CN101187630A - Agricultural drought monitoring method - Google Patents

Abstract

The invention discloses a method for monitoring farmland drought, which comprises following steps: obtaining soil or leaf water content of an observing point which is arranged on monitoring ground surface, building a functional relation between soil or leaf water content of the observing point and a corresponding index of characterized farmland drought condition, taking remote sensing data into the functional relation, and obtaining the soil or leaf water content of the monitoring ground surface, wherein the corresponding index of characterized ground surface drought condition is defined according to following methods: Firstly, the farmland ground surface whose vegetation coverage < =15% adopts a vertical drought index, secondly, the farmland ground surface whose vegetation coverage > 15% and <= 65% adopts a vegetation conditions albedo drought index, and thirdly, the farmland ground surface whose vegetation coverage > 65% to 100% adopts at least one of three following indexes: a short wave infrared vertical water loss index, a vegetation water content index and a vegetation water deficit index. The ground surface drought monitoring method of the invention is applied to drought testing farmland ground surface with different vegetation coverage degrees.

Description

A kind of agricultural drought monitoring method

Technical field

The present invention relates to a kind of agricultural drought monitoring method.

Background technology

The generating process of arid is potential, is not easy to find; The occurrence characteristics of agricultural drought is that coverage is big, brings serious catastrophic effect and economic loss; The factor that arid relates to is many, as meteorologic parameter, surface water situation, human social economy's activity and level of agricultural production and production structure etc.Remote sensing technology can provide the multi-source multidimensional multidate information in farmland, for new approach has been opened up in the agricultural drought monitoring.

The monitoring of traditional agricultural drought is the degree and the scope of monitoring arid with the data on the observation station, and wherein using maximum is meteorological measuring.Owing to periodicity and the zonal characteristics that arid takes place, realize the draught monitor of extensive area, remote sensing technology is one of feasible approach.The satellite remote sensing draught monitor is since the seventies in 20th century, at meteorological arid or agricultural arid, utilize visible light, near infrared, infrared, multiple wave band such as microwave, more models and method have been produced, as NOAA drought index (the NOAA Drought Index, NDI, Strommen et al, 1980), (the Vegetation Condition Index of the vegetation condition index based on normalized differential vegetation index (NDVI) by Kogan (1995) proposition, VCI) and based on the state of temperature index of surface temperature (Temperature Condition Index, TCI) etc.In these methods, Shang Weiyou is applicable to the large tracts of land agricultural drought monitoring method of the different growth phases of field-crop.

Summary of the invention

The purpose of this invention is to provide a kind of new method of utilizing remote sensing technology to carry out face of land draught monitor.

The method of the face of land provided by the present invention draught monitor, be soil or the leaf water content that obtains set observation station on the face of land to be monitored, the soil of described observation station or the corresponding index of leaf water content and sign face of land damage caused by a drought are set up functional relation, bring remotely-sensed data into described functional relation, draw the soil or the plant leaf blade water cut on the face of land to be monitored;

Wherein, the corresponding index of described sign face of land damage caused by a drought is determined as follows:

1), adopts vertical drought index on the face of land of vegetation coverage≤15%;

Described vertical drought index calculates according to following formula (I):

$\mathrm{PDI}=\frac{1}{\sqrt{M^2+1}}(R_{\mathrm{red}}+{\mathrm{MR}}_{\mathrm{nir}})---\left(I\right),$

Wherein, PDI is vertical drought index, and M is the soil line slope, R _RedBe the red spectral band reflectivity through atmospheric correction, R _NirBe near-infrared band reflectivity through atmospheric correction;

2) vegetation coverage＞15% and≤65% the face of land, adopt vegetation condition albedo drought index;

Described vegetation condition albedo drought index calculates according to following formula (II):

$\mathrm{VCADI}=\frac{A_{i,\mathrm{NDV}{I}_i}-A_{{\min ,\mathrm{NDVI}}_i}}{A_{\max ,{\mathrm{NDVI}}_i}-A_{{\min ,\mathrm{NDVI}}_i}}---\left(\mathrm{II}\right),$

Wherein, VCADI is a vegetation condition albedo drought index, $A_{\max ,\mathrm{NDV}{I}_i}=a+\mathrm{bNDV}{I}_i,$ $A_{\min ,\mathrm{NDV}{I}_i}=a^{\&prime;}+b^{\&prime;}{\mathrm{NDVI}}_i;$ A _{Min, NDVIi}And A _{Max, NDVIi}Be respectively minimum and the maximum albedo of district to be measured vegetation index (NDVI) when equaling certain particular value; A, b, a ', b ' is a undetermined coefficient, and a and a ' are respectively the intercepts of doing limit and wet limit in the Albedo-NDVI feature space, and b and b ' are respectively the slopes of doing limit and wet limit in the Albedo-NDVI feature space, scatter diagram by district to be measured vegetation index and albedo obtains (seeing accompanying drawing 3), A _{I, NDVIi}Be Albedo-NDVI feature space mid point (A _i, NDVI _i) albedo, NDVI _iBe D (A in the Albedo-NDVI feature space _i, NDVI _i) vegetation index;

3) in the farmland of vegetation coverage＞65%, adopt at least a in following three kinds of indexes: the vertical dehydration index of short-wave infrared, vegetation moisture exponential sum vegetation water deficit index are monitored;

The vertical dehydration index of described short-wave infrared calculates according to following formula (III):

$\mathrm{SPSI}=\frac{1}{\sqrt{M^2+1}}(R_{\mathrm{SWIR}}+{\mathrm{MR}}_{\mathrm{NIR}})---\left(\mathrm{III}\right)\text{,}$

Wherein, SPSI is the vertical dehydration index of short-wave infrared, R _SWIR, R _NIRBe respectively shortwave ruddiness (1550-1750nm) and near-infrared band (780-900nm) reflectivity through atmospheric correction, M is the NIR-SWIR baseline slope;

Described vegetation moisture index calculates according to following formula (IV):

$\mathrm{VWCI}=\frac{(M_1-M)\&times;({\mathrm{NIR}}_G-M_2\&times;{\mathrm{SWIR}}_G-I_2)}{(M_1-M_2)\&times;({\mathrm{NIR}}_G-M\&times;{\mathrm{SWIR}}_G)-(M_1-M)\&times;I_2+(M_2-M)\&times;I_1}---\left(\mathrm{IV}\right)$

Wherein, VWCI is a vegetation moisture index, and with reference to figure 4, M is a soil baseline slope in the NIR-SWIR space; M ₁, M ₂Be respectively the slope of AB and CD line, I ₁, I ₂Be respectively the intercept of AB and CD line, NIR _G, SWIR _GAlbedo value for the near infrared and the short-wave infrared of position to be measured correspondence;

Described vegetation water deficit index calculates according to following formula (V):

VWSI＝1-VWCI (V)

Wherein, VWSI is a vegetation water deficit index; VWCI is a vegetation moisture index, calculates according to formula (IV).

Wherein, vegetation coverage is meant that the vertical projection area of vegetation (comprising leaf, stem, branch) on ground accounts for the number percent of the statistics district total area.

In the said method, described 1) in, on the face of land, farmland of vegetation coverage≤15%, adopt vertical arid indices P DI, the M value that its computing formula relates to, in ruddiness-near-infrared band reflectivity two-dimensional space, the slope of the soil line that the each point of sign soil constitutes.Described 2) in, vegetation coverage＞15% and≤65% the face of land, farmland, adopt vegetation condition albedo drought index VCADI; Wherein, extract slope and the intercept (a, b, a ', b ') of doing limit, wet limit, calculate VCADI on this basis at the Albedo-NDVI feature space.Described 3) in,, adopt vertical dehydration index of short-wave infrared or vegetation moisture index on the face of land, farmland of vegetation coverage＞65% to 100%; Wherein the related parameter of each formula is all based on short-wave infrared-near infrared spectrum feature space.

In the said method, described leaf water content specifically can be blade equivalence moisture, described 3) in vegetation coverage greater than 65%, belong to high vegetation coverage situation.

In the said method, described R _RedCan be 630-690nm red spectral band reflectivity, described R through atmospheric correction _NirPerhaps R _NIRCan be 780-900nm near-infrared band reflectivity, described R through atmospheric correction _SWIRCan be 1550-1750nm shortwave red spectral band reflectivity, described R through atmospheric correction _NIRCan be 780-900nm near-infrared band reflectivity through atmospheric correction.

The present invention utilizes multi-source multidimensional multi-temporal remote sensing image, start with from making up the reflectance spectrum of farmland moisture-sensitive wave band or characterizing the n dimension spectral signature space that the ecological physical parameter of agricultural drought forms, foundation can quantitative response farmland water deficit information Remote Sensing Model, catch two the index soil moisture and blade (canopy) water cut of agricultural drought key, multiple array configuration with the ecological physical parameter of the spectral reflectivity or the face of land, by calculating the distance of agricultural ecological drought index in multidimensional spectral signature space, dose agricultural drought multidate information, on this basis agricultural drought is carried out monitoring and evaluation, and verified by radiation delivery model and field observation data.According to the different situations that surface vegetation covers, select to characterize the corresponding index of farmland damage caused by a drought.Select suitable observation station to obtain soil or leaf water content, set up funtcional relationship with the corresponding index that characterizes the farmland damage caused by a drought, and then be finally inversed by the soil or the crop leaf water cut of whole monitoring section.The corresponding index of the selected sign of the present invention farmland damage caused by a drought is as follows:

1) do not have in the farmland that vegetation covers and very low vegetation covers under (coverage≤15%) situation, remote sensing monitoring at the soil moisture index, according to NIR and the vertical drought index of RED spatial spectral feature construction (PDI), taken all factors into consideration the difference between soil water content, vegetation coverage, soil organic matter content and the different soils type, this model is simple and practical, and is workable.

2) under the vegetation part coverage condition of farmland,, vegetation condition albedo drought index (VCADI) has been proposed according to NDVI and albedo feature space.The introducing of albedo obtains atural object at the abundanter reflective information in hemisphere space, and the reflectance signature from visible light to infrared whole sunshine scope can both obtain reflection, inverting agricultural drought information so more accurately preferably.This model carries out arid assessment, easy operating, monitoring accuracy height.

3) under the situation that full vegetation covers, at the leaf water content index of agricultural drought monitoring, three kinds that have proposed based on blade equivalence moisture (EWT) or blade relative water content (FMC) are used for drought monitoring method.The present invention finds to do in the NIR-SWIR feature space wet situation and is typical trapezoidal or the triangle distribution, utilize blade radiation delivery model PROSPECT, canopy radiation delivery model (Lillesaeter/SailH) and the face of land-atmospheric radiation transmission 6S, proposed to be used for high vegetation and covered the canopy vertical dehydration index of moisture (SPSI), be applicable to the vegetation moisture index (VWCI) and the vegetation water deficit index (VWSI) of high coverage with water deficit monitoring model-short-wave infrared.This model is applicable to the inverting of agricultural drought monitoring parameter and the assessment of high vegetation-covered area.

The present invention utilizes remote sensing technology to carry out the agricultural drought dynamic monitoring, at the different growth phases of field-crop, selects for use the remote sensing different-waveband to set up the draught monitor index, and combined ground observation station observed watercut data are to obtain monitoring section large tracts of land arid situation.Through practical application check, this method is easy, efficient, easy operating, result are accurate, can be widely used among the agricultural drought monitoring of northern China and the Northwest.The face of land of the present invention drought monitoring method is applicable to the face of land, the farmland draught monitor of different vegetation coverages.

Description of drawings

Fig. 1 does wet situation distribution triangular in shape in ruddiness-near infrared spectrum feature space

Fig. 2 is vertical drought index (PDI) synoptic diagram

Fig. 3 is vegetation condition albedo drought index (VCADI) synoptic diagram

Fig. 4 is vertical dehydration index of short-wave infrared (SPSI) and vegetation moisture index (VWCI) synoptic diagram

Fig. 5 is the correlationship synoptic diagram of vertical drought index and soil moisture

Fig. 6 is the correlationship synoptic diagram of vegetation condition albedo drought index and different depth soil moisture

Fig. 7 is the vertical dehydration index of short-wave infrared (SPSI) estimation EWT _CanopyComparison with the field observation data

Fig. 8 is vegetation moisture index (VWCI) estimation EWT _CanopyComparison with the field observation data

Fig. 9 is the comparison of vegetation moisture index (VWCI) estimation FMC and field observation data

Embodiment

The structure of embodiment 1, draught monitor model

For different vegetation coverages, select suitable remote sensing wave band, by calculating the index that is fit to characterize farmland water regime under this vegetation coverage.Technical scheme is as follows:

1. the coverage low (vegetation coverage≤15%) of the face of land do not have vegetation covering or to(for) the face of land, the NIR-Red spectral signature space scatter diagram that utilizes the relevant wave band data of remote sensing to make up is typical triangle distribution (as Fig. 1), B-C is the soil baseline, and gradually dried by B to C soil, can obtain the mathematic(al) representation that B-C is the soil baseline through the spatial statistics feature:

R _nir，s＝MR _red，s+I (1)

R wherein _{Red, s}, R _{Nir, s}Be respectively red spectral band and near-infrared band reflectivity through atmospheric correction, M is that soil line slope, I represent the intercept of soil line on ordinate.

In Fig. 1, obtain through the vertical line L (Fig. 2) of true origin perpendicular to the soil baseline, can obtain the normal equation (2) of equation (1)

$R_{\mathrm{nir}}=-\frac{1}{M}{R}_{\mathrm{red}}---\left(2\right)$

On the NIR-Red feature space, from any one some E (R _Red, R _Nir) to the distance of straight line L the arid situation on the face of land can be described, promptly arid more from the L line face of land far away more, vice versa.Its drought index is minimum concerning water body, just in time drops on true origin, and it is moistening more more near initial point that all the other have any object of certain reflection potential.In general, all be water body near the space of L line or distribute than wet zone.Space away from the L line all is arid zone.Therefore, can be with any 1 the E (R on the NIR-Red feature space _Red, R _Nir) to the distance of straight line L arid situation is described, can set up a draught monitor model based on NIR-Red spectral space feature, promptly vertical drought index (Perpendicular Drought Index, PDI).

$\mathrm{PDI}=\frac{1}{\sqrt{M^2+1}}(R_{\mathrm{red}}+{\mathrm{MR}}_{\mathrm{nir}})---\left(3\right)$

Wherein, M is the soil line slope, carries out linear regression and obtains by choosing near the point that drops on the soil line.R _RedBe red spectral band (630-690nm) reflectivity through atmospheric correction, R _NirFor near-infrared band (775-900nm) reflectivity, for corresponding ruddiness, near-infrared band obtain through radiation calibration through atmospheric correction.

2. for the face of land, farmland of medium surface vegetation coverage (vegetation coverage greater than 15% to smaller or equal to 65%), need the influence of considering that vegetation covers.Albedo (Albedo)--the branch of " doing the limit " and " wet limit " is also arranged in the scatter diagram of normalized differential vegetation index (NDVI), and " doing the limit " is the high albedo part, and " wet limit " is low albedo part.Equally, can reflect the feature that the face of land covers by ABC.If do not consider the influence of other parameter, because vegetation changes the roughness on the face of land, in arid, semiarid zone, increase surface albedo decline along with vegetation, the increase of soil moisture also causes the decline of albedo, soil moisture and the positive correlation of vegetation growing way, and have negative correlation between vegetation, soil moisture and the albedo.

As shown in Figure 3, make albedo=A _i, in the Albedo-NDVI feature space, have certain some D (A _i, NDVI _i), the degree of drought that utilizes the difference through minimum and maximum albedo on the straight line of this point can reflect this point, this index be vegetation condition albedo drought index (Vegetation Condition Albedo Drought Index, VCADI).

$\mathrm{VCADI}=\frac{A_{i,\mathrm{NDV}{I}_i}-A_{{\min ,\mathrm{NDVI}}_i}}{A_{\max ,{\mathrm{NDVI}}_i}-A_{{\min ,\mathrm{NDVI}}_i}}---\left(4\right)$

Wherein, $A_{\max ,\mathrm{NDV}{I}_i}=a+\mathrm{bNDV}{I}_i,$ $A_{\min ,\mathrm{NDV}{I}_i}=a^{\&prime;}+b^{\&prime;}{\mathrm{NDVI}}_i.$ A _{Min, NDVIi}And A _{Max, NDVIi}Be respectively minimum and the maximum albedo of study area NDVI when equaling certain particular value.A, b, a ', b ' is a undetermined coefficient, a and a ' are respectively the intercepts of doing limit and wet limit in the Albedo-NDVI feature space, and b and b ' are respectively the slopes of doing limit and wet limit in the Albedo-NDVI feature space, can be by the NDVI of survey region and the scatter diagram of albedo, select AC, near the point of BC line, carry out linear regression and obtain corresponding value.A _{I, NDVIi}Be certain some D (A _i, NDVI _i) albedo, NDVI _iVegetation index for certain some D in the Albedo-NDVI feature space.

3. for the face of land of vegetation coverage height (vegetation coverage greater than 65% to 100% cover), on the scatter diagram of the spectral signature space of near infrared (NIR) short-wave infrared (SWIR) (Fig. 4), analysis can obtain NIR-SWIR baseline BC, mathematic(al) representation through spatial statistics:

R _NIR＝MR _SWIR+I (5)

R wherein _SWIR, R _NIRBe respectively shortwave ruddiness and near-infrared band reflectivity through atmospheric correction, M is that NIR-SWIR baseline slope, I represent the intercept of NIR-SWIR baseline on ordinate.

Obtain through the vertical line L (Fig. 4) of true origin, can obtain the normal equation (6) of equation (5) perpendicular to the soil baseline

$R_{\mathrm{NIR}}=-\frac{1}{M}{R}_{\mathrm{SWIR}}---\left(6\right)$

On the NIR-SWIR feature space, from any one some G (R _SWIR, R _NIR) to the distance of straight line L the arid situation on the face of land can be described, in general, all be water body near the space of L line or distribute than wet zone.Space away from the L line all is arid zone.Its drought index is minimum concerning water body, just in time drops on true origin, and it is moistening more more near initial point that all the other have any object of certain reflection potential.Therefore, can be with any 1 the G (R on the NIR-SWIR feature space _SWIR, R _NIR) to the distance of straight line L arid situation is described, set up a draught monitor model based on NIR-SWIR spectral space feature, this model be the vertical dehydration index of short-wave infrared (Shortwave Infrared Perpendicular Water Stress Index, SPSI).

$\mathrm{SPSI}=\frac{1}{\sqrt{M^2+1}}(R_{\mathrm{SWIR}}+{\mathrm{MR}}_{\mathrm{NIR}})---\left(7\right)$

Described SPSI is the vertical dehydration index of short-wave infrared, R _SWIR, R _NIRBe respectively shortwave ruddiness and near-infrared band reflectivity through atmospheric correction, M is the NIR-SWIR baseline slope, can obtain by linear regression.

As mentioned above, the direction away from the NIR-SWIR baseline determines the vegetation moisture jointly with the direction that is parallel to the NIR-SWIR baseline.It is identical that E, G, F point vegetation cover situation, but its vegetation moisture makes a world of difference, canopy vegetation moisture along Vector reduces gradually, The long more expression of vector vegetation moisture is few more,

Vector is short more, illustrates that canopy vegetation moisture is high more, therefore,

Can represent pixel in the water regime that is parallel to the NIR-SWIR base direction.

The length of vector shows the distance of pixel away from the NIR-SWIR baseline, and promptly pixel is big more away from the distance of NIR-SWIR baseline,

Vector is short more, and it is good more that the pixel vegetation covers, and vice versa.

With

The ratio of vector can not only reflect the vegetation moisture, can also reduce atmospheric action and the vegetation canopy scattering effect to ruddiness.

With

With

Ratio respectively called after vegetation moisture index (Vegetation Water Content Index, VWCI) and vegetation water deficit index (Vegetation WaterStress Index, VWSI).

$\mathrm{VWSI}=\frac{\stackrel{\&RightArrow;}{\mathrm{EG}}}{\stackrel{\&RightArrow;}{\mathrm{EF}}},$ $\mathrm{VWCI}=\frac{\stackrel{\&RightArrow;}{\mathrm{GF}}}{\stackrel{\&RightArrow;}{\mathrm{EF}}}=1-\frac{\stackrel{\&RightArrow;}{\mathrm{EG}}}{\stackrel{\&RightArrow;}{\mathrm{EF}}}---\left(8\right)$

Slope and the intercept of setting AB and CD line are M ₁, M ₂, I ₁, I ₂, their linear equations are respectively NIR=M ₁* SWIR+I ₁, NIR=M ₂* SWIR+I ₂, input point G (SWIR _G, NIR _G) be known, and EF is parallel with BC, and then the equation of EF line can be write as

NIR＝M×(SWIR-SWIR _G)+NIR _G

Group can obtain E (SWIR by solving an equation _E, NIR _E) point coordinate:

${\mathrm{SWIR}}_E=\frac{{\mathrm{NIR}}_G-M\&times;{\mathrm{SWIR}}_G-I_1}{M_1-M},$ ${\mathrm{NIR}}_E=\frac{{\mathrm{<figure-callout\; id="1"\; label="M"\; filenames="S2007101788074E00031.png"\; state="\{\{state\}\}">M</figure-callout>}}_1\&times;({\mathrm{NIR}}_G-M\&times;{\mathrm{SWIR}}_G)-M\&times;I_1}{M_1-M}$

Group can obtain F (SWIR by solving an equation _F, NIR _F) point coordinate:

${\mathrm{SWIR}}_F=\frac{{\mathrm{NIR}}_G-M\&times;{\mathrm{SWIR}}_G-I_2}{M_2-M},$ ${\mathrm{NIR}}_F=\frac{M_2\&times;({\mathrm{NIR}}_G-M\&times;{\mathrm{SWIR}}_G)-M\&times;I_2}{M_2-M}$

Can obtain E (SWIR at last _E, NIR _E), G (SWIR _G, NIR _G), F (SWIR _F, NIR _F) between distance, the universal expression formula of VWCI.

$\mathrm{VWCI}=\frac{(M_1-M)\&times;({\mathrm{NIR}}_G-M_2\&times;{\mathrm{SWIR}}_G-I_2)}{(M_1-M_2)\&times;({\mathrm{NIR}}_G-M\&times;{\mathrm{SWIR}}_G)-(M_1-M)\&times;I_2+(M_2-M)\&times;I_1}---\left(9\right)$

Wherein, VWCI is a vegetation moisture index, with reference to the accompanying drawings 4, and M is a soil baseline slope in the NIR-SWIR space; M ₁, M ₂Be respectively the slope of AB and CD line, I ₁, I ₂Be respectively the intercept of AB and CD line, NIR _G, SWIR _GBe respectively the albedo value of the near infrared and the short-wave infrared of position to be measured correspondence;

VWCI and VWSI be inverting vegetation moisture and water stress simultaneously.

4. cover situation according to the monitoring section vegetation, from above-mentioned three class indexs, select a kind of calculating.Simultaneously carry out the measurement (, measure actual leaf water content) of actual soil moisture content for high vegetation coverage at the observation website of monitoring section.Measured data is carried out linear regression with the monitoring exponential quantity in corresponding place, set up the funtcional relationship between index and the water cut, thereby obtain soil moisture content (or leaf water content) distribution situation of whole monitoring section.Delimit arid grade in conjunction with specific targets.

Embodiment 2, utilize the draught monitor model of embodiment 1 to monitor agricultural drought

1. the area that soil types is more single is as a monitoring section.

2. in monitoring section, select some point measurement soil moisture contents, field capacity and wilting coefficient.Can think that the field capacity in the single area of soil types is consistent with wilting coefficient, therefore these two measured values of each measuring point can be averaged, obtain the field capacity and the wilting coefficient of whole monitoring section.

3. according to the vegetation coverage condition of monitoring section, select suitable wave band to carry out the calculating of draught monitor index:

Use ruddiness and near-infrared band, carry out PDI and calculate.

Use albedo and normalized differential vegetation index data, carry out the calculating of VCADI.

Use near infrared and short-wave infrared wave band, carry out SPSI, VCWI, VCSI calculating.

4. the soil moisture content (or blade) of point position is carried out correlation analysis with the draught monitor index, obtain the numerical relation (generally being linear relationship) between interior soil (or blade) water cut of monitoring section and the monitoring index.

5. by 4 results that obtain, obtain soil (or blade) the water cut situation in the whole monitoring section.

6. carry out the arid situation assessment according to soil moisture content:

Soil moisture content＞field capacity 75% moistening;

Soil moisture content is normal moisture condition between the 75%-45% of field capacity;

Soil moisture content is less than 45% of field capacity, greater than the accent slightly arid of coefficient of withering;

Soil moisture content is less than the accent coefficient severe drought that withers.

Using the demonstration area is Ningxia Hui Autonomous Region and Shunyi, Beijing remote sensing experiment field.

Ningxia Hui Autonomous Region is positioned at the innerland, northwest of the mainland, occupies middle reaches, the Yellow River epimere, between 104 ° of 10 ' E-107 ° of 30 ' E, 35 ° of 25 ' N-39 ° of 25 ' N, and area 5.18 ten thousand km ², adjoin with provinces (district) such as Gansu, the Inner Mongol, Shaanxi.This area's arid is big to the agricultural production restriction, the almost annual great drought that takes place.The field inspection data are from this 25 standard weather stations in district and ecological monitoring station.All data compiled and edited by National Meteorological Bureau all that " agrometeorological observation standard " measure in 1993, mainly comprised:

1) farmland water parameters

The top layer 0-20cm different depth of soil moisture of 1990-2005 8 days every month, 18 and 28 days; Underground water table of the same period; The soil physiological parameter comprises wilting coefficient and field capacity.

2) crop characteristic parameter comprises wheat different phenological water requirement, temperature demand, to the soil moisture requirement;

3) synchronous remotely-sensed data MODIS.

Testing field, Shunyi, Beijing is positioned at 116 ° of 26 '-117 ° of E of E, between 40 °-40 ° 21 ' of the N.Testing field, Shunyi from calendar year 2001 by the end of March to 6 months, carried out star-machine-comprehensive simultaneous test of ground remote sensing, be that on-board data is obtained testing field and emphasis ground experiment field with Shunyi, Beijing, obtained airborne multi-angle multispectral data and high-spectral data.The content of observation comprises the multi-angle, multidate of winter wheat growing period, multi-platform, multiband, multiple dimensioned satellite remote sensing images and aerial remote sensing images, various weather datas and atmospheric parameter, the various biochemistry of winter wheat growing period, physical and chemical parameter, biomass and various structural parameters, spectroscopic data and agricultural microclimate parameter etc.Testing field, employed Shunyi data comprised data such as soil moisture, albedo, wheat leaf blade water cut and chlorophyll content, leaf area index, surface temperature, vegetation canopy surface temperature, near surface temperature during this was used.All data compiled and edited by National Meteorological Bureau all that " agrometeorological observation standard " measure in 1993.

1. utilize PDI to carry out the damage caused by a drought monitoring:

The Ningxia MODIS of autonomous region remotely-sensed data and corresponding soil moisture data are used in this checking.

The key of draught monitor is obtaining of arid information and quantification, and arid type is divided and grade and the overall target that finds the checking drought index of grading.The growth of various crops all has certain suitable soil moisture scope, be lower than when soil moisture content crop suitable soil moisture content lower limit (generally speaking, the soil moisture lower limit that crop suits is all at about 20% of field capacity, on be limited to field capacity) time, crop absorbs less than enough moisture and goes to compensate rising expenditure, and arid just takes place.Yet crop absorptivity and water-retaining quantity among field of soil, crop wilting coefficient and soil available water divide content etc. that confidential relation is arranged.In the remote sensing monitoring of agricultural drought situation, the comprehensive drought index that soil moisture combines with the farmland water balance can reflect the arid situation on the face of land more all sidedly.Thus, adopt during checking comprehensive drought index K (as shown in the formula), in order to the checking PDI.

$K=1-\frac{W-W_p}{W_h-W_p}---\left(10\right)$

In the formula, K is a drought index; W is soil moisture content (%); W _hBe the upper limit that soil can keep moisture, i.e. field capacity (%); W _pBe soil wilting coefficient (%).This shows that the variation of K can reflect the satisfaction degree of soil moisture to crop need water truly, reflects arid situation.Crop can obtain sufficient water supply, does not have arid and takes place, at this moment K=0; Work as W=W _pThe time, having lost the soil available water branch fully, crop is dead because of can not get water supply, at this moment K=1.

By drought index and the cloud detection of MODIS remote sensing video figure overlay analysis as a result, find field observation data set correspondence to be compared MODIS remote sensing video each the time phase data in, the pixel that has part to be covered by cloud, cover pixel four limits to cloud earlier during processing and seek cloudless covering pixel, the mean value of four pixels substitutes cloud and covers pixel drought index value on every side, if pixel is all covered by cloud around it, then reject this pixel.

As monitoring section, the vegetation coverage in this monitoring section on March 26th, 2005 is less than＜15% with administrative division, the Ningxia scope between 104 ° of 10 ' E-107 ° of 30 ' E, 35 ° of 25 ' N-39 ° of 25 ' N.With the MODIS Ningxia range data on March 26th, 2005, calculated the PDI of monitoring section, and compared with 22 meteorologies in Ningxia and Ecological View survey station point 0-20cm average soil moisture.What soil moisture content wherein adopted is field observation data on March 28.

This monitoring section is divided into 22 observation stations, adopts CNC-503DR type intelligence Neutron Moisture instrument to measure each sampling point top layer 0-20cm soil average moisture content (W _0-20).

Calculate the PDI of these 22 observation stations respectively.

Vertical drought index (Perpendicular Drought Index, PDI).

$\mathrm{PDI}=\frac{1}{\sqrt{M^2+1}}(R_{\mathrm{red}}+{\mathrm{MR}}_{\mathrm{nir}})---\left(3\right)$

Wherein, M is the soil line slope, R _RedBe red spectral band (630-690nm) reflectivity through atmospheric correction, R _NirBe near-infrared band (775-900nm) reflectivity through atmospheric correction.

The soil water content of table 1.2005 28, each observation stations of Ningxia on March and vertical arid indices P DI

	Longitude	Latitude	PDI	0-20cm soil moisture content (%)	Red	Nir
							Yinchuan	106°18′	38°25′	0.267111	24.5	0.169	0.207
The Qingtongxia	105°54′	38°01′	0.313107	3	0.2	0.241
							The Da Wu mouth	106°24′	39°02′	0.217588	25.4	0.143	0.164
Guyuan	106°16′	36°00′	0.302125	13.8	0.189	0.236
							106°18′	35°58′	0.258194	16	0.175	0.19
	The Huinong	106°46′	39°13′	0.213155	23.8	0.142							0.159
The sea is former							105°39′	36°34′	0.266456	8.7	0.168	0.207
	105°32′	36°42′	0.299505	5.2	0.185	0.36
							The Xiji	105°43′	35°58′	0.289883	16.4	0.183	0.225
The Jingyuan	106°19′	35°30′	0.226153	19.2	0.133	0.184
							106°21′	35°30′	0.230939	15.7	0.138	0.186
	The Longde	106°07′	35°37′	0.251091	22.9	0.148							0.204
The Taole							106°42′	38°48′	0.258647	27.9	0.163	0.201
	The Pingluo	106°34′	38°54′	0.212802	23.7	0.138							0.162
The Wuzhong							106°08′	37°47′	0.290437	4	0.185	0.224
	The Xingren	105°15′	36°56′	0.343133	5.4	0.217							0.266
Yongning							106°15′	38°15′	0.270789	24.1	0.17	0.211
	In peaceful	105°39′	37°26′	0.227009	21	0.147							0.173
The salt pond							107°23′	37°48′	0.311647	4.6	0.192	0.246
	The Lingwu	106°29′	38°05′	0.308976	2.6	0.196							0.239
The centre halfback							105°13′	37°20′	0.265046	28.4	0.167	0.206
	With one heart	105°54′	36°58′	0.333914	6.7	0.211							0.259

PDI and 22 observation station W of monitoring section according to 22 observation stations of monitoring section _0-20Excell sets up funtcional relationship with data processing software, and functional relation is as follows: W _0-20=-0.0033PDI+0.3215, R ²=0.5596 (Fig. 5).

Vertical arid exponential sum soil moisture content data (W _0-20) presenting high correlation, this may be relevant with vegetation growing way situation.The maximal value of vegetation index on March 28 (NDVI) is 0.34, and crop has just begun to stand up, and vegetation covers low, and vegetation is little to the interference of face of land reflectivity, and reflectance spectrum is mainly determined by soil physico-chemical property.

2. utilize albedo drought index VCADI to carry out the damage caused by a drought monitoring:

With testing field, Shunyi, Beijing (scope is the same) as monitoring section, the vegetation coverage the during monitoring of this monitoring section greater than 15% less than 65%.

This monitoring section is divided into 24 observation stations, adopts CNC-503DR type intelligence Neutron Moisture instrument to measure each sampling point top layer 5cm, 10cm, 20cm, 0-20cm soil moisture content.Data in the table 2 are the top layer 5cm in 24 17 days April calendar year 2001 of observation station, 10cm, 20cm, 0-20cm average soil moisture (W ₅, W ₁₀, W ₂₀, W _0-20), and water-retaining quantity among field of soil w _h, soil wilting coefficient w _p

The soil water content of table 2.2001 17, each observation stations of Beijing on April

The observation station numbering	Longitude	Latitude	W 5(％)	W 10(％)	W 20(％)	W 0-20(％)	W h(％)	W p(％)
									25	116°34′33.4″	40°11′41.2″	6.510	12.699	17.067	12.09178	22.5	10.4
26	116°34′34.3″	40°11′43.6″	3.406	10.200	16.678	10.09486	22.5	10.4
									27	116°34′34.0″	40°11′46.0″	4.982	13.298	21.927	13.40233	22.5	10.4
28	116°34′33.7″	40°11′48.5″	7.445	15.554	22.681	15.22647	22.5	10.4
									29	116°34′37.2″	40°11′49.0″	4.909	13.578	22.177	13.55472	22.5	10.4
30	116°34′37.5″	40°11′46.6″	9.881	16.839	22.507	16.40919	22.5	10.4
									31	116°34′37.9″	40°11′44.1″	4.842	10.992	16.343	10.72571	22.5	10.4
32	116°34′38.1″	40°11′41.8″	6.045	12.283	17.249	11.85891	22.5	10.4
									33	116°34′42.4″	40°11′41.6″	6.890	11.570	16.211	11.55713	22.5	10.4
34	116°34′42.1″	40°11′44.0″	3.579	8.061	13.665	8.434948	22.5	10.4
									35	116°34′41.8″	40°11′46.5″	7.308	12.786	20.920	13.6712	22.5	10.4
36	116°34′41.6″	40°11′49.0″	8.520	14.292	20.990	14.60063	22.5	10.4
									46	116°34′24.8″	40°11′34.1″	4.411	10.309	16.410	10.37664	22.1	10
47	116°34′25.2″	40°11′30.1″	7.624	14.026	17.082	12.91068	22.1	10
									48	116°34′31.1″	40°11′30.4″	6.828	13.522	17.083	12.4777	22.1	10
49	116°34′30.8″	40°11′34.4″	6.380	13.256	17.005	12.21337	22.1	10
									52	116°34′34.4″	40°11′34.7″	6.228	11.834	16.429	11.49704	22.1	10
53	116°34′34.8″	40°11′30.6″	8.114	14.146	17.094	13.11814	22.1	10
									54	116°34′31.7″	40°11′53.6″	16.274	22.079	24.440	20.931	22.7	10.4
55	116°34′31.2″	40°11′56.9″	13.599	20.024	23.542	19.05479	22.7	10.4
									58	116°34′37.1″	40°11′57.6″	5.278	12.650	20.097	12.67541	22.7	10.4
59	116°34′37.5″	40°11′54.6″	3.974	11.429	19.492	11.6315	22.7	10.4
									60	116°34′42.8″	40°11′54.6″	13.101	19.949	23.173	18.74107	22.7	10.4
61	116°34′42.4″	40°11′58.0″	17.926	22.195	23.777	21.2994	22.7	10.4

The albedo drought index calculates according to following formula (II):

$\mathrm{VCADI}=\frac{A_{{i,\mathrm{NDVI}}_i}-A_{{\min ,\mathrm{NDVI}}_i}}{A_{{\max ,\mathrm{NDVI}}_i}-A_{\min ,\mathrm{NDV}{I}_i}}---\left(\mathrm{II}\right),$

Wherein, VCADI is the albedo drought index, $A_{{\max ,\mathrm{NDVI}}_i}=a+\mathrm{bNDV}{I}_i,$ $A_{{\min ,\mathrm{NDVI}}_i}=a^{\&prime;}+b^{\&prime;}{\mathrm{NDVI}}_i;$ A _{Min, NDVIi}And A _{Max, NDVIi}Be respectively minimum and the maximum albedo of district to be measured vegetation index when equaling certain particular value; A, b, a ', b ' is a undetermined coefficient, scatter diagram (seeing accompanying drawing 3) by district to be measured vegetation index and albedo obtains, and a and a ' are respectively the intercepts of doing limit and wet limit in the Albedo-NDVI feature space, and b and b ' are respectively the slopes of doing limit and wet limit in the Albedo-NDVI feature space; A _{I, NDVIi}, be Albedo-NDVI feature space mid point (A _i, NDVI _i) albedo; NDVI _iBe D (A in the Albedo-NDVI feature space _i, NDVI _i) vegetation index;

With the albedo-NDVI feature space of Shunyi, Beijing study area ETM+ remotely-sensed data in 17 days April calendar year 2001 structure, extracting on the basis of doing limit and wet limit, further determine the related parameter that has of VCADI by statistical study.Utilize near infrared albedo and corresponding NDVI data to calculate VCADI.

VCADI and 24 observation station W of monitoring section according to 24 observation stations of monitoring section ₅, W ₁₀, W ₂₀, W _0-20Excel sets up funtcional relationship with data processing software, and wherein, the VCADI that usefulness near-infrared band (0.7-4.0 μ m) albedo is calculated and the funtcional relationship of soil moisture content are as follows:

VCADI and W ₅Functional relation be W ₅=-9.3465Ln (VCADI)+1.4711, R ²=0.8057;

VCADI and W ₁₀Functional relation be W ₁₀=-22.019*VCADI+26.032, R ²=0.7962;

VCADI and W ₂₀Functional relation be W ₂₀=-16.968*VCADI+28.556, R ²=0.6585;

VCADI and W _0-20Functional relation be W _0-20=-20.578*VCADI+24.873, R ²=0.8363.

The result shows, VCADI and the soil moisture calculated by the near infrared albedo present negative correlation preferably, especially the pass with average soil moisture of 0-20cm and top layer 10cm soil moisture is best, has the average soil moisture (W of linear relationship (Fig. 6) and 0-20cm _0-20) related coefficient square be respectively R ²=0.84,0.83.Fig. 6 utilizes the VCADI of near-infrared band (0.7-4.0 μ m) albedo calculating and the curve map of soil moisture content, VCADI and W ₅Logarithmic curve to close be best, and and the linear relationship of other deep soil moisture be best.Among Fig. 6, W ₅, W ₁₀, W ₂₀, W _0-20Represent 5cm respectively, 10cm, 20cm, the average soil moisture of 0-20cm, R2 be related coefficient square.

3. utilize SPSI and VWCI (VWSI) to carry out the damage caused by a drought monitoring:

As monitoring section, the vegetation coverage in 19 days Mays calendar year 2001 of this monitoring section is greater than 65% with testing field, Shunyi, Beijing (scope is the same).

In order to verify SPSI and VWCI, with test site, Shunyi, Beijing TM/ETM+ image on 19 days Mays calendar year 2001, the synchronous field inspection data of blade equivalence moisture (EWT) that is calculated by SPSI and VWCI and blade relative water content (FMC) and satellite compare.

The leaf water content of (pixel yardstick) is relevant with leaf area index on the unit area, and along with the increase of leaf area index, unit area EWT will increase.Pixel reflection be the average moisture content of pixel yardstick, therefore, the EWT of field observation multiply by after the leaf area index (as EWT _Canopy=LAI * EWT _Leaf), can compare with the vegetation moisture that remotely-sensed data obtains.FMC is the mean value of sampling area, can FMC value direct the observation station data and remote sensing pixel yardstick compare.

The monitoring section in May 19 calendar year 2001 is divided into 13 observation stations, the latitude, longitude of each point, LAI, the VWCI by calculating, SPSI and EWT thereof _CanopyConcrete value such as following table 3:

The vegetation water regime parameter value of table 3.2001 each observation station of Beijing in 19, on Mays

The sampling numbering	Longitude	Latitude	VWCI	SPSI	LAI	EWT canopy	FWC
								NW1-1	40°11′42.1″	116°34′01.1″	0.862698	0.473955	1.607	242.1298	372.32
NW1-2	40°11′42.6″	116°34′04.6″	0.928463	0.465037	2.483	250.3996	249.52
								NW2-1	40°11′42.1″	116°34′16.2″	0.805081	0.482557	1.487	187.6734	311.62
NW2-2	40°11′47.7″	116°34′19.1″	0.70719	0.527388	1.489	173.2655	287.33
								NW3-1	40°11′34.1″	116°34′24.8″	0.835301	0.485952	1.862	153.6023	203.70
NW3-2	40°11′34.4″	116°34′30.8″	0.704406	0.479479	1.357	124.1287	225.81
								NW3-3	40°11′34.7″	116°34′34.4″	0.915049	0.467324	2.203	212.0869	238.05
NW4-26	40°11′43.6″	116°34′34.3″	0.859784	0.467799	1.780	186.3046	258.42
								NW4-31	40°11′44.1″	116°34′37.9″	0.870286	0.455644	2.020	191.3939	233.67
NW4-34	40°11′44.0″	116°34′42.1″	0.907015	0.461642	2.067	239.0386	284.94
								NW5-55	40°11′56.9″	116°34′31.2″	0.645697	0.500237	0.735	72.36716	243.53
NW5-58	40°11′57.6″	116°34′37.1″	0.75568	0.458563	1.321	135.1022	251.92
								NW5-61	40°11′58.0″	116°34′42.4″	0.99157	0.461325	2.624	330.1371	310.83

The vertical dehydration index of short-wave infrared calculates according to following formula (III):

$\mathrm{SPSI}=\frac{1}{\sqrt{M^2+1}}(R_{\mathrm{SWIR}}+{\mathrm{MR}}_{\mathrm{NIR}})---\left(\mathrm{III}\right),$

Wherein, SPSI is the vertical dehydration index of short-wave infrared; R _NIRBe the 780-900nm near-infrared band reflectivity through atmospheric correction, described R _SWIRBe 1550-1750nm short-wave infrared wave band reflectivity through atmospheric correction; M is the NIR-SWIR baseline slope.

Vegetation moisture index calculates according to following formula (IV):

$\mathrm{VWCI}=\frac{(M_1-M)\&times;({\mathrm{NIR}}_G-M_2\&times;{\mathrm{SWIR}}_G-I_2)}{(M_1-M_2)\&times;({\mathrm{NIR}}_G-M\&times;{\mathrm{SWIR}}_G)-(M_1-M)\&times;I_2+(M_2-M)\&times;I_1}---\left(\mathrm{IV}\right)$

Wherein, VWCI is a vegetation moisture index; M is a soil baseline slope in the NIR-SWIR space; As Fig. 4, M ₁, M ₂Be respectively the slope of AB and CD line, I ₁, I ₂Be respectively the intercept of AB and CD line; NIR _G, SWIR _GAlbedo value for the near infrared and the short-wave infrared of position to be measured correspondence.

According to SPSI, the VWCI of each observation station of monitoring section and the EWT of each observation station of monitoring section _CanopySet up funtcional relationship with FMC with data processing software Excell, wherein

SPSI and EWT _CanopyFuntcional relationship as follows:

SPSI＝1.0986*EWT _canopy+16.213，R ²＝0.6864；

VWCI and EWT _CanopyFuntcional relationship as follows:

VWCI＝0.9871*EWT _canopy+9.1872，R ²＝0.7365；

The funtcional relationship of VWCI and FMC is as follows:

VWCI＝0.7525*FMC+64.449，R ²＝0.7053；

Utilize R ²With mean square root error (RMSE), the remote sensing observations data have been carried out precision analysis.The result shows that model estimate value and field observation data have higher correlativity (seeing Fig. 7,8,9).

By analyzing as seen, VWCI (VWSI), SPSI are effective to the vegetation moisture monitoring, and VWCI (VWSI) monitoring accuracy is than SPSI height.

Claims (6)

1. the method for a face of land draught monitor, be soil or the leaf water content that obtains set observation station on the face of land to be monitored, the soil of described observation station or the corresponding index of leaf water content and sign face of land damage caused by a drought are set up functional relation, bring remotely-sensed data into described functional relation, draw the soil or the plant leaf blade water cut on the face of land to be monitored;

Wherein, the corresponding index of described sign face of land damage caused by a drought is determined as follows:

1) on the face of land, farmland of vegetation coverage≤15%, adopts vertical drought index;

Described vertical drought index calculates according to following formula (I):

$\mathrm{PDI}=\frac{1}{\sqrt{M^2+1}}(R_{\mathrm{red}}+{\mathrm{MR}}_{\mathrm{nir}})---\left(I\right),$

Wherein, PDI is vertical drought index, and M is the soil line slope, R _RedBe the red spectral band reflectivity through atmospheric correction, R _NirBe near-infrared band reflectivity through atmospheric correction;

2) vegetation coverage＞15% and≤65% the face of land, farmland, adopt vegetation condition albedo drought index;

Described vegetation condition albedo drought index calculates according to following formula (II):

$\mathrm{VCADI}=\frac{A_{i,\mathrm{NDV}{I}_i}-A_{{\min ,\mathrm{NDVI}}_i}}{A_{\max ,{\mathrm{NDVI}}_i}-A_{{\min ,\mathrm{NDVI}}_i}}---\left(\mathrm{II}\right),$

Wherein, VCADI is a vegetation condition albedo drought index, $A_{\max ,\mathrm{NDV}{I}_i}=a+\mathrm{bNDV}{I}_i,$ $A_{\min ,\mathrm{NDV}{I}_i}=a^{\&prime;}+b^{\&prime;}{\mathrm{NDVI}}_i;$ A _{Min, NDVIi}And A _{Max, NDVIi}Be respectively minimum and the maximum albedo of district to be measured vegetation index when equaling certain particular value; A, b, a ', b ' is a undetermined coefficient, and a and a ' are respectively the intercepts of doing limit and wet limit in the Albedo-NDVI feature space, and b and b ' are respectively the slopes of doing limit and wet limit in the Albedo-NDVI feature space, scatter diagram by district to be measured vegetation index and albedo obtains A _{I, NDVIi}Be Albedo-NDVI feature space mid point (A _i, NDVI _i) albedo, NDVL is D (A in the Albedo-NDVI feature space _i, NDVI _i) vegetation index;

3), adopt at least a in following three kinds of indexes: the vertical dehydration index of short-wave infrared, vegetation moisture exponential sum vegetation water deficit index on the face of land, farmland of vegetation coverage＞65% to 100%;

The vertical dehydration index of described short-wave infrared calculates according to following formula (III):

$\mathrm{SPSI}=\frac{1}{\sqrt{M^2+1}}(R_{\mathrm{SWIR}}+{\mathrm{MR}}_{\mathrm{NIR}})---\left(\mathrm{III}\right)\text{,}$

Wherein, SPS worker is the vertical dehydration index of short-wave infrared, R _SWIR, R _NIRBe respectively short-wave infrared and near-infrared band reflectivity through atmospheric correction, M is a soil baseline slope in the empty palace gate of NIR-SWIR;

Described vegetation moisture index calculates according to formula (IV):

$\mathrm{VWCI}=\frac{(M_1-M)\&times;({\mathrm{NIR}}_G-M_2\&times;{\mathrm{SWIR}}_G-I_2)}{(M_1-M_2)\&times;({\mathrm{NIR}}_G-M\&times;{\mathrm{SWIR}}_G)-(M_1-M)\&times;I_2+(M_2-M)\&times;I_1}---\left(\mathrm{IV}\right)$

Wherein, VWCI is a vegetation moisture index; M is a soil baseline slope in the NIR-SWIR space; As Fig. 4, M ₁, M ₂Be respectively the slope of AB and CD line, I ₁, I ₂Be respectively the intercept of AB and CD line; NIR _G, SWIR _GAlbedo value for the near infrared and the short-wave infrared of position to be measured correspondence;

Described vegetation water deficit index calculates according to following formula (V):

VWSI＝1-VWCI (V)

Wherein, VWSI is a vegetation water deficit index; VWCI is a vegetation moisture index, calculates according to formula (IV).

2. method according to claim 1, it is characterized in that: described 1), the face of land, farmland at vegetation coverage≤15%, adopt vertical arid indices P DI, the M value that its computing formula relates to, in ruddiness-near-infrared band reflectivity two-dimensional space, the slope of the soil line that the each point of sign soil constitutes.

3. method according to claim 1 is characterized in that: described 2), vegetation coverage＞15% and≤65% the face of land, farmland, adopt vegetation condition albedo drought index VCADI; Wherein, extract slope and the intercept of doing limit, wet limit, calculate VCADI on this basis at the Albedo-NDVI feature space.

4. method according to claim 1 is characterized in that: described 3), on the farmland face of land of vegetation coverage＞65% to 100%, adopt vertical dehydration index of short-wave infrared or vegetation moisture index; Wherein the related parameter of each formula is all based on short-wave infrared-near infrared spectrum feature space.

5. method according to claim 1 is characterized in that: described R _RedBe the 630-690nm red spectral band reflectivity through atmospheric correction, described R _NirBe the 780-900nm near-infrared band reflectivity through atmospheric correction, described R _SWIRBe 1550-1750nm short-wave infrared wave band reflectivity through atmospheric correction.

6. according to arbitrary described method in the claim 1 to 5, it is characterized in that: the described face of land is the face of land, farmland.

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