Application prospects in the Northwestern Agricultural Development Wang Pengxin, Gong Jianya (State Key Laboratory of Surveying, Mapping and Remote Sensing Information Engineering of Wuhan University, Wuhan 430079, Hubei, China) Li Xiaowen (Department of Resources and Environmental Science, Beijing Normal University, Beijing 100875), independently, can also complement each other for agriculture Production and development provide strong technical support. They can quickly and accurately obtain multi-dimensional information of agricultural production systems, especially time-dimensional information, can comprehensively manage and process attribute data and spatial data, and can provide corresponding technical services for agricultural production decision-making, thus accurately guiding Agricultural production promotes the healthy development of the ecological environment. The application prospects of 3S technology in agricultural development in Northwest China were discussed, focusing on the remote sensing inversion of soil moisture and the dynamic monitoring of drought and desertification.
1 Introduction They are the three major supporting technologies of spatial information acquisition, management, analysis and application in the earth observation system. They can quickly and accurately locate spatial entities, and at the same time obtain information macroscopically and carry out spatial information at specific locations. Comprehensive analysis. 3S's precision agricultural technology is one of the three cutting-edge projects in the agricultural high-tech field of the S863 plan (a medium- and long-term plan for the national high-tech research and development in the 21st century). Remote sensing refers to the remote from the remote The platform is used to acquire, process, and apply the science and technology of target feature information. It uses special optical, electronic and electro-optical detection instruments to record the electromagnetic wave signals radiated or reflected by distant objects, and then processed by optical or computer processing to become an image that can be recognized by the human eye, thereby revealing the detection The nature of the object and its changing law. Remote sensing technology can provide us with observation data of different spatial and temporal resolutions and spectral resolutions, which is very useful for dynamic monitoring of agricultural resources and crop growth. GIS is a spatial information system, used to collect, store, analyze and disseminate information on the surface of the earth. It is a system that integrates computer hardware and software, time, personnel, organizations and institutional arrangements.
GPS is a satellite-based radio time measurement positioning and navigation system, which is mainly used to extract the spatial position of the target in real time and so on. At present, the positioning accuracy can reach tens of meters, and the difference technology can be used to make the positioning accuracy up to 1 m. This article discusses the application prospects of 3S technology in agricultural development in the northwest region from the reality of drought in the northwest region and fragile ecological environment The paper focuses on the application of NO AA satellite enhanced very high resolution radiometer soil moisture and dynamic monitoring of drought and desertification.
2 Remote sensing inversion of soil moisture Since Manabe introduced the interactive concept of soil moisture in the global climate model in 1969, soil moisture has been considered as one of the important land surface parameters. The water content of the soil surface controls the amount of solar radiation into latent heat flux and sensible heat flux and their proportion in solar radiation, and also controls the infiltration of precipitation and surface runoff. The spatial and temporal variability of the soil moisture distribution pattern is very large, from the time scale, from a few minutes to a few months, from the spatial scale, from a few centimeters to thousands of kilometers, and soil water deficit often manifests as a large area, Therefore, the only feasible way to monitor soil moisture deficit and determine its range is to use satellite remote sensing technology and application of remote sensing technology. Using remote sensing technology to understand, observe and simulate soil moisture distribution patterns is the most attractive and challenging to study the characteristics of land surface jobs.
The essence of thermal inertia is a characteristic of the ground object to prevent its temperature from changing, and it is the inherent thermal characteristic of the object itself. When the amount of heat absorbed or released by a feature is equal, the amplitude of the feature's temperature change is inversely proportional to the feature's thermal inertia. Therefore, in the feature's Sunday temperature change, the thermal inertia plays a decisive role [4]. Price systematically elaborated the thermal inertia method and the remote sensing imaging of thermal inertia, and proposed the concept of apparent thermal inertia [5]. Sui Hongzhi et al. And Ma Ainai used NO AA data to study the remote sensing information model of soil water content in North China Plain.
2.1 Calculation of brightness temperature The surface temperature of the land obtained on the satellite sensor is called brightness temperature. The 4th and 5th bands of AV HRR can provide remote sensing data of thermal infrared bands, and the band ranges are 10.3 ～ 11.3μm and 11.5 ～ 12.50μm respectively. The Planck spectrum radiation equation can be used for solving after conversion The brightness temperature of the 4th and 5th bands: where: C is the center wave number of the i-band filter, the unit is cm is the i-band brightness temperature, and the unit is the i-band spectral radiant energy, which can be obtained by the following formula : In the formula: DN is the digital value obtained by the 4th and 5th band sensors, c and d are constants (scaling coefficients), which can be obtained from the data provided by AV HRR.
2. 2 Inversion of land surface temperature Land surface temperature refers to the surface temperature of the ground, and for bare soil, it is the surface temperature of the soil. The principle of retrieving land surface temperature using remote sensing technology is based on the spectral infrared radiation of the surface thermal infrared band. The theoretical basis is that the radiation energy emitted by the ground surface increases rapidly with the increase of its surface temperature. The land surface temperature in reverse performance refers to the average temperature on the pixel scale. In the past few decades, using the 4th and 5th band data has developed a variety of windowing algorithms for retrieving land surface temperature. Although the calculation methods of these algorithms are very different, they can be divided into two categories according to their expressions.
2. 2. The general form of the split-window algorithm The general expression of the split-window algorithm is: where: T is the land surface temperature A and B is the coefficient, which is caused by the atmospheric and other related factors on the 4th and 5th band thermal spectrum radiation and The impact of its transmission is determined. This algorithm was proposed by Price [8] and is widely used in the study of land surface temperature. The author made several simplifications of the effect of the atmosphere on the radiation transmission from the ground to the sensor: Since the mid-1980s, in order to improve the accuracy of the windowing algorithm, the Price method has been modified several times. Can refer to literature [9 2. 2. 2 other forms of split window algorithm other forms of split window algorithm refers to using different methods from formula (3) to obtain the land surface temperature. Becker et al. Proposed a partial window algorithm [12], which is suitable for the case where the viewing angle is less than 46 ° from the nadir.
Among them: A, P and M are undetermined coefficients, they are affected by many factors in the radiation transmission process from the ground to the sensor. For A, A and are calculated by the following formulas: the ground specific emissivity in the 4th and 5th bands, respectively. It can be seen from equations (6) and (7) that the sum is a function of the ground specific emissivity (X).
Kerr et al. Proposed a split-window algorithm for land surface temperature under arid and semi-arid conditions [13]. In arid and semi-arid environments, vegetation cover is sparse, and the land surface is composed of vegetation cover and bare soil.
According to equation (8), the surface temperature T of the vegetation canopy and the surface temperature of the bare soil can be estimated. Among them: a is the coefficient, which represents the total influence of the atmosphere and the influence of the ground specific emissivity.
Under arid and semi-arid conditions, the relationship between land surface temperature and T can be expressed by the following formula: where: C is the fraction of vegetation coverage under arid and semi-arid conditions, which can be estimated by the normalized vegetation index (NDV I): is the area under study The minimum value of N DVI when bare soil is the maximum value of N DVI when the entire pixel scale is covered by vegetation. N DVI is the current value of the normalized vegetation index (N DV I) at the pixel scale (NDV I is defined in Part 2 of this article) ).
2.3 Establishment of soil moisture inversion model The establishment of empirical or semi-empirical models of soil surface moisture content and thermal inertia can be used to invert soil surface water content in large areas.
The soil water content (W) is a function of the thermal inertia P and the relative density of the soil, that is, by extracting the soil relative density d and calculating the soil thermal inertia P from the AV HRR data, the w, s can be obtained according to the empirical formula, thereby establishing Soil moisture model based on AV HRR data. For a certain area, d can be regarded as constant. In this paper, a logarithmic model is used as an example to establish an empirical relationship model between W and P at the regional level: where: a and b are undetermined coefficients.
2.3.1 Relationship between soil thermal inertia and soil apparent thermal inertia Under the condition of simplifying the surface boundary conditions, the Fourier series method is used to solve the one-dimensional heat conduction equation of the temperature change on Sunday, and the true The relationship between thermal inertia (P) and apparent thermal inertia (AT I) where: S is the solar constant, C is the atmospheric transmittance of solar radiation, and is a function of the solar declination and local latitude, therefore, S basically represents the surface The net solar radiation k that can be received during the day is constant for the earth's rotation angular velocity B.
2.3.2 Calculation of apparent thermal inertia The definition of soil apparent thermal inertia is: where: T is the albedo and ΔT is the temperature difference between day and night.
2. 3. 3 Calculation of albedo and temperature difference between day and night Albedo is defined as the reflectivity of the whole wave band of the ground object. Since the amount of solar energy is mainly concentrated in a very narrow range of 0.25 to 1.5 μm, the reflectance of the entire wave band of the ground object can be approximately calculated using the reflectance of the visible light and near infrared bands. He et al. Derived the empirical formula for obtaining albedo using AV HRR remote sensing data as follows: d represents the reflectivity in bands 1 and 2 respectively (refer to Part 2 of this article).
The temperature difference between the day and night can be obtained from the surface temperature of the land inversely reflected by the NO AA satellite during daytime and night transit.
3 Dynamic monitoring of drought 3.1 Conditional vegetation index method Vegetation index is a quantitative method to describe vegetation coverage or vegetation vitality, it is based on the strong absorption characteristics of plant chlorophyll at 0.69μm, through plants in visible light (AV HRR Band 1) and near infrared band (AV HRR band 2) reflectance ratio or linear combination to achieve the expression of vegetation status information. There are many expressions of vegetation index, the most commonly used is the normalized vegetation index. The definition of the normalized vegetation index is: where: d represents the reflectivity of the first and second bands, which can be used by the AV HRR first and second band sensors The obtained digital value is calculated.
The calculation method is similar to equation (2). The conditional vegetation index (V egetation information, and contains the historical change information of NDV I, its connotation is defined as: represents the minimum value of the year under study.
The denominator part of the above formula is the dynamic change range of the vegetation index, which reflects the habitat of the local vegetation in a certain sense. The molecular part of the habitat represents the local meteorological information in a certain sense. If the difference between N DVI and is small or close to zero, it means that the period The crops are growing very poorly.
If the values ​​of several consecutive decades in the same area are less than a certain range, indicating that the vegetation condition has deteriorated, it indicates that there is a drought situation and the possibility of drought development in the area.
3.2 Conditional temperature index method Arid and semi-arid areas in the northwest, where precipitation is scarce and unevenly distributed, during the rainy season, due to the influence of thin clouds in the atmosphere, it can be reduced within a certain period, resulting in the illusion of drought. In order to eliminate this effect, the conditional temperature index can be used. The definition of Temperature is similar to the definition, but it emphasizes the relationship between temperature and N DVI, that is, high temperature is harmful to plant growth.
Among them: T is the value of the brightness temperature of the fourth band in the first period of a year, and T and T represent the historical maximum and minimum values ​​of the first period, respectively.
The reason for the selection is that, compared to the fifth band, the radiance obtained by the satellite sensor in the fourth band is less affected by water vapor in the atmosphere. The smaller the TCI, the more drought.
4 Dynamic monitoring of desertification In arid and semi-arid environments, low vegetation coverage has little protection of the soil, resulting in severe erosion processes (wind and water erosion) and land degradation. The so-called desertification refers to the land degradation caused by various factors including climate variability and human activities in arid, semi-arid and semi-humid arid areas [17], which is the result of the interaction of human unreasonable economic activities and fragile ecological environment . The manifestation of desertification in terms of main land use types is grassland degradation, arable land degradation and forest land degradation, which reduces the arable land resources, grassland quality and ecological environment. The northwest is the area where sandstorms are the most serious. A few days ago, the land area of ​​desert, Gobi and sandstorms totaled 1.74 million km, accounting for 18. 2 [18] of the national land area. For example, the long-term over-exploitation of water resources in the agricultural reclamation of the upper and middle reaches of the Heihe River has caused the shrinkage of the lower Inner Mongolia Ejina Oasis and the dryness of the Juyan Sea. Today ’s Minqin Oasis is suffering from the ecological crisis caused by the development of upstream Wuwei. High-resolution satellite data, such as NO AA, can be used to classify remote sensing data on the basis of obtaining vegetation coverage, which can then be used for large-scale desertification monitoring applications. High spatial resolution satellite data, such as Landsat M SS / TM Detailed investigation of desertification. The author used Landsat M SS / TM data to study the grassland degradation characteristics of the typical steppe in Inner Mongolia for more than 20 years and found that the application of remote sensing images can not only study the degree of grassland degradation, but also the spatial and temporal distribution characteristics of degraded grassland. The application of GPS technology can accurately locate desertified areas, especially their borders.
Application technology Real-time field storage, analysis, management and update of spatial data and attribute data of desertified areas. The data source of GIS database is mainly obtained by RS and GPS technology.
[6] Sui Hongzhi, Tian Guoliang, Li Jianjun, etc. Monitoring soil moisture by thermal inertia method [A]. Tian Guoliang. Remote Sensing Dynamic Research [C]. Beijing: Science Press, 1990.
[7] Ma Ainai. Remote Sensing Information Model [M]. Beijing: Peking University Press, 1997.
[18] Lu Dadao, Xue Fengxuan. 1997 China Regional Development Report [M]. Beijing: Commercial Seal [19] Wang Xiaofang, Shen Maoxiang. Walking out of the misunderstanding of China's desertification prevention and control: when local improvement and overall expansion reverse the situation [M]. Ministry of Science and Technology of the People's Republic of China
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