CN102103077A

CN102103077A - MODIS data-based agricultural drought monitoring method

Info

Publication number: CN102103077A
Application number: CN 200910248457
Authority: CN
Inventors: 王瑞杰; 宇万太; 覃志豪
Original assignee: Institute of Applied Ecology of CAS
Current assignee: Institute of Applied Ecology of CAS
Priority date: 2009-12-16
Filing date: 2009-12-16
Publication date: 2011-06-22

Abstract

本发明公开了一种基于MODIS数据的农业干旱监测方法。该农田干旱监测，利用作物供水指数和降雨距平指数来确定农业旱情指数。在进行旱情监测时利用MODIS数据反演植被指数和地表温度，利用植被指数和地表温度计算作物供水指数。利用降水数据计算降水距平指数。最后对旱情指数进行级别划分来确定干旱严重程度。The invention discloses an agricultural drought monitoring method based on MODIS data. The farmland drought monitoring uses the crop water supply index and the rainfall anomaly index to determine the agricultural drought index. During drought monitoring, MODIS data was used to invert vegetation index and surface temperature, and vegetation index and surface temperature were used to calculate crop water supply index. Calculate the precipitation anomaly index using the precipitation data. Finally, the drought index is graded to determine the severity of the drought.

Description

An Agricultural Drought Monitoring Method Based on MODIS Data

技术领域technical field

本发明涉及一种农田干旱监测方法。The invention relates to a farmland drought monitoring method.

背景技术Background technique

干旱的发生过程是潜在的，不容易发现；农田干旱的发生特征是影响范围大，带来严重的灾难性后果和经济损失；研究、评价干旱发生和发展的过程，可以采取相应的抗旱防灾减灾措施，减少农业灾害损失。随着遥感技术的发展，遥感以其动态、实时、多光谱、廉价的优势，为旱情监测开辟了新的途径。遥感获取的植被指数和地表温度是描述地球表面特征的两个十分重要参数，因此干旱发生时，可通过植被指数或地表温度的变化来揭示作物生理异常特征，间接反映农田水热胁迫状况。按照遥感数据所使用的波段可分为：可见光、近红外、热红外、微波等。由于所使用的波段不同，产生了众多模型和方法。如水分亏缺指数模型、温度植被干旱指数模型等。虽然用于农业旱灾监测的模型很多，但大多数只是实验性的研究。因此探索一种相对精确的农田干旱监测方法非常必要。The occurrence process of drought is latent and not easy to discover; the occurrence of drought in farmland is characterized by a wide range of influence, which brings serious catastrophic consequences and economic losses; research and evaluation of the process of occurrence and development of drought can take corresponding measures to combat drought and prevent disasters Disaster mitigation measures to reduce agricultural disaster losses. With the development of remote sensing technology, remote sensing has opened up a new way for drought monitoring with its dynamic, real-time, multi-spectral and cheap advantages. The vegetation index and surface temperature obtained by remote sensing are two very important parameters to describe the characteristics of the earth's surface. Therefore, when drought occurs, changes in vegetation index or surface temperature can be used to reveal the abnormal characteristics of crop physiology and indirectly reflect the water and heat stress of farmland. According to the wave bands used in remote sensing data, it can be divided into: visible light, near infrared, thermal infrared, microwave, etc. Due to the different bands used, many models and methods have been produced. Such as water deficit index model, temperature vegetation drought index model, etc. Although there are many models for agricultural drought monitoring, most of them are only experimental studies. Therefore, it is necessary to explore a relatively accurate monitoring method for farmland drought.

发明内容Contents of the invention

本发明的目的是提供一种利用遥感技术进行地表干旱监测的新方法，该农田干旱监测，利用作物供水指数和降雨距平指数来确定农业旱情指数。在进行旱情监测时利用MODIS数据反演植被指数和地表温度，利用植被指数和地表温度计算作物供水指数。利用降水数据计算降水距平指数。最后对旱情指数进行级别划分来确定干旱严重程度。。The purpose of the present invention is to provide a new method for surface drought monitoring using remote sensing technology. The farmland drought monitoring uses crop water supply index and rainfall anomaly index to determine agricultural drought index. During drought monitoring, MODIS data was used to invert vegetation index and surface temperature, and vegetation index and surface temperature were used to calculate crop water supply index. Calculate the precipitation anomaly index using the precipitation data. Finally, the drought index is graded to determine the severity of the drought. .

本发明采用基于植被指数和地表温度的农业干旱监测模型来进行监测，在监测的过程中对模型生态参数进行改进，所表述地表旱情的相应参数按照如下方法确定：以被监测农田地块所对应的MODIS各波段像元的光谱反射率数据为基础；The present invention adopts the agricultural drought monitoring model based on the vegetation index and the surface temperature to monitor. During the monitoring process, the ecological parameters of the model are improved, and the corresponding parameters of the expressed surface drought are determined according to the following method: the corresponding parameters of the monitored farmland block Based on the spectral reflectance data of MODIS pixels in each band;

一种基于MODIS数据的农业干旱监测方法，主要是借助遥感技术手段在大区域尺度上探讨一种客观、动态、实时、准确、易于实现的监测方法。在本方法中采用基于植被指数和地表温度的农业干旱监测模型来进行监测，在监测的过程中对模型生态参数进行改进，所表述地表旱情的相应参数按照如下方法确定：An agricultural drought monitoring method based on MODIS data mainly uses remote sensing technology to explore an objective, dynamic, real-time, accurate and easy-to-implement monitoring method on a large regional scale. In this method, the agricultural drought monitoring model based on vegetation index and surface temperature is used for monitoring. During the monitoring process, the ecological parameters of the model are improved, and the corresponding parameters of the described surface drought are determined according to the following method:

以被监测农田地块所对应的MODIS各波段像元的光谱反射率数据为基础；Based on the spectral reflectance data of MODIS pixels in each band corresponding to the monitored farmland plot;

要得到EVI、T_s、VSWI和SDI参数，对所选波段的遥感数据进行统一的预处理，主要包括按常规方法进行(在ERDAS或ENVI等遥感软件中处理)辐射量计算、几何校正、云检测、投影转换，以便进一步进行匹配计算；在计算降雨距平指数时，首先应将降雨数据进行插值处理，得到降水距平图；插值完成后统一转成栅格数据，以便与遥感数据对比分析。To obtain EVI, T _s , VSWI and SDI parameters, the remote sensing data of the selected bands should be uniformly preprocessed, mainly including conventional methods (processing in remote sensing software such as ERDAS or ENVI) radiation amount calculation, geometric correction, cloud Detection and projection conversion for further matching calculation; when calculating the rainfall anomaly index, the rainfall data should be interpolated first to obtain the precipitation anomaly map; after the interpolation is completed, it will be uniformly converted into raster data for comparative analysis with remote sensing data .

1)植被指数的计算：1) Calculation of vegetation index:

$EVI EVI = = G G \times \times \frac{{ρ ρ}_{NIR NIR} - - {ρ ρ}_{Red Red}}{{ρ ρ}_{NIR NIR} + + {C C}_{11} \times \times {ρ ρ}_{Red Red} - - {C C}_{22} \times \times {ρ ρ}_{Blue Blue} + + L L}$

EVI为增强植被指数，ρ_NIR、ρ_Red和ρ_Blue分别为被监测农田地块所对应的MODIS的近红外波段、红光波段和蓝光波段像元的光谱反射率；L为背景调整项；C₁和C₂为拟合系数；G为增益因子；在计算MODIS-EVI时，L＝1，C₁＝6，C₂＝7.5，G＝2.5；分别计算出被监测农田地块所对应的遥感影像各像元点的EVI数据；EVI is the enhanced vegetation index, ρ _NIR , ρ _Red and ρ _Blue are the spectral reflectance of the pixels in the near-infrared band, red band and blue band of MODIS corresponding to the monitored farmland plot respectively; L is the background adjustment item; C ₁ and C ₂ are the fitting coefficients; G is the gain factor; when calculating MODIS-EVI, L=1, C ₁ =6, C ₂ =7.5, G=2.5; respectively calculate the corresponding EVI data of each pixel point of the remote sensing image;

2)地表温度反演2) Inversion of surface temperature

地表温度是农业旱灾监测的一个基本参数，其计算方法采用覃志豪等(覃志豪，高懋芳，秦晓敏等，农业旱灾监测中的地表温度遥感反演方法-以MODIS数据为例[J].自然灾害学报，2005，14(4)：64-71.)提出的基于中大尺度MODIS数据的劈窗算法，其计算公式为：Surface temperature is a basic parameter of agricultural drought monitoring, and its calculation method adopts Qin Zhihao et al. 2005, 14(4): 64-71.) proposed a window-splitting algorithm based on medium and large-scale MODIS data, and its calculation formula is:

T_s＝A₀+A₁T₃₁-A₂T₃₂ T _s =A ₀ +A ₁ T ₃₁ -A ₂ T ₃₂

A₀＝E₁a₃₁-E₂a₃₂ A ₀ =E ₁ a ₃₁ -E ₂ a ₃₂

A₁＝1+A+E₁b₃₁ A ₁ =1+A+E ₁ b ₃₁

A₂＝A+E₂b₃₂ A ₂ =A+E ₂ b ₃₂

A＝D₃₁/E₀ A＝D ₃₁ /E ₀

E₁＝D₃₂(1-C₃₁-D₃₁)/E₀ E ₁ =D ₃₂ (1-C ₃₁ -D ₃₁ )/E ₀

E₂＝D₃₁(1-C₃₂-D₃₂)/E₀ E ₂ =D ₃₁ (1-C ₃₂ -D ₃₂ )/E ₀

E₀＝D₃₂C₃₁-D₃₁C₃₂ E ₀ ＝D ₃₂ C ₃₁ -D ₃₁ C ₃₂

C_t＝ε_iτ_i C _t =ε _i τ _i

D_t＝[1+(1-ε_i)τ_i]D _t ＝[1+(1-ε _i )τ _i ]

式中T_s是地表温度(K)，T₃₁和T₃₂分别是MODIS第31和32波段的亮度温度，A₀，A₁和A₂是劈窗算法的参数，a₃₁，b₃₁，a₃₁和b₃₂是常量，在地表温度0-50℃范围内分别可取a₃₁＝-64.60363，b₃₁＝0.440817，a₃₂＝-68.72575，b₃₂＝0.473453；其他中间参数的计算详见覃志豪等针对MODIS数据的分裂窗算法(覃志豪，高懋芳，秦晓敏等，农业旱灾监测中的地表温度遥感反演方法-以MODIS数据为例[J].自然灾害学报，2005，14(4)：64-71.)；然后利用公式分别计算出被监测农田地块所对应的遥感影像各像元点的T_s数据；In the formula, T _s is the surface temperature (K), T ₃₁ and T ₃₂ are the brightness temperature of the 31st and 32nd bands of MODIS respectively, A ₀ , A ₁ and A ₂ are the parameters of the window-splitting algorithm, a ₃₁ , b ₃₁ , a ₃₁ and b ₃₂ are constants, and a ₃₁ = -64.60363, b ₃₁ = 0.440817, a ₃₂ = -68.72575, b ₃₂ = 0.473453 can be taken respectively in the range of surface temperature 0-50°C; for the calculation of other intermediate parameters, please refer to Qin Zhihao et al. Split-window Algorithm for MODIS Data (Qin Zhihao, Gao Maofang, Qin Xiaomin, etc., Remote Sensing Retrieval Method of Land Surface Temperature in Agricultural Drought Monitoring—Taking MODIS Data as an Example[J]. Journal of Natural Disasters, 2005, 14(4): 64-71. ); Then use the formula to calculate the T _s data of each pixel point of the remote sensing image corresponding to the monitored farmland plot;

其中i是指被监测农田地块所对应的MODIS影像的第31和32波段，分别为i＝31或32；τ_i是被监测农田地块所对应的遥感影像各像元点的波段i的大气透过率，ε_i是被监测农田地块所对应的遥感影像各像元点的波段i的地表比辐射率。where i refers to the 31st and 32nd bands of the MODIS image corresponding to the monitored farmland plot, i=31 or 32 respectively; _τi is the band i of each pixel point of the remote sensing image corresponding to the monitored farmland plot Atmospheric transmittance, ε _i is the surface specific emissivity of the band i of each pixel point of the remote sensing image corresponding to the monitored farmland plot.

ε_t＝ε_iw+P_vR_vε_iv+(1-P_v)R_sε_is ε _t ＝ε _iw +P _v R _v ε _iv +(1-P _v )R _s ε _is

ε_iw、ε_iv和ε_is分别是被监测农田地块所对应的遥感影像各像元点的水体、植被和裸土在第波段i波段的地表比辐射率，分别取ε_31w＝0.99683，ε_32w＝0.99254，ε_31v＝0.98672，ε_32v＝0.98990，ε_31s＝0.96767，ε_31s＝0.97790；R_v和R_s分别是被监测农田地块所对应的遥感影像的植被和裸土的辐射比率，覃志豪等(Qin Z，Karnieli A.Progress in the Remote Sensing of Land SurfaceTemperature and Ground Emissivity Using NOAA-AVHRR[J].InternationalJournal of Remote Sensing，1999，20(12)：2367-2397.)，计算的R_v＝0.99240，R_s＝1.00744。P_v是被监测农田地块所对应的各像元点的植被覆盖率，通过植被指数进行估算：ε _iw , ε _iv and ε _is are the surface specific emissivity of the water body, vegetation and bare soil in the band i of each pixel point of the remote sensing image corresponding to the monitored farmland plot, respectively, and ε _31w = 0.99683, ε _32w ＝0.99254, ε _31v ＝0.98672, ε _32v ＝0.98990, ε _31s ＝0.96767, ε _31s ＝0.97790; R _v and R _s are the radiation ratios of vegetation and bare soil in the remote sensing image corresponding to the monitored farmland block, (Qin Z, Karnieli A. Progress in the Remote Sensing of Land Surface Temperature and Ground Emissivity Using NOAA-AVHRR[J]. International Journal of Remote Sensing, 1999, 20(12): 2367-2397.), calculated R _v = 0.99240, R _s = 1.00744. P _v is the vegetation coverage rate of each pixel point corresponding to the monitored farmland plot, which is estimated by the vegetation index:

${P P}_{v v} = = \frac{NDVI NDVI - - {NDVI NDVI}_{s the s}}{{NDVI NDVI}_{v v} - - {NDVI NDVI}_{s the s}}$

式中NDVI是被监测农田地块所对应的各像元点的植被指数，NDVI_v和NDVI_s分别是茂密植被覆盖和完全裸土像元的NDVI值，通常取NDVI_v＝0.674，NDVI_s＝0.039(孙睿，朱启疆.植被净第一性生产力模型及中国净第一性生产力的分析[J].北京师范大学学报(自然科学版)，1998，34：132-137.)；In the formula, NDVI is the vegetation index of each pixel point corresponding to the monitored farmland plot, NDVI _v and NDVI _s are the NDVI values of dense vegetation coverage and completely bare soil pixels respectively, usually NDVI _v = 0.674, NDVI _s = 0.039 (Sun Rui, Zhu Qijiang. Vegetation net primary productivity model and analysis of China's net primary productivity [J]. Journal of Beijing Normal University (Natural Science Edition), 1998, 34: 132-137.);

$NDVI NDVI = = \frac{{B B}_{22} - - {B B}_{11}}{{B B}_{22} + + {B B}_{11}}$

式中B₁和B₂分别是MODIS图像第1和2波段的反射率。where B ₁ and B ₂ are the reflectances of the first and second bands of the MODIS image, respectively.

大气透过率与大气水汽含量之间呈现接近线性关系，根据覃志豪等(The relationship between atmospheric transmittance and atmospheric water vapor content is nearly linear, according to Qin Zhihao et al. (

Qin Z，Karnieli A，Berliner P.A Mono- window Algorithm for RetrievingLand Surface Temperature from Landsat TM Data and Its ApplicationQin Z, Karnieli A, Berliner P.A Mono- window Algorithm for RetrievingLand Surface Temperature from Landsat TM Data and Its Application

当水汽含量在0.4～2.0g/cm²时：When the water vapor content is 0.4～2.0g/ ^cm2 :

τ₃₁＝0.99513-0.0808wτ ₃₁ =0.99513-0.0808w

τ₃₂＝0.99376-0.11369wτ ₃₂ =0.99376-0.11369w

当水汽含量在2～4.0g/cm²时：When the water vapor content is 2-4.0g/ ^cm2 :

τ₃₁＝1.08692-0.12759wτ ₃₁ =1.08692-0.12759w

τ₃₂＝1.07900-0.15925wτ ₃₂ =1.07900-0.15925w

当水汽含量在4.0～6.0g/cm²时：When the water vapor content is 4.0～6.0g/ ^cm2 :

τ₃₁＝1.07268-0.12571wτ ₃₁ =1.07268-0.12571w

τ₃₂＝0.93821-0.12613wτ ₃₂ =0.93821-0.12613w

计算大气水汽含量的公式为：The formula for calculating the water vapor content of the atmosphere is:

w＝[(α-lnT_w)/β]² w=[(α-lnT _w )/β] ²

其中w是被监测农田地块所对应的各像元点的大气水汽含量；T_w是被监测农田地块MODIS影像第18波段所对应的各像元点的反射率与第2波段所对应的各像元点的地面反射率的比值，α、β是参数，分别取α＝0.02，β＝0.651；Among them, w is the atmospheric water vapor content of each pixel point corresponding to the monitored farmland block; T _w is the reflectance of each pixel point corresponding to the 18th band of the MODIS image of the monitored farmland block corresponding to the second band The ratio of the ground reflectance of each pixel point, α, β are parameters, respectively take α=0.02, β=0.651;

3)作物供水指数的计算：3) Calculation of crop water supply index:

$VSWI VSWI = = \frac{EVI EVI}{{T T}_{s the s}}$

式中VSWI为被监测农田地块所对应的各像元点的作物供水指数；EVI为被监测农田地块所对应的MODIS影像各像元点的增强植被指数；T_s为被监测农田地块所对应的遥感影像各像元点的地表温度；In the formula, VSWI is the crop water supply index of each pixel point corresponding to the monitored farmland block; EVI is the enhanced vegetation index of each pixel point of the MODIS image corresponding to the monitored farmland block; T _s is the monitored farmland block The surface temperature of each pixel point of the corresponding remote sensing image;

4)对作物供水指数进行标准化：4) Standardize the crop water supply index:

SDI＝(VSWI-VSWI_d)/(VSWI_w-VSWI_d)×100％SDI＝(VSWI- _VSWId )/( _VSWIw - _VSWId )×100%

式中SDI是标准化后被监测农田地块所对应的遥感影像各像元点作物的供水指数，取0～100％，其中SDI＝0表示严重干旱，SDI＝100％表示非常湿润；VSWI_d和VSWI_w分别为最旱时和最湿润时的作物供水指数；EVI的分级步长可设为d，当d＝0.05时，适宜作物生长的温度空间为20℃～45℃时，VSWI_d＝(n×d)/45，VSWI_w＝(n×d)/20，n为增强植被指数步长的个数，n≥1的正整数。对于农田生态系统，VAWI_d和VSWI_w在适宜生长的温度下的值分别为：In the formula, SDI is the water supply index of crops in each pixel of the remote sensing image corresponding to the monitored farmland block after normalization, taking 0-100%, where SDI=0 means severe drought, SDI=100% means very humid; VSWI _d and VSWI _w is the crop water supply index at the driest time and the wettest time respectively; the step size of EVI can be set as d, when d=0.05, the temperature space suitable for crop growth is 20°C-45°C, VSWI _d =( n×d)/45, VSWI _w =(n×d)/20, n is the number of enhanced vegetation index steps, n≥1 positive integer. For farmland ecosystems, the values of VAWI _d and VSWI _w at suitable growth temperatures are:

5)降水距平指数的计算5) Calculation of precipitation anomaly index

$SRI SRI = = \frac{R R}{{22 R R}_{w w}} \times \times 100100 % %$

式中SRI为被监测农田地块各像元点的降水距平指数，SRI越大越湿润；R为当旬降雨量；R_w为多年当旬降雨量平均值，在本方法中取最近10年的当旬降雨量平均值。当R＞2R_w时为极端湿润，取SRI＝100％，R＞R_w时为正常，取SRI＝50％，R小于多年平均的一半时，为极度干旱，此时SRI取值为0～25％。In the formula, SRI is the precipitation anomaly index of each pixel point of the monitored farmland plot, the larger the SRI, the wetter; _R is the rainfall in the current ten-day period; The average rainfall of the current ten days. When R>2R _w is extremely humid, take SRI=100%, when R>R _w is normal, take SRI=50%, and when R is less than half of the average for many years, it is extremely dry, and the value of SRI at this time is 0～ 25%.

干旱是较长时间缺雨的气象现象，一旬以内的缺雨并不一定出现干旱，一旬以上时间的缺雨才会产生干旱问题，因此需要同时考虑前期的降雨量。本方法考虑了最近8旬的降雨影响，从而计算出综合降水距平指数，其公式为：Drought is a meteorological phenomenon of lack of rain for a long period of time. A lack of rain for less than a decade does not necessarily lead to drought. A lack of rain for more than a decade will cause drought. Therefore, it is necessary to consider the previous rainfall at the same time. This method takes into account the impact of the last 80 days of rainfall, and thus calculates the comprehensive precipitation anomaly index, whose formula is:

SMRI＝A₀×SRI₀+A₁×SRI₁+A₂×SRI₂+A₃×SRI₃+…+A₈×SRI₈ SMRI＝A ₀ ×SRI ₀ +A ₁ ×SRI ₁ +A ₂ ×SRI ₂ +A ₃ ×SRI ₃ +…+A ₈ ×SRI ₈

6)确定农业旱情指数6) Determine the agricultural drought index

DI＝B₁×SDI+B₂×SMRIDI=B ₁ ×SDI+B ₂ ×SMRI

式中DI为被监测农田地块所对应的遥感影像各像元点的农业旱情指数，它是作物供水指数和降雨距平指数的偶合，取0～100％，DI＝0表示非常干旱，DI＝100％表示非常湿润；SDI是标准化供水指数，B₁是其权重，取0.6；SMRI是考虑多旬降雨因素的干旱指数，B₂是其权重，取0.4；B₁，B₂的权重取值是根据SDI的取值与实际情况最为相符。然后根据级别划分标准，判断干旱的程度；In the formula, DI is the agricultural drought index of each pixel point of the remote sensing image corresponding to the monitored farmland block, which is the coupling of the crop water supply index and the rainfall anomaly index, taking 0 to 100%, DI=0 means very dry, DI = 100% means very humid; SDI is the standardized water supply index, B ₁ is its weight, which is taken as 0.6; SMRI is the drought index considering multi-day rainfall factor, B ₂ is its weight, which is taken as 0.4; B ₁ and B ₂ are taken as weight The value is based on the value of SDI which is most consistent with the actual situation. Then judge the degree of drought according to the classification standard;

在此处，1％≤DI≤15％为重旱，15％＜DI≤30％为中旱，30％＜DI≤50％为轻旱，50％＜DI≤70％为正常，70％＜DI≤100％为湿润。Here, 1% ≤ DI ≤ 15% is severe drought, 15% < DI ≤ 30% is moderate drought, 30% < DI ≤ 50% is light drought, 50% < DI ≤ 70% is normal, 70% < DI≤100% is wet.

本发明的优点在于：The advantages of the present invention are:

利用MODIS遥感数据对大区域的农田进行干旱监测。在监测中对模型参数进行改进，采用增强植被指数(EVI)代替归一化植被指数(NDVI)对作物供水指数进行计算。NDVI和EVI都是中分辨率成像光谱仪数据(MODIS)选用的植被指数，EVI作为NDVI的继承和对NDVI的某些缺陷改进，综合处理了土壤背景、大气噪声和红光饱和问题。而且NDVI的计算是对近红外和红光波段的非线性拉伸，其结果是增强了低值部分，抑制了高值部分，对高植被覆盖区容易出现信号饱和。EVI则克服了植被高覆盖区易饱和，植被低覆盖区受土壤植被影响较大的缺点。本发明利用遥感技术进行农田干旱动态监测，经实际应用检验，该方法简便、高效、易于操作、结果准确，能够广泛应用于我国的农田干旱监测之中；Drought monitoring of cropland over a large area using MODIS remote sensing data. The parameters of the model were improved during monitoring, and the enhanced vegetation index (EVI) was used instead of the normalized difference vegetation index (NDVI) to calculate the crop water supply index. Both NDVI and EVI are the vegetation indices selected by Moderate Resolution Imaging Spectrometer Data (MODIS). EVI, as the inheritance of NDVI and some improvements to NDVI, comprehensively deals with the problems of soil background, atmospheric noise and red light saturation. Moreover, the calculation of NDVI is a nonlinear stretching of the near-infrared and red light bands. As a result, the low-value part is enhanced, and the high-value part is suppressed. Signal saturation is prone to occur in high vegetation coverage areas. EVI overcomes the disadvantages that areas with high vegetation coverage are easily saturated, and areas with low vegetation coverage are greatly affected by soil vegetation. The present invention utilizes remote sensing technology to monitor farmland drought dynamics. The method is simple, efficient, easy to operate and accurate in results through practical application tests, and can be widely used in farmland drought monitoring in my country;

具体实施方式Detailed ways

以华北地区农田干旱监测为例。研究区主要包括河北、北京、天津、山东、河南、江苏和安徽省。此区为我国主要农业区，主要气象灾害是干旱，其发生频率高，持续时间长，对国民经济有着严重的影响。本区经常出现的是春旱；因此对华北地区4月份的旱情进行了监测研究。Take farmland drought monitoring in North China as an example. The research area mainly includes Hebei, Beijing, Tianjin, Shandong, Henan, Jiangsu and Anhui provinces. This area is the main agricultural area in my country, and the main meteorological disaster is drought, which occurs frequently and lasts for a long time, and has a serious impact on the national economy. Spring droughts often occur in this area; therefore, the monitoring and research on the drought in North China in April was carried out.

首先根据研究区范围选择相对应的MODISL1B数据，按常规方法(在ERDAS或ENVI等遥感数据软件中处理)对所选波段的遥感数据进行统一的预处理，利用遥感软件数据进行几何校正、云检测和投影转换，以便进一步进行匹配计算。然后利用各相应的波段数据分别计算被监测地区各像元点的EVI、T_s和作物供水指数。并根据被监测区遥感影像各像元点的EVI值的大小选择其所对应的VSWI_d和VSWI_w值对作物供水指数进行标准化处理。利用研究区各气象站点的4月上、中、下旬的降雨数据计算降水距平指数和综合降雨距平指数；在ArcGIS软件里对综合降水距平指数插值，得到能与遥感影像匹配的降水距平指数删格图。最后根据降水距平指数和标准化的作物供水指数计算农业旱情指数。First, select the corresponding MODISL1B data according to the scope of the study area, and perform unified preprocessing on the remote sensing data of the selected band according to the conventional method (processing in remote sensing data software such as ERDAS or ENVI), and use the remote sensing software data to perform geometric correction and cloud detection. and projection transformation for further matching calculations. Then use the corresponding band data to calculate the EVI, T _s and crop water supply index of each pixel point in the monitored area. And according to the size of the EVI value of each pixel point of the remote sensing image in the monitored area, the corresponding VSWI _d and VSWI _w values are selected to standardize the crop water supply index. The precipitation anomaly index and the comprehensive rainfall anomaly index were calculated by using the rainfall data of each meteorological station in the study area in the first, middle and late April; the comprehensive precipitation anomaly index was interpolated in the ArcGIS software to obtain the precipitation distance that can match the remote sensing image Flat exponential grid plot. Finally, the agricultural drought index is calculated according to the precipitation anomaly index and the standardized crop water supply index.

1)植被指数的计算：1) Calculation of vegetation index:

2)地表温度反演2) Inversion of surface temperature

T_s＝A₀+A₁T₃₁-A₂T₃₂ T _s =A ₀ +A ₁ T ₃₁ -A ₂ T ₃₂

A₀＝E₁a₃₁-E₂a₃₂ A ₀ =E ₁ a ₃₁ -E ₂ a ₃₂

A₁＝1+A+E₁b₃₁ A ₁ =1+A+E ₁ b ₃₁

A₂＝A+E₂b₃₂ A ₂ =A+E ₂ b ₃₂

A＝D₃₁/E₀ A＝D ₃₁ /E ₀

C_i＝ε_iτ_i C _i =ε _i τ _i

D_t＝[1+(1-ε_i)τ_i]D _t ＝[1+(1-ε _i )τ _i ]

ε_i＝ε_iw+P_vR_vε_iv+(1-P_v)R_sε_is ε _i ＝ε _iw +P _v R _v ε _iv +(1-P _v )R _s ε _is

ε_iw、ε_iv和ε_is分别是被监测农田地块所对应的遥感影像各像元点的水体、植被和裸土在第波段i波段的地表比辐射率，分别取ε_31w＝0.99683，ε_32w＝0.99254，ε_31v＝0.98672，ε_32v＝0.98990，ε_31s＝0.96767，ε_31s＝0.97790；R_v和R_s分别是被监测农田地块所对应的遥感影像的植被和裸土的辐射比率，覃志豪等(Qin Z，Karnieli A.Progress in the Remote Sensing of Land SurfaceTemperature and Ground Emissivity Using NOAA-AVHRR[J].InternationalJournal of Remote Sensing，1999，20(12)：2367-2397.)，计算的R_v＝0.99240，R_s 1.00744。P_v是被监测农田地块所对应的各像元点的植被覆盖率，通过植被指数进行估算：ε _iw , ε _iv and ε _is are the surface specific emissivity of the water body, vegetation and bare soil in the band i of each pixel point of the remote sensing image corresponding to the monitored farmland plot, respectively, and ε _31w = 0.99683, ε _32w ＝0.99254, ε _31v ＝0.98672, ε _32v ＝0.98990, ε _31s ＝0.96767, ε _31s ＝0.97790; R _v and R _s are the radiation ratios of vegetation and bare soil in the remote sensing image corresponding to the monitored farmland block, (Qin Z, Karnieli A. Progress in the Remote Sensing of Land Surface Temperature and Ground Emissivity Using NOAA-AVHRR[J]. International Journal of Remote Sensing, 1999, 20(12): 2367-2397.), calculated R _v = 0.99240, R _s 1.00744. P _v is the vegetation coverage rate of each pixel point corresponding to the monitored farmland plot, which is estimated by the vegetation index:

$NDVI NDVI = = \frac{{B B}_{22} - - {B B}_{11}}{{B B}_{22} + + {B B}_{11}}$

Qin Z，Karnieli A，Berliner P.A Mono- window Algorithm for RetrievingLand Surface Temperature from Landsat TM Data and Its Application toQin Z, Karnieli A, Berliner P.A Mono- window Algorithm for RetrievingLand Surface Temperature from Landsat TM Data and Its Application to

τ₃₁＝0.99513-0.0808wτ ₃₁ =0.99513-0.0808w

τ₃₂＝0.99376-0.11369wτ ₃₂ =0.99376-0.11369w

τ₃₁＝1.08692-0.12759wτ ₃₁ =1.08692-0.12759w

τ₃₂＝1.07900-0.15925wτ ₃₂ =1.07900-0.15925w

τ₃₁＝1.07268-0.12571wτ ₃₁ =1.07268-0.12571w

τ₃₂＝0.93821-0.12613wτ ₃₂ =0.93821-0.12613w

w＝[(α-lnT_w)/β]² w=[(α-lnT _w )/β] ²

3)作物供水指数的计算：3) Calculation of crop water supply index:

$VSWI VSWI = = \frac{EVI EVI}{{T T}_{s the s}}$

式中SDI是标准化后被监测农田地块所对应的遥感影像各像元点作物的供水指数，取0～100％，其中SDI＝0表示严重干旱，SDI＝100％表示非常湿润；VSWI_d和VSWI_w分别为最旱时和最湿润时的作物供水指数；EVI的分级步长可设为d，当d＝0.05时，适宜作物生长的温度空间为20℃～45℃时，VSWI_d＝(n×d)/45，VSWI_w＝(n×d)/20，n为增强植被指数步长的个数，n≥1的正整数(此实例中n的确定是按所监测农田地块面积每平方公里为1个像元计，n为像元数)。对于农田生态系统，VAWI_d和VSWI_w在适宜生长的温度下的值分别为：In the formula, SDI is the water supply index of crops in each pixel of the remote sensing image corresponding to the monitored farmland block after normalization, taking 0-100%, where SDI=0 means severe drought, SDI=100% means very humid; VSWI _d and VSWI _w is the crop water supply index at the driest time and the wettest time respectively; the step size of EVI can be set as d, when d=0.05, the temperature space suitable for crop growth is 20°C-45°C, VSWI _d =( n×d)/45, VSWI _w =(n×d)/20, n is the number of enhanced vegetation index steps, n≥1 positive integer (in this example, the determination of n is based on the monitored farmland plot area Each square kilometer is counted as one pixel, and n is the number of pixels). For farmland ecosystems, the values of VAWI _d and VSWI _w at suitable growth temperatures are:

5)降水距平指数的计算5) Calculation of precipitation anomaly index

$SRI SRI = = \frac{R R}{{22 R R}_{w w}} \times \times 100100 % %$

式中SRI为被监测农田地块各像元点的降水距平指数，SRI越大越湿润；R为当旬降雨量；R^w为多年当旬降雨量平均值，在本方法中取最近10年的当旬降雨量平均值。当R＞2R^w时为极端湿润，取SRI＝100％，R＞R^w时为正常，取SRI＝50％，R小于多年平均的一半时，为极度干旱，此时SRI取值为0～25％。In the formula, SRI is the precipitation anomaly index of each pixel point of the monitored farmland plot, the larger the SRI, the wetter; ^R is the rainfall in the current ten-day period; The average rainfall of the current ten days. When R>2R ^w is extremely humid, take SRI=100%, when R>R ^w is normal, take SRI=50%, and when R is less than half of the average for many years, it is extremely dry, and the value of SRI at this time is 0～ 25%.

6)确定农业旱情指数6) Determine the agricultural drought index

DI＝B₁×SDI+B₂×SMRIDI=B ₁ ×SDI+B ₂ ×SMRI

在实际应用中，为了更直观地向有关部门提供旱情结果，需要对旱情结果进行统计，通过计算各省、区不同旱情等级的受旱面积及受旱比例来评价受旱程度(表1)。In practical application, in order to provide the relevant departments with more intuitive results of drought, it is necessary to make statistics on the results of drought, and evaluate the degree of drought by calculating the drought-affected area and drought-affected proportion of different drought levels in various provinces and regions (Table 1).

表1：华北地区4月份各省农业旱情监测结果Table 1: Monitoring results of agricultural drought in various provinces in North China in April

从本月降水情况看，本月河北西部和河南大部降水较常年偏少，由于冷暖空气都很活跃，所以强对流天气非常多，中下旬，江苏、安徽的一些地方出现了连续的雷雨天气。但由站点观测数据大致看出，大部分地区降水量一般不足20mm，加上前期降水偏少，大风天气多，气温大幅回升，造成土壤失墒较快，持续的高温少雨使这些地方的干旱持续发展，至月底旱区有所扩大。从降雨数据看，本监测方法能够很好的反映出农业旱情的基本趋势。Judging from the precipitation situation this month, the precipitation in western Hebei and most parts of Henan this month is less than normal. Due to the active cold and warm air, there are a lot of strong convective weather. In the middle and late ten days, some places in Jiangsu and Anhui experienced continuous thunderstorms. . However, it can be seen from the observation data at the site that the precipitation in most areas is generally less than 20 mm. In addition, the precipitation in the early stage is relatively small, the windy weather is more frequent, and the temperature rises sharply, resulting in rapid soil moisture loss. The continuous high temperature and less rain make the drought in these places continue. Developed, and the dry area expanded by the end of the month. From the rainfall data, this monitoring method can well reflect the basic trend of agricultural drought.

Claims

1. A method for monitoring agricultural drought based on MODIS data, based on the spectral reflectance data of each band pixel of MODIS corresponding to the monitored farmland plot; It is characterized in that:

1) Calculation of vegetation index:

EVI EVI = = G G \times \times \frac{{ρ ρ}_{NIR NIR} - - {ρ ρ}_{Red Red}}{{ρ ρ}_{NIR NIR} + + {C C}_{11} \times \times {ρ ρ}_{Red Red} - - {C C}_{22} \times \times {ρ ρ}_{Blue Blue} + + L L}

EVI is the enhanced vegetation index, ρ _NIR , ρ _Red and ρ _Blue are the spectral reflectance of the pixels in the near-infrared band, red band and blue band of MODIS corresponding to the monitored farmland plot respectively; L is the background adjustment item; C ₁ and C ₂ are the fitting coefficients; G is the gain factor; when calculating MODIS-EVI, L=1, C ₁ =6, C ₂ =7.5, G=2.5; respectively calculate the corresponding EVI data of each pixel point of the remote sensing image;

2) Inversion of surface temperature, the calculation formula is:

T _s =A ₀ +A ₁ T ₃₁ -A ₂ T ₃₂

A ₀ =E ₁ a ₃₁ -E ₂ a ₃₂

A ₁ =1+A+E ₁ b ₃₁

A ₂ =A+E ₂ b ₃₂

A＝D ₃₁ /E ₀

E ₁ =D ₃₂ (1-C ₃₁ -D ₃₁ )/E ₀

E ₂ =D ₃₁ (1-C ₃₂ -D ₃₂ )/E ₀

E ₀ ＝D ₃₂ C ₃₁ -D ₃₁ C ₃₂

C _i =ε _i τ _i

D _i ＝[1+(1-ε _i )τ _i ]

In the formula, T _s is the surface temperature (K), T ₃₁ and T ₃₂ are the brightness temperature of the 31st and 32nd bands of MODIS respectively, A ₀ , A ₁ and A ₂ are the parameters of the window-splitting algorithm, a ₃₁ , b ₃₁ , a ₃₁ and b ₃₂ are constants, a ₃₁ = -64.60363, b ₃₁ = 0.440817, a ₃₂ = -68.72575, b ₃₂ = 0.473453 respectively can be taken in the range of surface temperature 0-50°C; The T _s data of each pixel point of the remote sensing image corresponding to the block;

where i refers to the 31st and 32nd bands of the MODIS image corresponding to the monitored farmland plot, i=31 or 32 respectively; _τi is the band i of each pixel point of the remote sensing image corresponding to the monitored farmland plot Atmospheric transmittance, ε _i is the surface specific emissivity of the band i of each pixel point of the remote sensing image corresponding to the monitored farmland plot.

ε _i ＝ε _iw +P _v R _v ε _iv +(1-P _v )R _s ε _is

ε _iw , ε _iv and ε _is are the surface specific emissivity of the water body, vegetation and bare soil in the i-th band of each pixel point of the remote sensing image corresponding to the monitored farmland plot, respectively, and ε _31w = 0.99683, ε _32w ＝0.99254, ε _31v ＝0.98672, ε _32v ＝0.98990, ε _31s ＝0.96767, ε _31s ＝0.97790; R _v and R _s are the radiation ratios of vegetation and bare soil in the remote sensing image corresponding to the monitored farmland block, calculated R _v =0.99240, R _s =1.00744. P _v is the vegetation coverage rate of each pixel point corresponding to the monitored farmland plot, which is estimated by the vegetation index:

{p p}_{v v} = = \frac{{NDVI NDVI - - NDVI NDVI}_{s the s}}{{NDVI NDVI}_{v v} - - {NDVI NDVI}_{s the s}}

In the formula, NDVI is the vegetation index of each pixel point corresponding to the monitored farmland plot, NDVI _v and NDVI _s are the NDVI values of dense vegetation coverage and completely bare soil pixels respectively, usually NDVI _v = 0.674, NDVI _s = 0.039;

NDVI NDVI = = \frac{{B B}_{22} - - {B B}_{11}}{{B B}_{22} + + {B B}_{11}}

where B1 and B2 are the reflectances of the first and second bands of the MODIS image, respectively.

There is a nearly linear relationship between the atmospheric transmittance and the atmospheric water vapor content, and the relationship is

When the water vapor content is 0.4～2.0g/ ^cm2 :

τ ₃₁ =0.99513-0.0808w

τ ₃₂ =0.99376-0.11369w

When the water vapor content is 2-4.0g/ ^cm2 :

τ ₃₁ =1.08692-0.12759w

τ ₃₂ =1.07900-0.15925w

When the water vapor content is 4.0～6.0g/ ^cm2 :

τ ₃₁ =1.07268-0.12571w

τ ₃₂ =0.93821-0.12613w

The formula for calculating the water vapor content of the atmosphere is:

w=[(α-lnT _w )/β] ²

Among them, w is the atmospheric water vapor content of each pixel point corresponding to the monitored farmland block; T _w is the reflectance of each pixel point corresponding to the 18th band of the MODIS image of the monitored farmland block corresponding to the second band The ratio of the ground reflectance of each pixel point, α, β are parameters, respectively take α=0.02, β=0.651;

3) Calculation of crop water supply index:

VSWI VSWI = = \frac{EVI EVI}{{T T}_{s the s}}

In the formula, VSWI is the crop water supply index of each pixel point corresponding to the monitored farmland block; EVI is the enhanced vegetation index of each pixel point of the MODIS image corresponding to the monitored farmland block; T _s is the monitored farmland block The surface temperature of each pixel point of the corresponding remote sensing image;

4) Standardize the crop water supply index:

SDI＝(VSWI- _VSWId )/( _VSWIw - _VSWId )×100%

In the formula, SDI is the water supply index of crops in each pixel of the remote sensing image corresponding to the monitored farmland block after normalization, taking 0-100%, where SDI=0 means severe drought, SDI=100% means very humid; VSWI _d and VSWI _w is the crop water supply index at the driest time and the wettest time respectively; the step size of EVI can be set as d, when d=0.05, the temperature space suitable for crop growth is 20°C-45°C, VSWI _d =( n×d)/45, VSWI _w =(n×d)/20, n is the number of enhanced vegetation index steps, n≥1 positive integer. For farmland ecosystems, the values of VAWI _d and VSWI _w at suitable growth temperatures are:

5) Calculation of precipitation anomaly index

SRI SRI = = \frac{R R}{{22 R R}_{w w}} \times \times 100100 % %

In the formula, SRI is the precipitation anomaly index of each pixel point of the monitored farmland plot, the larger the SRI, the wetter; _R is the rainfall in the current ten-day period; The average rainfall of the current ten days. When R>2R _w is extremely humid, take SRI=100%, when R>R _w is normal, take SRI=50%, and when R is less than half of the average for many years, it is extremely dry, and the value of SRI at this time is 0～ 25%.

Drought is a meteorological phenomenon of lack of rain for a long period of time. A lack of rain for less than a decade does not necessarily lead to drought. A lack of rain for more than a decade will cause drought. Therefore, it is necessary to consider the previous rainfall at the same time. This method takes into account the impact of the last 80 days of rainfall, and thus calculates the comprehensive precipitation anomaly index, whose formula is:

SMRI＝A ₀ ×SRI ₀ +A ₁ ×SRI ₁ +A ₂ ×SRI ₂ +A ₃ ×SRI ₃ +…+A ₈ ×SRI ₈

6) Determine the agricultural drought index

DI=B ₁ ×SDI+B ₂ ×SMRI

In the formula, DI is the agricultural drought index of each pixel point of the remote sensing image corresponding to the monitored farmland block, which is the coupling of the crop water supply index and the rainfall anomaly index, taking 0 to 100%, DI=0 means very dry, DI = 100% means very humid; SDI is the standardized water supply index, B ₁ is its weight, which is taken as 0.6; SMRI is the drought index considering multi-day rainfall factor, B ₂ is its weight, which is taken as 0.4; B ₁ and B ₂ are taken as weight The value is based on the value of SDI which is most consistent with the actual situation. Then judge the degree of drought according to the classification standard;

Here, 1% ≤ DI ≤ 15% is severe drought, 15% < DI ≤ 30% is moderate drought, 30% < DI ≤ 50% is light drought, 50% < DI ≤ 70% is normal, 70% < DI≤100% is wet.

2. according to the described method of claim 1, it is characterized in that: to obtain EVI, T _s , VSWI and SDI parameter, carry out unified preprocessing to the remote sensing data of selected band, mainly comprise carrying out radiation dose calculation, geometry Correction, cloud detection, and projection conversion for further matching calculations; when calculating the rainfall anomaly index, the rainfall data should be interpolated first to obtain a precipitation anomaly map; Data comparative analysis.