step 3, remote sensing inversion of ground surface biophysical parameter;Step 4, ground surface net radiation and soil heat flux calculation;Step 5, remote sensing moisture index construction of joint ground surface temperature and short-wave infrared information;Step 6, remote sensing moisture index space-time reconstruction.The application constructs a kind of remote sensing moisture index of joint ground surface temperature and short-wave infrared information, improves the key deficiency that traditional moisture index based on ground surface temperature information is greatly influenced by albedo, optimizes the oversaturation effect of moisture index based on short-wave infrared information in high vegetation cover area, can make full use of satellite thermal infrared and short-wave infrared observation information, realizes the moisture index calculation of large range, no data area, complex non-uniform underlying surface.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing surface moisture monitoring technology, and particularly relates to a method for constructing a remote sensing moisture index that combines surface temperature and shortwave infrared information. Background Technology

[0002] Remote sensing moisture indices are crucial tools for monitoring surface moisture conditions, playing a key role in agriculture, hydrology, meteorology, and ecology. Vegetation water stress directly impacts vegetation's physiological and biochemical processes, morphological structure, and growth and development. Timely and accurate monitoring of vegetation moisture status is essential for improving crop water management and guiding water-saving agricultural production. Meanwhile, due to the heterogeneity of the underlying surface, moisture distribution often exhibits significant non-uniformity. Constructing remote sensing-based moisture indices can serve operational applications such as regional evapotranspiration estimation, irrigation monitoring, land surface hydrological model simulation and assimilation, regional drought monitoring and assessment, and ecological vegetation restoration in arid regions.

[0003] The direct means of using remote sensing to monitor vegetation water stress is to monitor changes in vegetation indices, crop water content, canopy temperature, and soil moisture. Remote sensing water index monitoring methods mainly include: (1) Water stress monitoring methods based on green vegetation indices; these methods use the correlation between chlorophyll and vegetation indices to indirectly reflect the vegetation water stress status under drought conditions, such as TCI and VCI indices. Although these methods can reveal the physiological state of vegetation to a certain extent, they are significantly insufficient in capturing the dynamic response process of water stress. (2) Water stress monitoring methods based on thermal infrared; vegetation subjected to water stress will cause changes in canopy temperature, and combined with thermal infrared data, canopy water content and vegetation water stress status can be monitored. Since Tanner proposed using canopy temperature to indicate vegetation water deficit, the theoretical framework of CWSI (Crop water stress index) proposed by Jackson et al. based on the canopy energy balance principle and the Penman-Monteith formula has provided an important foundation for the diagnosis of vegetation water stress based on thermal infrared. Moran et al. proposed the Water Deficit Index (WDI) based on the vegetation index / land surface temperature characteristic space, and further extended the CWSI to sparse vegetation cover areas. Other water stress methods based on canopy temperature include canopy temperature variability, reference temperature, canopy-air temperature difference, etc. However, water stress monitoring methods based on thermal infrared are strongly affected by environmental factors such as incident solar radiation and albedo, and have poor stability. (3) Water stress monitoring methods based on passive microwave remote sensing of soil moisture and vegetation optical thickness; microwave remote sensing is not affected by the atmosphere and cloud cover, and has potential in areas with more cloud cover, but the current coarse resolution of passive microwave remote sensing is the main factor restricting its widespread application. (4) Water stress detection methods based on vegetation chlorophyll fluorescence; chlorophyll fluorescence is closely related to vegetation photosynthesis and can be used to monitor the physiological state of vegetation and water stress, and has been applied to water stress detection at intercontinental and global scales. The method of detecting water stress based on vegetation chlorophyll fluorescence is very promising, but the resolution of current satellite vegetation chlorophyll fluorescence products is relatively coarse, which is difficult to meet the needs of regional-scale water stress detection. (5) Water stress monitoring method based on shortwave infrared; the shortwave infrared spectrum is extremely sensitive to changes in vegetation and soil moisture content, and has great potential in characterizing vegetation water stress. Based on shortwave infrared information, researchers have constructed a series of remote sensing water stress indices to characterize the underlying surface water stress, such as the moisture stress index (MSI), the global vegetation moisture index (GVMI), the shortwave infrared water stress index (SIWSI), the shortwave infrared angle normalization index (SANI), and the shortwave infrared angle slope index (SASI). However, the shortwave infrared index has a saturation effect in high vegetation cover areas and is insufficient in reflecting water stress in high vegetation cover areas.

[0004] Although significant progress has been made in the research and development of remote sensing water indices, studies have shown that water indices based on passive microwave remote sensing and vegetation chlorophyll fluorescence are limited by their coarse resolution, making them difficult to apply to regional-scale monitoring. Traditional remote sensing water indices that rely solely on vegetation greenness or underlying surface temperature are also susceptible to environmental factors and are ill-suited for capturing water stress, thus having significant limitations in evapotranspiration modeling and regional drought monitoring in arid regions. Currently, there is still no remote sensing water index suitable for monitoring dynamic information on regional water stress, and the construction of a remote sensing water index integrating multi-source information remains a hot topic and a challenge. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing a remote sensing moisture index that combines surface temperature and shortwave infrared information, so as to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention discloses a method for constructing a remote sensing moisture index that combines surface temperature and shortwave infrared information. The method includes the following steps:

[0008] Step 1: Satellite remote sensing data collection for the study area: Collect satellite remote sensing data for the study area that includes both thermal infrared and shortwave infrared bands.

[0009] Step 2: Collection and Spatial Interpolation of Meteorological Radiation Data in the Study Area: Collect meteorological radiation data in the study area, including daily average temperature, atmospheric pressure, and relative humidity. For areas with sparse or lacking station observation data, use land surface atmospheric driving data provided by the China Meteorological Administration's Land Surface Data Assimilation System. Spatially interpolate the collected meteorological radiation data in the study area to make it consistent with the spatial resolution of the satellite remote sensing data.

[0010] Step 3, Remote sensing inversion of surface biophysical parameters: Obtain surface biophysical parameters, including normalized difference vegetation index, surface albedo, and vegetation cover, through remote sensing inversion;

[0011] Vegetation cover was calculated using the normalized difference in vegetation index and the soil-vegetation dichotomy model.

[0012] (1)

[0013] In the formula: Vegetation cover; NDVI is the normalized vegetation index. The NDVI value for pure vegetation pixels is set to 0.85; The NDVI value for a pure bare soil pixel is set to 0.125;

[0014] Step 4: Calculation of net surface radiation and soil heat flux:

[0015] The net radiation at the Earth's surface is calculated as follows:

[0016] (2)

[0017] In the formula: Net surface radiation; It is the surface albedo; For incident shortwave radiation; For incident long-wave radiation; To emit long-wave radiation; The surface emissivity;

[0018] Incident shortwave radiation The calculation is as follows:

[0019] (3)

[0020] In the formula: It is the solar constant; The angle between the sunlight and the surface normal; The relative distance between the Earth and the Sun; Atmospheric transmittance is calculated as follows:

[0021] (4)

[0022] In the formula: Atmospheric pressure, kPa; The atmospheric equivalent water content is expressed in mm. The solar altitude angle; The parameter used to express atmospheric turbidity is set to 0.25;

[0023] Long-wave radiation emitted The calculation is as follows:

[0024] (5)

[0025] In the formula: The surface emissivity; It is Stefan Boltzmann's constant; The surface temperature is K;

[0026] Parameterization using vegetation indices:

[0027] (6)

[0028] Incident longwave radiation The calculation is as follows:

[0029] (7)

[0030] In the formula: The near-surface air temperature, in K; The effective atmospheric emissivity is calculated using the following formula:

[0031] (8)

[0032] The soil heat flux is calculated as follows:

[0033] (9)

[0034] In the formula: Soil heat flux;

[0035] Step 5: Construction of the remote sensing moisture index combining surface temperature and shortwave infrared information: This specifically includes the following processes:

[0036] 1) Calculation of Normalized Temperature-Moisture Index:

[0037] The Normalized Temperature and Moisture Index (TSI) is calculated as follows:

[0038] (10)

[0039] In the formula: LST is the satellite-observed Earth's surface temperature, in K; The air temperature when the satellite passes overhead; The temperature difference between ground and air under humid extreme conditions; air temperature during satellite transit. The calculation is based on the daily average temperature as follows:

[0040] (11)

[0041] In the formula: The average daily temperature; This is the ratio of the temperature one hour before and after the satellite's passage to the daily average temperature.

[0042] in, The maximum ground-temperature difference under clear-sky conditions, representing the extreme dryness limit, is calculated based on the Earth's surface energy balance equation as follows:

[0043] (12)

[0044] In the formula: Air density, kg / m³; The specific heat of air at constant pressure, J / kg∙ o C; The aerodynamic impedance, s / m, is calculated as follows:

[0045] (13)

[0046] 2) Calculation of the normalized shortwave infrared water shortage index:

[0047] The normalized shortwave infrared water scarcity index (WSI) is calculated as follows:

[0048] (14)

[0049] In the formula: NDSMI is the normalized shortwave infrared moisture index, calculated as follows:

[0050] (15)

[0051] In the formula: and These are the reflectance values ​​for MODIS band 5 and band 7, respectively. and The maximum and minimum values ​​of NDSMI are 0.667 and 0.035, respectively.

[0052] 3) Construction of a remote sensing moisture index combining surface temperature and shortwave infrared information:

[0053] The remote sensing moisture index TSWI, which combines surface temperature and shortwave infrared information, is calculated as follows:

[0054] (16)

[0055] In the formula: It is constructed based on the ratio of the characteristic spatial Euclidean distance between the normalized shortwave infrared water shortage index and the normalized surface temperature and moisture index. is the diagonal in the feature space, with a value of 1.414; where, The distance of the point to be calculated from the wetting limit point in the space of the normalized shortwave infrared water shortage index and the normalized surface temperature and moisture index is denoted as . The calculation is as follows:

[0056] (17)

[0057] In the formula: and The normalized surface temperature moisture index and the normalized shortwave infrared water shortage index of the points to be calculated are respectively.

[0058] Step 6: Spatiotemporal reconstruction of remote sensing moisture index: The TSWI is interpolated using a linear interpolation method for the data before and after the time series. For the time series data that is still missing after linear interpolation, the seasonal average value is used to replace it, so as to obtain a spatiotemporally continuous regional remote sensing moisture index.

[0059] Furthermore, the satellite remote sensing data in step 1 includes MODIS remote sensing product data as well as Landsat 8 and Landsat 9 satellite data.

[0060] Furthermore, the land surface atmospheric driving data provided by the China Meteorological Administration's land surface data assimilation system in step 2 includes six elements: 2m air temperature, 2m specific humidity, 10m wind speed, surface air pressure, precipitation, and shortwave radiation; the real-time products of the China Meteorological Administration's land surface data assimilation system are obtained through the National Meteorological Science Data Center.

[0061] Furthermore, in step 2, the method used for spatial interpolation of the collected meteorological radiation data of the study area is inverse distance weighting or Kriging.

[0062] Furthermore, the normalized vegetation index and surface albedo in step 3 were obtained using MODIS vegetation index data products.

[0063] The beneficial effects of this invention are as follows: This invention constructs a remote sensing moisture index that combines surface temperature and shortwave infrared information, which improves upon the key deficiency of traditional moisture indices based on surface temperature information, which are greatly affected by albedo. It also optimizes the oversaturation effect of moisture indices based on shortwave infrared information in high vegetation cover areas. It can make full use of satellite thermal infrared and shortwave infrared observation information to realize the calculation of moisture index on large-scale, data-free areas and complex non-uniform underlying surfaces. It can serve operational application fields such as regional evapotranspiration estimation, irrigation monitoring, land surface hydrological model simulation and assimilation, regional drought monitoring and assessment, and ecological vegetation restoration in arid areas.

[0064] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the method flow described in this invention;

[0066] Figure 2 A schematic diagram illustrating the principle of constructing a remote sensing moisture index that combines surface temperature and shortwave infrared information;

[0067] Figure 3 This is a comparison chart of relative humidity and TSWI time series of soil in the central Tibetan Plateau in Example 1. Detailed Implementation

[0068] This invention discloses a method for constructing a remote sensing moisture index that combines surface temperature and shortwave infrared information, such as... Figure 1 As shown, the method includes the following steps:

[0069] Step 1: Satellite remote sensing data collection for the study area:

[0070] For satellite remote sensing data collection, satellite data that includes both thermal infrared and shortwave infrared bands is preferred. Specifically, MODIS remote sensing product data can meet the requirements for calculating the remote sensing moisture index at a resolution of 1 km, while Landsat 8 and Landsat 9 data can meet the requirements for calculating the remote sensing moisture index at a resolution of 100 meters. The download address for EOS / MODIS satellite product data is: https: / / modis.gsfc.nasa.gov / data / dataprod / . The URL for Landsat 8 and Landsat 9 satellite data is https: / / www.earthdata.nasa.gov / data / projects / hls.

[0071] Step 2: Collection and spatial interpolation of meteorological radiation data in the study area:

[0072] For meteorological radiation data collection, daily average temperature, atmospheric pressure, and relative humidity data for the study area need to be collected. For areas with sparse or lacking station observation data, land surface atmospheric driving data provided by the China Meteorological Administration's Land Surface Data Assimilation System can be used, which includes six elements: 2m air temperature, 2m specific humidity, 10m wind speed, surface air pressure, precipitation, and shortwave radiation. Real-time products of the China Meteorological Administration's Land Surface Data Assimilation System can be obtained through the National Meteorological Science Data Center (http: / / data.cma.cn).

[0073] To ensure consistency with the spatial resolution of the selected satellite remote sensing data, inverse distance weighting or kriging was used to spatially interpolate the meteorological radiation data for the study area. For the land surface atmospheric driving data from the China Meteorological Administration's land surface data assimilation system, a spatial resampling method was employed to prepare the meteorological radiation data for the study area.

[0074] Step 3: Remote sensing inversion of surface biophysical parameters:

[0075] Land surface biophysical parameters, including the Normalized Difference Vegetation Index (NDV), albedo, and vegetation cover, are obtained through remote sensing inversion. The NDC and albedo can be directly obtained from MODIS vegetation index data products. The NDC is obtained using the latest MODIS 6.1 version of the land surface vegetation index data product (MOD13AI.061). Albedo can be obtained from the latest MODIS 6.1 version of the land surface albedo data product (MCD43A3.061). Vegetation cover is calculated using the commonly used NDC and soil-vegetation dichotomy model.

[0076] (1)

[0077] In the formula: Vegetation cover; NDVI is the normalized vegetation index. The NDVI value for pure vegetation pixels is set to 0.85; The NDVI value for a pure bare soil pixel is set to 0.125.

[0078] Step 4: Calculation of net surface radiation and soil heat flux:

[0079] The net radiation at the Earth's surface is calculated as follows:

[0080] (2)

[0081] In the formula: Net surface radiation; It is the surface albedo; For incident shortwave radiation; For incident long-wave radiation; To emit long-wave radiation; is the surface emissivity.

[0082] Incident shortwave radiation The calculation is as follows:

[0083] (3)

[0084] In the formula: It is the solar constant; This is the angle between the sunlight and the surface normal, which can be replaced by the solar zenith angle from satellite parameters; The relative distance between the Earth and the Sun; Atmospheric transmittance is calculated as follows:

[0085] (4)

[0086] In the formula: Atmospheric pressure, kPa; The atmospheric equivalent water content is expressed in mm. The solar altitude angle; The parameter used to express atmospheric turbidity is set to 0.25.

[0087] Long-wave radiation emitted The calculation is as follows:

[0088] (5)

[0089] In the formula: The surface emissivity; It is Stefan Boltzmann's constant; Let K be the surface temperature.

[0090] Parameterization using vegetation indices:

[0091] (6)

[0092] Incident longwave radiation The calculation is as follows:

[0093] (7)

[0094] In the formula: The near-surface air temperature, in K; The effective atmospheric emissivity is calculated using the following formula:

[0095] (8)

[0096] The soil heat flux is calculated as follows:

[0097] (9)

[0098] In the formula: This refers to soil heat flux.

[0099] Step 5: Constructing a remote sensing moisture index combining surface temperature and shortwave infrared information:

[0100] This invention uses land surface temperature and shortwave infrared radiation, which are sensitive to the moisture content of the underlying surface, to construct a remote sensing moisture index. To eliminate the inconsistency in dimensions between land surface temperature and shortwave infrared radiation, both are normalized, i.e., the Normalized Temperature-Moisture Index (TSI) and the Normalized Shortwave Infrared Water Depletion Index (WSI) are calculated separately. The feature space (WSI / TSI) based on the Normalized Shortwave Infrared Water Depletion Index and the Normalized Temperature-Moisture Index is as follows: Figure 2 As shown. The aridity limit point corresponds to higher surface temperature and lower underlying surface moisture supply, corresponding to higher TSI and lower WSI. The humidity limit point corresponds to lower surface temperature and higher underlying surface moisture supply, corresponding to lower TSI and higher WSI. Therefore, the distance between the aridity limit point and the humidity limit point is the maximum distance in the WSI / TSI two-dimensional feature space. Therefore, the distance from the point to be calculated to the humidity limit point can be constructed ( The distance between the dryness limit point and the wetness limit point ( The remote sensing moisture index is a ratio of the ratio of the radiation balance to the energy balance. In the vertical direction, the constructed remote sensing moisture index reflects the response of the underlying soil moisture and vegetation moisture content.

[0101] Furthermore, it should be noted that this feature space is completely different from the traditional feature spaces based on land surface temperature and vegetation index. Traditional feature spaces based on land surface temperature and vegetation index, because the vegetation index primarily reflects a greenness response, are essentially not sensitive to the moisture response of the underlying surface. In particular, the vegetation index's response to water bodies is negative, resulting in a trapezoidal feature space. Determining such a feature space often requires identifying four extreme points, which is difficult in practical application. In contrast, the normalized shortwave infrared water scarcity index and normalized land surface temperature and moisture index feature spaces constructed in this invention effectively avoid the shortcomings of traditional feature spaces, requiring only the characterization of two extreme points: dryness and wetness. Moreover, the theoretical basis of this feature space is clear.

[0102] Constructing a remotely sensed moisture index that combines surface temperature and shortwave infrared information involves the following processes:

[0103] 1) Calculation of Normalized Temperature-Moisture Index:

[0104] The Normalized Temperature-Moisture Index (TSI) is one of the key components in constructing the remote sensing moisture index in this invention. The TSI is calculated based on the surface temperature difference at extreme dry and wet limits, as follows:

[0105] (10)

[0106] In the formula: LST is the satellite-observed Earth's surface temperature, in K; The air temperature when the satellite passes overhead; The temperature difference between the land surface and the air under the humid limit conditions typically corresponds to the difference between the surface temperature and the air temperature at the lake surface. Empirical observations have shown that... The air temperature during a satellite's transit is close to zero. The following calculation is derived using the daily average temperature:

[0107] (11)

[0108] In the formula: The average daily temperature; It is the ratio of the temperature one hour before and after the satellite's passage to the daily average temperature, which can be calculated based on long-term temperature observation data or meteorological reanalysis data from the station.

[0109] in, The temperature difference between the Earth's surface and the Earth's surface under extreme dryness conditions is greatly affected by the solar radiation received. The maximum temperature difference between the Earth's surface and the Earth's surface under clear sky conditions can be calculated based on the Earth's surface energy balance equation as follows:

[0110] (12)

[0111] In the formula: air density (kg / m3); The specific heat of air at constant pressure (J / kg· ... o C); The aerodynamic impedance (s / m) is calculated as follows:

[0112] (13)

[0113] 2) Calculation of the normalized shortwave infrared water shortage index:

[0114] Shortwave infrared light is sensitive to moisture information, and compared to the visible and near-infrared spectra, it responds better to changes in vegetation and soil moisture content. Based on this key characteristic of shortwave infrared light's extreme sensitivity to moisture information, the normalized shortwave infrared water deficit index (WSI) is calculated as follows:

[0115] (14)

[0116] In the formula: NDSMI is the normalized shortwave infrared moisture index, calculated as follows:

[0117] (15)

[0118] In the formula: and The values ​​represent the reflectance of MODIS band 5 (wavelength 1.24 μm) and band 7 (wavelength 2.130 μm), respectively. Since NDSMI is calculated based on two shortwave infrared bands, it is insensitive to changes in non-moisture information and can effectively characterize the moisture information of the underlying surface. and These are the maximum and minimum values ​​of NDSMI, which can be 0.667 and 0.035.

[0119] 3) Construction of a remote sensing moisture index combining surface temperature and shortwave infrared information:

[0120] Based on the principle of constructing a remote sensing moisture index that combines land surface temperature and shortwave infrared information, this invention proposes a remote sensing moisture index (TSWI) based on the Euclidean distance method, calculated as follows:

[0121] (16)

[0122] In the formula: The system is constructed based on the ratio of the characteristic spatial Euclidean distance between the normalized shortwave infrared water scarcity index and the normalized surface temperature and moisture index. is the diagonal in the feature space, with a value of 1.414. Wherein, The distance of the point to be calculated from the wetting limit point in the space of the normalized shortwave infrared water shortage index and the normalized surface temperature and moisture index is denoted as . The calculation is as follows:

[0123] (17)

[0124] In the formula: and The normalized surface temperature and moisture index and the normalized shortwave infrared water shortage index are respectively used for the points to be calculated.

[0125] By combining surface temperature and shortwave infrared response information to moisture, a novel remote sensing moisture index was constructed.

[0126] Step 6: Spatiotemporal reconstruction of remote sensing moisture index:

[0127] Due to the influence of weather factors such as clouds and fog, satellite remote sensing data for land surface temperature and shortwave infrared radiation inevitably suffers from data gaps. To obtain a remotely sensed moisture index that combines spatiotemporally continuous regional land surface temperature and shortwave infrared information, it is necessary to... Temporal interpolation is performed. This invention uses a linear interpolation method for the TSWI data before and after the temporal series. For temporal data that is still missing after linear interpolation, the seasonal average value is used as a substitute. Through the above processing, a spatiotemporally continuous regional remote sensing moisture index is obtained.

[0128] Based on the spatiotemporal reconstruction of the TSWI remote sensing moisture index, regional surface water status monitoring, evapotranspiration inversion, and regional drought monitoring applications can be carried out using the TSWI remote sensing hydrological index.

[0129] Example 1

[0130] This embodiment is an application example of the above method.

[0131] To verify the reliability of the TSWI remote sensing moisture index constructed by the above method, this embodiment collected MODIS data products and ERA5-Land data from the central Tibetan Plateau and calculated the TSWI index for the region. This embodiment collected the soil temperature and moisture multi-scale observation network dataset for the central Tibetan Plateau constructed by Yang Kun et al. [Yang, K., Chen, Y., Zhao, L., Qin, J., La, Z., Zhou, X., Jiang, Y., Tian, ​​J. (2021). The multiscale observation network of soil temperature and moisture on the central Tibetan Plateau (2010-2021). National TibetanPlateau / Third Pole Environment Data Center. https: / / doi.org / 10.11888 / Terre.tpdc.271918.https: / / cstr.cn / 18406.11.Terre.tpdc.271918.].

[0132] The reliability of the TSWI index was assessed using surface soil moisture data from 57 observation stations across a soil moisture monitoring network. The mean surface relative soil moisture from these 57 stations represents the regional soil moisture status, based on data from soil relative humidity and TSWI time series data in the central Tibetan Plateau. Figure 3 As shown in the time-series comparison chart, TSWI effectively reflects the temporal changes in surface soil moisture, with a correlation coefficient exceeding 0.81. This result demonstrates the significant advantage of TSWI in revealing underlying surface moisture changes. This embodiment proves that the novel combined surface temperature and shortwave infrared moisture index proposed in this invention can provide a new means for regional moisture status, evapotranspiration retrieval, and regional drought monitoring.

[0133] Finally, it should be noted that the above description is only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred arrangement, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for constructing a remote sensing moisture index that combines surface temperature and shortwave infrared information, characterized in that, The method includes the following steps: Step 1: Satellite remote sensing data collection for the study area: Collect satellite remote sensing data for the study area that includes both thermal infrared and shortwave infrared bands. Step 2: Collection and Spatial Interpolation of Meteorological Radiation Data in the Study Area: Collect meteorological radiation data in the study area, including daily average temperature, atmospheric pressure, and relative humidity. For areas with sparse or lacking station observation data, use land surface atmospheric driving data provided by the China Meteorological Administration's Land Surface Data Assimilation System. Spatially interpolate the collected meteorological radiation data in the study area to make it consistent with the spatial resolution of the satellite remote sensing data. Step 3, Remote sensing inversion of surface biophysical parameters: Obtain surface biophysical parameters, including normalized difference vegetation index, surface albedo, and vegetation cover, through remote sensing inversion; Vegetation cover was calculated using the normalized difference in vegetation index and the soil-vegetation dichotomy model. （1） In the formula: Vegetation cover; NDVI is the normalized vegetation index. The NDVI value for pure vegetation pixels is set to 0.85; The NDVI value for a pure bare soil pixel is set to 0.125; Step 4: Calculation of net surface radiation and soil heat flux: The net radiation at the Earth's surface is calculated as follows: （2） In the formula: Net surface radiation; It is the surface albedo; For incident shortwave radiation; For incident long-wave radiation; To emit long-wave radiation; The surface emissivity; Incident shortwave radiation The calculation is as follows: （3） In the formula: It is the solar constant; The angle between the sunlight and the surface normal; The relative distance between the Earth and the Sun; Atmospheric transmittance is calculated as follows: （4） In the formula: Atmospheric pressure, kPa; The atmospheric equivalent water content is expressed in mm. The solar altitude angle; The parameter used to express atmospheric turbidity is set to 0.25; Long-wave radiation emitted The calculation is as follows: （5） In the formula: The surface emissivity; It is Stefan Boltzmann's constant; The surface temperature is K; Parameterization using vegetation indices: （6） Incident longwave radiation The calculation is as follows: （7） In the formula: The near-surface air temperature, in K; The effective atmospheric emissivity is calculated using the following formula: （8） The soil heat flux is calculated as follows: （9） In the formula: Soil heat flux; Step 5: Construction of the remote sensing moisture index combining surface temperature and shortwave infrared information: This specifically includes the following processes: 1) Calculation of Normalized Temperature-Moisture Index: The Normalized Temperature and Moisture Index (TSI) is calculated as follows: （10） In the formula: LST is the satellite-observed Earth's surface temperature, in K; The air temperature when the satellite passes overhead; The temperature difference between ground and air under humid extreme conditions; air temperature during satellite transit. The calculation is based on the daily average temperature as follows: （11） In the formula: The average daily temperature; This is the ratio of the temperature one hour before and after the satellite's passage to the daily average temperature. in, The maximum ground-temperature difference under clear-sky conditions, representing the extreme dryness limit, is calculated based on the Earth's surface energy balance equation as follows: （12） In the formula: Air density, kg / m³; The specific heat of air at constant pressure, J / kg∙ o C; The aerodynamic impedance, s / m, is calculated as follows: （13） 2) Calculation of the normalized shortwave infrared water shortage index: The normalized shortwave infrared water scarcity index (WSI) is calculated as follows: （14） In the formula: NDSMI is the normalized shortwave infrared moisture index, calculated as follows: （15） In the formula: and These are the reflectance values ​​for MODIS band 5 and band 7, respectively. and The maximum and minimum values ​​of NDSMI are 0.667 and 0.035, respectively. 3) Construction of a remote sensing moisture index combining surface temperature and shortwave infrared information: The remote sensing moisture index TSWI, which combines surface temperature and shortwave infrared information, is calculated as follows: （16） In the formula: It is constructed based on the ratio of the characteristic spatial Euclidean distance between the normalized shortwave infrared water shortage index and the normalized surface temperature and moisture index. is the diagonal in the feature space, with a value of 1.414; where, The distance of the point to be calculated from the wetting limit point in the space of the normalized shortwave infrared water shortage index and the normalized surface temperature and moisture index is denoted as . The calculation is as follows: （17） In the formula: and The normalized surface temperature moisture index and the normalized shortwave infrared water shortage index of the points to be calculated are respectively. Step 6: Spatiotemporal reconstruction of remote sensing moisture index: The TSWI is interpolated using a linear interpolation method for the data before and after the time series. For the time series data that is still missing after linear interpolation, the seasonal average value is used to replace it, so as to obtain a spatiotemporally continuous regional remote sensing moisture index.

2. The method for constructing a remote sensing moisture index combining surface temperature and shortwave infrared information according to claim 1, characterized in that, The satellite remote sensing data in Step 1 includes MODIS remote sensing product data as well as Landsat 8 and Landsat 9 satellite data.

3. The method for constructing a remote sensing moisture index combining surface temperature and shortwave infrared information according to claim 1, characterized in that, The land surface atmospheric driving data provided by the China Meteorological Administration's land surface data assimilation system in step 2 includes six elements: 2m air temperature, 2m specific humidity, 10m wind speed, surface air pressure, precipitation, and shortwave radiation. The real-time products of the China Meteorological Administration's land surface data assimilation system are obtained through the National Meteorological Science Data Center.

4. The method for constructing a remote sensing moisture index combining surface temperature and shortwave infrared information according to claim 1, characterized in that, In step 2, the spatial interpolation of the collected meteorological radiation data of the study area is performed using either inverse distance weighting or kriging.

5. The method for constructing a remote sensing moisture index combining surface temperature and shortwave infrared information according to claim 1, characterized in that, The normalized vegetation index and surface albedo in step 3 were obtained using MODIS vegetation index data products.

Images