CN108876172B - Surface soil water content assessment method based on improved MODIS vegetation water supply index - Google Patents

Surface soil water content assessment method based on improved MODIS vegetation water supply index Download PDF

Info

Publication number
CN108876172B
CN108876172B CN201810683462.6A CN201810683462A CN108876172B CN 108876172 B CN108876172 B CN 108876172B CN 201810683462 A CN201810683462 A CN 201810683462A CN 108876172 B CN108876172 B CN 108876172B
Authority
CN
China
Prior art keywords
index
vegetation
ndvi
water content
modis
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810683462.6A
Other languages
Chinese (zh)
Other versions
CN108876172A (en
Inventor
孟令奎
洪志明
张文
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University WHU
Original Assignee
Wuhan University WHU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan University WHU filed Critical Wuhan University WHU
Priority to CN201810683462.6A priority Critical patent/CN108876172B/en
Publication of CN108876172A publication Critical patent/CN108876172A/en
Application granted granted Critical
Publication of CN108876172B publication Critical patent/CN108876172B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Health & Medical Sciences (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Health & Medical Sciences (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Educational Administration (AREA)
  • Primary Health Care (AREA)
  • Quality & Reliability (AREA)
  • Astronomy & Astrophysics (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Remote Sensing (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Agronomy & Crop Science (AREA)
  • Animal Husbandry (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Mining & Mineral Resources (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The invention provides a method for evaluating the water content of surface soil based on an improved MODIS vegetation water supply index, which deeply researches the characteristics of VSWI mathematical expression of the vegetation water supply index, finds that the variation of the numerator denominator has larger influence degree difference on the VSWI of the vegetation water supply index, improves the influence of the denominator on the index by improving the numerator sensitivity and adjusting the exponential expression structure, and provides a soil water content evaluation method based on the improved MODIS vegetation water supply index, namely a surface water content temperature index SWCTI. Compared with a passive microwave product, the SWCTI has better spatial resolution and higher time resolution compared with an active microwave product. The method provided by the invention compromises the time and space resolution characteristics of the active and passive microwave methods, and provides a surface soil water content estimation method with better time and space resolution.

Description

Surface soil water content assessment method based on improved MODIS vegetation water supply index
Technical Field
The invention belongs to the technical field of remote sensing image soil water content estimation, and particularly relates to a surface soil water content estimation method based on an improved MODIS vegetation water supply index.
Background
The soil water content plays an important role in the water and energy conversion of global water circle, biosphere and atmospheric circle, directly influences the water and energy exchange of land and atmospheric interface, is a key input parameter of hydrological model, climate model, ecological model and land process model, is also a key parameter factor for the research of hydrological, agricultural, climate, ecological and other fields, and plays an important role in a plurality of researches and applications. Improving the monitoring precision of the soil water content has important significance for agricultural production and water resource management.
For decades, researchers have proposed a number of surface soil moisture content estimation methods. Such as an on-site measurement method and a remote sensing method based on visible light, short-wave infrared, thermal infrared and microwave. The field measurement method is the most traditional method, including an earth boring soil weighing method, a neutron instrument method, a TDR (time domain reflectometry) method and the like, which have high precision in single-point measurement, but due to large spatial variability of soil moisture, poor station data representativeness, high acquisition cost, extreme time consumption, labor waste, high price and the like, the method is difficult to popularize in a large range. The remote sensing technology provides a soil water content estimation tool which can obtain higher time resolution and spatial resolution with lower time and expense cost. Among remote sensing methods, microwave remote sensing is the most effective soil moisture content estimation method. The microwave remote sensing technology has many advantages, for example, the microwave remote sensing technology is not limited by illumination and climate conditions, can work all day long and all weather, can penetrate cloud layers and has certain penetration capacity to the vegetation. Passive microwaves have the disadvantage, however, of having a low spatial resolution (20-40km), and active microwaves can provide higher spatial resolution of the monitored product, but with relatively low temporal resolution (16-25 days). Relatively speaking, the visible light/infrared data provided by MODIS are greatly influenced by atmosphere and cloud, but have relatively high time and spatial resolution, and the acquisition mode is simpler and cheaper, and can be used as an important supplementary means for microwave remote sensing.
Few researchers have reported that under conditions of high vegetation coverage, satellite remote sensing monitoring means are used for soil water content estimation, because satellite electromagnetic waves (such as optical/thermal infrared and C wave band) cannot penetrate through dense vegetation to reach the earth surface. Therefore, there is a need for an efficient soil moisture estimation method suitable for medium and high vegetation coverage. The optical/thermal infrared experience and the physical method have good correlation between the soil water content estimation aspect and the site actual measurement soil water content, and the temperature vegetation index TVDI and the vegetation water supply index VSWI are two most representative indexes. However, the applicable condition of TVDI is that most of the study area is cloud-free, and the study area is large enough (including wet bare soil, dry bare soil, water-stressed vegetation and vegetation with good water supply), but this is often difficult to satisfy, and the selection of the area range has a large impact on TVDI. Relative to TVDI, the vegetation water supply index VSWI is a simple and effective soil moisture estimation index that has been shown to correlate significantly with crop moisture, soil moisture under most climatic and surface coverage type conditions. However, because the normalized vegetation index NDVI is easily saturated in high vegetation coverage areas and has water stress hysteresis, the sensitivity of VSWI to monitor soil moisture content is reduced. In contrast, the surface temperature LST is relatively sensitive to water stress, however, due to the limitations of the mathematical properties of the ratiometric index itself, the surface temperature contributes less to the sensitivity of the index.
The following references are referred to herein:
[1]Cai,G.,Du,M.,&Liu,Y.(2011).Regional Drought Monitoring and Analysing Using MODIS data A Case study in Yunnan Province.Ccta 2010,345(1),243–251.https://doi.org/10.1007/978-3-642-18336-2.
[2]Carlson,T.N.,Gillies,R.R.,&Perry,E.M.(1994).A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover.Remote Sensing Reviews,9(February 2015),161–173.https://doi.org/10.1080/02757259409532220.
[3]Dong T，Meng L K and Zhang W..(2015).Analysis of the application of MODIS shortwave infrared water stress index in monitoring agricultural drought.Journal of R emote Sensing,19(2),319–327.
[4]Du Xiao,Wang Shixin,Zhou Yi,W.H.(2008).No Title.Geomatics and Information Science ofWuhan University,32(3).
[5]Fensholt,R.,&Sandholt,I.(2003).Derivation of a shortwave infrared water stress index from MODIS near-and shortwave infrared data in a semiarid environment.Remote Sensing of Environment,87(1),111–121.https://doi.org/10.1016/j.rse.2003.07.002.
[6]Ghulam,A.,Qin,Q.,&Zhan,Z.(2007).Designing of the perpendicular drought index.Environmental Geology,52(6),1045–1052.https://doi.org/10.1007/s00254-006-0544-2.
[7]Qin,Q.,Ghulam,A.,Zhu,L.,Wang,L.,Li,J.,&Nan,P.(2008).Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China.International Journal of Remote Sensing,29(7),1983–1995.https://doi.org/10.1080/01431160701355264.
[8]Sandholt,I.,Rasmussen,K.,&Andersen,J.(2002).A simple interpretation of the surface temperature/vegetation sindex space for assessment of surface moisture status.Remote Sensing of Environment,79(2),213–224.https://doi.org/10.1016/S0034-4257(01)00274-7.
[9]Wang,L.,Qu,J.J.,&Hao,X.(2008).Forest fire detection using the normalized multi-band drought index(NMDI)with satellite measurements.Agricultural and Forest Meteorology,148(11),1767–1776.https://doi.org/10.1016/j.agrformet.2008.06.005.
[10]Yao YunJun,Q.Q.(2011).Retrieval ofsoil moisture based on MOIDS shortwave infrared spectral feature.
[11]Zhang,H.,Chen,H.,Sun,R.,Yu,W.,Zou,C.,&Shen,S.(2009).The application of unified surface water capacity method in drought remote sensing monitoring,7472,74721M.https://doi.org/10.1117/12.829735.
[12]Zhang,N.,Hong,Y.,Qin,Q.,&Liu,L.(2013).VSDI:a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing.International Journal of Remote Sensing,34(13),4585–4609.https://doi.org/10.1080/01431161.2013.779046.
[13]Zhang D,Meng L,Qu J J,et al.Estimation of Surface Soil Moisture in Cornfields Using a Modified MODIS-Based Index and Considering Corn Growth Stages[J].IEEE Journal of Selected Topics inApplied Earth Observations and Remote Sensing,2017,10(12):5618-5631.
disclosure of Invention
In order to solve the technical problems, the invention deeply researches the characteristics of the vegetation water supply index VSWI mathematical expression, finds that the difference of the degree of influence of the variation of the numerator denominator on the vegetation water supply index VSWI is large, improves the influence of the denominator on the index by improving the numerator sensitivity and adjusting the index expression structure, and provides an improved MODIS vegetation water supply index-based soil water content evaluation method, namely a surface water content temperature index SWCTI.
The invention aims to provide an improved MODIS index-based soil water content estimation method suitable for medium and high vegetation coverage areas in an area scale. The invention adopts the following ideas:
the soil water content estimation method comprises a remote sensing method and an on-site measurement method, and the on-site measurement method has high precision, is discontinuous in space and high in cost, and is difficult to apply to soil water content measurement and calculation in a large area. Compared with the field measurement method based on ground stations, the satellite remote sensing technology can invert the soil water content with space continuity in large space scale, and widely-commercialized global soil water content products are produced, and mainly take passive microwave products as the main products. However, passive microwave products have poor spatial resolution, while active microwave products have higher spatial resolution but lower temporal resolution. Compared with the microwave method, the visible light/infrared method based on MODIS has higher time and spatial resolution, and can be used as an important supplement of the microwave method. VSWI in the visible light/infrared method is a common soil water content remote sensing index, and is suitable for medium and high vegetation coverage areas, however, the inversion performance is limited by a normalized vegetation index NDVI, and meanwhile, due to the limitation of the characteristics of an index mathematical expression, the influence degree of the surface temperature LST on the index is small, and the advantage that the surface temperature is sensitive to vegetation water is difficult to exert. Therefore, the invention comprehensively considers the characteristics of the visible light/infrared method and the mathematical characteristics of the remote sensing index, constructs an estimation method of the surface soil water content based on the improved MODIS temperature vegetation index, and obtains the surface soil water content with high space-time resolution on the regional scale.
The method comprises the following steps:
step 1, MODIS remote sensing data preprocessing of an experimental area comprises the following substeps,
step 1.1, performing quality control operation on each pixel in the surface reflectivity data of the 1-7 wave bands in MOD09A1, eliminating pixel data which are greatly influenced by cloud, aerosol and the like, and then resampling the reflectivity product of MOD09A1 to 1km resolution;
step 1.2, performing quality control operation on each pixel in daytime surface temperature (LST) data in MOD11A2, and selecting a pixel with better quality;
step 2, calculating a surface water content index SWCI and a normalized vegetation index NDVI of the experimental area through combined operation among wave bands; wherein the surface water content index SWCI is calculated as follows,
Figure BDA0001711218120000041
in the formula, R6And R7Respectively, the reflectivity of the MODIS data in the 6 th and 7 th wave bands;
the normalized vegetation index NDVI is calculated as follows,
Figure BDA0001711218120000042
in the formula, R1And R2Respectively the reflectivity of the 1 st wave band and the 2 nd wave band of the MODIS data;
step 3, threshold segmentation of the bare soil and low vegetation coverage area and the medium and high vegetation coverage areas of the experimental area comprises the following substeps,
step 3.1, calculating the normalized vegetation index NDVI of the bare soil pixel, and setting the normalized vegetation index NDVI as the minimum normalized vegetation index NDVImin；
Step 3.2, determining the normalized vegetation index NDVI corresponding to the pure vegetation pixel through the leaf area index, and setting the normalized vegetation index NDVI as the maximum normalized vegetation index NDVImax；
Step 3.3, calculating the vegetation coverage Fvc, wherein the calculation formula is as follows,
Figure BDA0001711218120000051
in the formula NDVIminWith NDVImaxNDVI obtained from steps 3.1 and 3.2, respectivelyiIs the NDVI value of the pixel i; selection of NDVIiWhen the coverage area is a, the Fvc is used as a threshold value for distinguishing different vegetation coverage areas, the coverage area is higher than the threshold value and corresponds to a middle and high vegetation coverage area, and the coverage area is a bare soil and low vegetation coverage area when the coverage area is lower than the threshold value;
step 3.4, obtaining MODIS surface reflectivity (LSR) and surface temperature (LST), calculating NDVI and other remote sensing indexes, and dividing the experimental area into two types according to the vegetation coverage Fvc threshold, namely, an bare soil area, a low vegetation coverage area, a medium vegetation coverage area and a high vegetation coverage area;
step 4, selecting middle and high vegetation coverage areas according to Fvc thresholds of the bare soil, low vegetation and middle and high vegetation coverage areas in the step 3, and constructing an improved MODIS index, namely a surface water content temperature index SWCTI, through linear combination of SWCI and surface temperature LST, wherein the formula is as follows:
Figure BDA0001711218120000052
wherein SWCI is a surface water content index, and C is a surface temperature adjustment index.
Further, in step 1, the MOD09a1 and MOD11a2 are subjected to quality control operation by using a 'mask _ sds' tool of the LDOPE.
Further, the leaf area index in step 3.2 is provided by MODIS series product MOD15A2, and when the leaf area index is greater than 2, the vegetation coverage is almost 100%, and the corresponding NDVI is NDVImax。
Further, step 4 is first based on
Figure BDA0001711218120000053
Obtaining the optimal value C of the surface temperature adjustment indexopt，
Figure BDA0001711218120000054
When the surface temperature adjustment variable is taken as C, the goodness of fit between SWCTI and the actually measured soil water content data of the experimental area is CoptAdjusting the surface temperature to an optimal value when the fitting goodness of SWCTI and actually-measured soil water content data is highest, and then adjusting CoptThe optimal surface water content temperature index SWCTI for the experimental zone was obtained in equation (1).
Further, a is 0.3.
Compared with the prior art, the invention has the following characteristics and beneficial effects:
compared with a passive microwave product, the surface water content temperature index SWCTI provided by the invention has better spatial resolution and higher time resolution compared with an active microwave product. The method provided by the invention compromises the time and space resolution characteristics of the active and passive microwave methods, and provides a surface soil water content estimation method with better time and space resolution.
Compared with VSWI, the method disclosed by the invention has the advantages that the influence of the surface temperature LST on the sensitivity of the index is enhanced, the correlation of molecules on the soil water content is improved, and the operation is simple.
Drawings
FIG. 1 is a flow chart of improved MODIS index calculation;
FIG. 2 is an NDVI thresholding segmentation of bare soil, low vegetation and medium and high vegetation segments;
FIG. 3 is a linear fit of SWCTI in 8 and 9 months and the actual soil moisture content under sandy loam conditions, (a) is a scatter diagram of SWCTI in 8 months and sandy loam soil moisture content, and (b) is a scatter diagram of SWCTI in 9 months and sandy loam soil moisture content.
Detailed Description
The technical solutions and advantages of the present invention will be further described with reference to the accompanying drawings and the detailed description.
Step 1: preprocessing remote sensing data of an experimental region MODIS;
in the time period of 8 months and 1 day in 2010 to 12 months and 31 days in 2014 in MODIS series V005, 8 months and 1 day in 9 months and 30 days in 9 months every year, 8 days of clear sky and little cloud in Naqu region are adopted to synthesize a ground surface reflectivity product MOD09A1 and a ground surface temperature product MOD11A2 to calculate various remote sensing indexes. And the measured soil water content data of 0-5cm of a multi-scale observation network of the soil temperature and humidity in the middle of the Qinghai-Tibet plateau provided by the research institute of Qinghai-Tibet plateau of the Chinese academy of sciences is adopted as verification data.
MODIS surface reflectance (LSR) quality control and resampling
In order to ensure the data quality, the MODIS 1-7 waveband earth surface reflectivity data needs to be subjected to quality control, for each pixel in the 1-7 waveband earth surface reflectivity data in the MOD09A1, quality control operation is carried out by using a mask _ sds ' tool of LDOPE, pixels which are greatly influenced by cloud, aerosol and the like are removed, and the quality control data come from ' surf _ refl _ state _500m ' quality control data provided by the MOD09A 1. Table 1 shows the surface reflectance (LSR) quality control criteria. And the reflectivity product of MOD09a1 was resampled to 1km resolution using the MODIS Reprojection Tool (MRT).
TABLE 1 MODIS surface reflectivity quality control Standard
Figure BDA0001711218120000061
MODIS surface temperature (LST) quality control
Quality control of MODIS surface temperature (LST) data is required to ensure data quality. For each pel in the daytime surface temperature (LST) data in MOD11A2, a quality control operation was performed using the 'mask _ sds' tool of LDOPE to select the pel with better quality, the quality control data being derived from the 'LST _ Day _1 km' quality control data provided by MOD11A 2. Table 2 shows MODIS surface temperature (LST) quality control criteria.
TABLE 2 MODIS surface temperature quality control Standard
Figure BDA0001711218120000071
Step 2: calculating remote sensing indexes of the experimental area;
2.1, calculating the surface water content index SWCI through the combined operation among wave bands, wherein the calculation formula is as follows:
Figure BDA0001711218120000072
in the formula, R6And R7The reflectivity of the MODIS data in the 6 th and 7 th wave bands respectively.
2.2, calculating the normalized vegetation index NDVI through the combined operation among the wave bands, wherein the calculation formula is as follows:
Figure BDA0001711218120000073
in the formula, R1And R2Respectively, the reflectivity of the 1 st wave band and the 2 nd wave band of the MODIS data.
And step 3: threshold segmentation of bare soil, low vegetation coverage areas and medium and high vegetation coverage areas;
(a) calculating normalized vegetation index NDVI of the bare soil pixel, and setting the normalized vegetation index NDVI as the minimum normalized vegetation index NDVImin；
(b) Determining the normalized vegetation index NDVI corresponding to the pure vegetation pixel through the leaf area index (the leaf area index LAI is provided by MODIS series products MOD15A 2), and setting the normalized vegetation index NDVI as the maximum normalized vegetation index NDVImaxAccording to the relationship between the leaf area index and the vegetation coverage FVC, when the leaf area index is more than 2, the vegetation coverage is almost 100%, and the corresponding NDVI is NDVImax。
(c) Calculating vegetation coverage Fvc
Calculating the vegetation coverage Fvc, wherein the formula is as follows:
Figure BDA0001711218120000074
in the formula NDVIminWith NDVImaxObtained by the process of steps 3.1 and 3.2, NDVIiIs the NDVI value of pel i.
(d) According to the latitude and longitude of a station actually measured by a multi-scale observation network of the temperature and humidity of soil in the middle of Qinghai-Tibet plateau provided by research institute of Qinghai-Tibet plateau of Chinese academy of sciences, MODIS surface reflectivity (LSR) and surface temperature (LST) corresponding to the station in an experimental area of Naqu region are obtained through IDL programming, remote sensing indexes are calculated to serve as experimental data, the experimental data are divided into two types according to a vegetation coverage Fvc threshold, namely corresponding to bare soil, a low vegetation coverage area, a medium vegetation coverage area and a high vegetation coverage area, wherein when the NDVI is 0.3, the Fvc serves as a threshold for distinguishing different vegetation coverage areas, the medium vegetation coverage area and the high vegetation coverage area correspond to the threshold, and the bare soil and the low vegetation coverage area correspond to the threshold below the threshold.
And 4, step 4: calculating the NDVI and the Fvc threshold of the area according to the bare soil, the low vegetation coverage and the medium and high vegetation coverage in the step 3, selecting the medium and high vegetation coverage to construct an improved MODIS index, and adjusting the optimal value C of the index according to the ground surface temperatureoptAnd obtaining the optimal surface water content temperature index SWCTI of the experimental area, wherein the formula is as follows:
Figure BDA0001711218120000081
C＝Copt
or
Figure BDA0001711218120000082
Figure BDA0001711218120000083
C, adjusting the index of the earth surface temperature, wherein the value range is influenced by the earth surface temperature distribution of the experimental area, and the value is between the maximum value and the minimum value of the earth surface temperature of the experimental area;
Figure BDA0001711218120000084
when the surface temperature adjustment variable is taken as C, the goodness of fit between the SWCTI and the actually measured soil water content data is CoptAnd adjusting the variable of the earth surface temperature when the fitting goodness of the SWCTI and the actually measured soil water content data is the highest.
Fig. 1 provides a specific calculation of the modified MODIS index. In the figure, MOD09A1 is MODIS satellite 500m ground surface reflectivity product, and MOD11A2 is 1km ground surface temperature/reflectivity L3 product. By using a 'mask _ sds' tool of LDOPE and quality control data of 'surf _ refl _ state _500 m' provided by MOD09A1, the MOD09A11-7 waveband earth surface reflectivity data (LSR) is subjected to quality control according to the standard in Table 1, and the earth surface reflectivity of a pixel with better quality is obtained. The reflectivity product of MOD09a1 was resampled to 1km resolution using the MODIS Reprojection Tool (MRT). By using a 'mask _ sds' tool of LDOPE, quality control data 'LST _ Day _1 km' provided by MOD11A2 is adopted to perform quality control on MOD11A2 surface temperature data (LST) according to the standard in Table 2, and the daytime surface temperature of the pixel with better quality is obtained. The indices SWCI and NDVI were calculated according to step 2. And 3, dividing the sample data into data corresponding to the bare soil, the low vegetation coverage area and the medium and high vegetation coverage areas according to the threshold segmentation of the bare soil, the low vegetation coverage area and the medium and high vegetation coverage areas in the step 3. Finally, in the data corresponding to the medium and high vegetation coverage areas, the improved MODIS index SWCTI is obtained according to step 4 by using the SWCI and the LST.
Fig. 2 provides a method for calculating NDVI thresholds corresponding to bare soil, low vegetation coverage, and medium and high vegetation coverage.
Firstly, according to the method in the step 2.3, calculating NDVI by using MOD09A1 data, extracting the LAI (leaf area index) synthesized in 8 days by using MOD15A2 data, selecting 1800 pairs of LAI-NDVI data, and drawing a line box diagram. And (3) determining the NDVI when the vegetation is completely covered by analyzing the saturation point (NDVI is 0.71), wherein the NDVI generally tends to 0 according to the bare soil reflectivity spectral characteristics, so that the value of the bare soil NDVI is determined to be 0, calculating the vegetation coverage FVC according to the step 3, and dividing the vegetation coverage.
Figure 3 provides a plot of the linear fit effect of SWCTI versus measured soil moisture for 8 and 9 months.
And table 3 is a statistical table of the fitting effect of the index and other remote sensing indexes on the actually measured soil water content data of different soil types in the experimental area of 0-5cm, and the result shows that the fitting effect of the SWCTI and the actually measured data is best under various conditions relative to other remote sensing indexes. The coefficient of determination R of the fitting of the index and the soil water content2And 8 months is higher than 9 months, and the sandy loam condition is higher than the silty clay loam condition. The best correlation with measured soil moisture content is: under sandy loam condition of 8 months, R20.607 (R0.779), in case of sandpaper loam in month 9, R20.4734 (R0.688). Under the condition of not distinguishing the soil types, the decision coefficients of SWCTI and actually-measured soil water content fitting in the 8 th month and the 9 th month are respectively R20.4828(R ═ 0.695), R2 ═ 0.4244(R ═ 0.652), SWCI andthe LST is a composition index of the SWCTI and participates in the calculation of the SWCTI, and the result shows that the SWCTI is improved relative to the SWCI and the LST. In general, SWCTI has a high correlation with measured soil water content data.
TABLE 3 model fitting Effect
Figure BDA0001711218120000091
Figure BDA0001711218120000101
Note: the data was tested for confidence level of 0.001
The method can fully utilize the advantages of a visible light/infrared measurement mode compared with a microwave measurement mode and an on-site measurement mode, breaks through the limitation of VSWI mathematical expressions, provides a surface soil water content estimation method with better time and space resolution, and is suitable for surface soil water content estimation of an area scale.
In a word, the method has excellent application effect and good application prospect.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (4)

1. A method for evaluating the water content of surface soil based on an improved MODIS vegetation water supply index is characterized by comprising the following steps:
step 1, MODIS remote sensing data preprocessing of an experimental area comprises the following substeps,
step 1.1, performing quality control operation on each pixel in the surface reflectivity data of the 1-7 wave bands in MOD09A1, eliminating pixel data greatly affected by cloud and aerosol, and then resampling the reflectivity product of MOD09A1 to 1km resolution;
step 1.2, performing quality control operation on each pixel in the daytime surface temperature LST data in MOD11A2, and selecting a pixel with better quality;
step 2, calculating a surface water content index SWCI and a normalized vegetation index NDVI of the experimental area through combined operation among wave bands; wherein the surface water content index SWCI is calculated as follows,
Figure FDA0003151086670000011
in the formula, R6And R7Respectively, the reflectivity of the MODIS data in the 6 th and 7 th wave bands;
the normalized vegetation index NDVI is calculated as follows,
Figure FDA0003151086670000012
in the formula, R1And R2Respectively the reflectivity of the 1 st wave band and the 2 nd wave band of the MODIS data;
step 3, threshold segmentation of the bare soil and low vegetation coverage area and the medium and high vegetation coverage areas of the experimental area comprises the following substeps,
step 3.1, calculating the normalized vegetation index NDVI of the bare soil pixel, and setting the normalized vegetation index NDVI as the minimum normalized vegetation index NDVImin；
Step 3.2, determining the normalized vegetation index NDVI corresponding to the pure vegetation pixel through the leaf area index, and setting the normalized vegetation index NDVI as the maximum normalized vegetation index NDVImax；
Step 33, calculating the vegetation coverage Fvc, the calculation formula is as follows,
Figure FDA0003151086670000013
in the formula NDVIminWith NDVImaxNDVI obtained from steps 3.1 and 3.2, respectivelyiIs the NDVI value of the pixel i; selection of NDVIiWhen the coverage area is a, the Fvc is used as a threshold value for distinguishing different vegetation coverage areas, the coverage area is higher than the threshold value and corresponds to a middle and high vegetation coverage area, and the coverage area is a bare soil and low vegetation coverage area when the coverage area is lower than the threshold value;
step 3.4, obtaining MODIS surface reflectivity LSR and daytime surface temperature LST, calculating NDVI and other remote sensing indexes, and dividing the experimental area into two types according to the vegetation coverage Fvc threshold, namely, an bare soil area, a low vegetation coverage area, a medium vegetation coverage area and a high vegetation coverage area;
step 4, selecting middle and high vegetation coverage areas according to Fvc thresholds of the bare soil, low vegetation and middle and high vegetation coverage areas in the step 3, and constructing an improved MODIS index, namely a surface water content temperature index SWCTI, through linear combination of SWCI and surface temperature LST, wherein the formula is as follows:
Figure FDA0003151086670000021
in the formula, SWCI is a surface water content index, and C is a surface temperature adjustment index;
in step 4, first according to
Figure FDA0003151086670000022
Obtaining the optimal value C of the surface temperature adjustment indexopt，
Figure FDA0003151086670000023
When the surface temperature adjustment variable is taken as C, the goodness of fit between SWCTI and the actually measured soil water content data of the experimental area is CoptAdjusting the surface temperature to an optimal value when the fitting goodness of SWCTI and actually-measured soil water content data is highest, and then adjusting CoptThe optimal surface water content temperature index SWCTI for the experimental zone was obtained in equation (1).
2. The method for evaluating the water content of the surface soil based on the improved MODIS vegetation water supply index as claimed in claim 1, wherein: in step 1, MOD09a1 and MOD11a2 are subjected to quality control operations by using a 'mask _ sds' tool of LDOPE.
3. The method for evaluating the water content of the surface soil based on the improved MODIS vegetation water supply index as claimed in claim 1, wherein: the leaf area index in step 3.2 is provided by MODIS series product MOD15A2, when the leaf area index is greater than 2, the vegetation coverage is almost 100%, and the corresponding NDVI is NDVImax。
4. The method for evaluating the water content of the surface soil based on the improved MODIS vegetation water supply index as claimed in claim 1, wherein: the value of a is 0.3.
CN201810683462.6A 2018-06-28 2018-06-28 Surface soil water content assessment method based on improved MODIS vegetation water supply index Active CN108876172B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810683462.6A CN108876172B (en) 2018-06-28 2018-06-28 Surface soil water content assessment method based on improved MODIS vegetation water supply index

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810683462.6A CN108876172B (en) 2018-06-28 2018-06-28 Surface soil water content assessment method based on improved MODIS vegetation water supply index

Publications (2)

Publication Number Publication Date
CN108876172A CN108876172A (en) 2018-11-23
CN108876172B true CN108876172B (en) 2021-09-03

Family

ID=64295211

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810683462.6A Active CN108876172B (en) 2018-06-28 2018-06-28 Surface soil water content assessment method based on improved MODIS vegetation water supply index

Country Status (1)

Country Link
CN (1) CN108876172B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110852474B (en) * 2019-09-24 2020-11-06 广州地理研究所 Land water reserve prediction method, device and equipment based on decision tree algorithm
CN110646587B (en) * 2019-09-29 2020-10-09 武汉大学 High-resolution agricultural drought monitoring method and device combining multi-source remote sensing data
CN115346120B (en) * 2022-08-16 2023-06-20 中国科学院空天信息创新研究院 Grassland biomass and carbon fixation remote sensing estimation method thereof
CN115481368B (en) * 2022-09-29 2023-05-19 河北省科学院地理科学研究所 Vegetation coverage estimation method based on full remote sensing machine learning

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101187630A (en) * 2007-12-05 2008-05-28 北京大学 Agricultural drought monitoring method
EP3158302B1 (en) * 2014-06-18 2021-12-29 Innopix, Inc. Spectral imaging system for remote and noninvasive detection of target substances using spectral filter arrays and image capture arrays
US9721181B2 (en) * 2015-12-07 2017-08-01 The Climate Corporation Cloud detection on remote sensing imagery
CN108169161B (en) * 2017-12-12 2019-12-24 武汉大学 Corn planting area soil humidity assessment method based on improved MODIS index

Also Published As

Publication number Publication date
CN108876172A (en) 2018-11-23

Similar Documents

Publication Publication Date Title
CN108876172B (en) Surface soil water content assessment method based on improved MODIS vegetation water supply index
Zhu et al. Analysis of remotely-sensed ecological indexes' influence on urban thermal environment dynamic using an integrated ecological index: a case study of Xi’an, China
Ghulam et al. Designing of the perpendicular drought index
Xu et al. Effect of off-zenith observations on reducing the impact of precipitation on ground-based microwave radiometer measurement accuracy
Pan et al. Extracting desertification from Landsat TM imagery based on spectral mixture analysis and Albedo-Vegetation feature space
CN109871637B (en) Near-ground air temperature estimation method under cloud-sky condition
Yang et al. Validation of a dual-pass microwave land data assimilation system for estimating surface soil moisture in semiarid regions
Zhang et al. Assimilating AMSU-A radiances in the TC core area with NOAA operational HWRF (2011) and a hybrid data assimilation system: Danielle (2010)
Wang et al. A multilayer soil moisture dataset based on the gravimetric method in China and its characteristics
CN113553907A (en) Forest ecological environment condition evaluation method based on remote sensing technology
Rui et al. Assimilation of remote sensing and crop model for LAI estimation based on ensemble Kaiman Filter
Zhang et al. Comprehensive evaluation of mainstream gridded precipitation datasets in the cold season across the Tibetan Plateau
CN111079835A (en) Himapari-8 atmospheric aerosol inversion method based on deep full-connection network
Song et al. Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model
Wu et al. Remotely sensed estimation and mapping of soil moisture by eliminating the effect of vegetation cover
CN112014542A (en) Method and device for making soil moisture map of vegetation coverage area, storage medium and equipment
CN114486783A (en) Winter wheat field soil moisture inversion method based on unmanned aerial vehicle multi-source remote sensing
Tian et al. Comprehensive validation of seven root zone soil moisture products at 1153 ground sites across China
Liu et al. Multifractal detrended fluctuation analysis of regional precipitation sequences based on the CEEMDAN-WPT
Singh et al. Spatial and temporal variability of soil moisture over India using IRS P4 MSMR data
Zhao et al. Evaluation of the ERS scatterometer-derived soil water index to monitor water availability and precipitation distribution at three different scales in China
Zhang et al. A Spatial Downscaling Method for Solar-Induced Chlorophyll Fluorescence Product Using Random Forest Regression and Drought Monitoring in Henan Province
Hu et al. Monitoring spatial patterns and changes of surface net radiation in urban and suburban areas using satellite remote-sensing data
Xie et al. Complement time-series UAV spectral data based on Ambrals kernel-driven model to monitor soil moisture content
Zheng et al. Water vapor estimation based on 1-year data of E-band millimeter wave link in North China

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant