AU2021101693A4 - Method for determining water use efficiency of vegetation layer and evapotranspiration water-gross primary production-water use efficiency measuring device - Google Patents
Method for determining water use efficiency of vegetation layer and evapotranspiration water-gross primary production-water use efficiency measuring device Download PDFInfo
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Abstract
OF THE DISCLOSURE
The disclosure discloses a method for determining water use efficiency of
vegetation layer. The method includes the following. Gross primary production (GPP) is
calculated based on a temperature and greenness (TG) model, and evapotranspiration water
(ET) is calculated based on improved Maximum Entropy Production (MEP) model. The
input parameters adopted in the improved maximum entropy production model include
land surface specific humidity, land surface temperature, vegetation leaf area, net radiation
of land surface, net radiation of vegetation, air specific humidity and air temperature. The
input parameters adopted for the improved MEP model include land surface specific
humidity, vegetation leaf area, net radiation of land surface, net radiation of vegetation, air
specific humidity, and air temperature, and which are collected in the measured region on
the spot in real time. By coupling ET and GPP, the inconsistency between ET and GPP
models are avoided.
Canopy layer
Lower layer
Shrub layer
Land surface layer
FIG. 2
Description
Canopy layer
Lower layer
Shrub layer
Land surface layer
FIG. 2
106844-AU-PA-U
Field of the Disclosure
[0001] The disclosure belongs to the field of protection of agricultural and forestry water
resources, and more specifically, relates to a method for determining water use efficiency
of vegetation layer and an evapotranspiration water-gross primary production-water use
efficiency (ET-GPP-WUE) measuring device.
Description of Related Art
[0002] Evapotranspiration (ET) is the second largest water flux in the terrestrial water
loop, accounting for about 60%-70% of precipitation. In the method of calculating ET,
conventionally, actual evapotranspiration data is acquired mainly from ground observation
data, and the models are mostly established on water vapor transportation and energy
balance constraints and calculated by ground meteorological stations. Through the above
methods, all the obtained data is associated with crop evaporation as reference, and there
is a lack of actual evapotranspiration. Currently the method for determining gross
primary production (GPP) of terrestrial ecosystem is performed mainly based on
estimation of satellite remote sensing data and models varying permeability (VPM) model,
Eddy Covariance-Light Use efficiency (EC-LUE), temperature and greenness (TG) model,
etc.) with lower resolution, which has a reduced accuracy of result, and results in an
increase in the uncertainty of runoff communities, watersheds and local scales, making it difficult to meet actual application requirements.
[0003] One of the ecosystems WUE (Water Use Efficiency) is defined as GPP/ET. In
2005, Rahman et al. proposed obtaining GPP based on the observation data of the Eddy
Covariance (EC) flux tower. Such method incorporates vegetation index and ground
temperature, establishes a temperature and greenness model (TG model), and estimates the
GPP of deciduous and evergreen forests. The input parameters used in the above method
include are leaf area index (greenness value), land surface temperature, and unit conversion
coefficient m. In 2011, Wang JF et al. established a land surface latent heat
(evapotranspiration) estimating method that is performed based on Maximum Entropy
Production (MEP) theory. The method calculates ET, and the input parameters adopted
include net radiation, land surface temperature and land surface specific humidity.
[0004] In the related art, in the WUE calculation process, among the three input
variables of the TG model, the scalar m is typically obtained by calculating the annual
average nighttime land surface temperature, and the land surface temperature and leaf area
index are obtained and calculated based on remote sensing products. Among the three
input variables in the MEP model, typically the net radiation and surface temperature
variables are directly provided through the EC-related data set FLUXNET2015, and the
specific humidity is calculated based on the Clausius-Clapeyron equation. The data
sources of the TG model and the MEP model are different, which leads to internal
inconsistencies in the data during the WUE calculation process, which in turn affects the
calculation accuracy of the WUE. In addition, most of the existing methods are
performed based on remote sensing data, and such data has low resolution and cannot be
easily obtained. Besides, the reliability of the remote sensing data remains uncertain if
the influence of weather factor is taken into consideration. The GPP product obtained through historical data can hardly make a reasonable prediction for current situation, and the validity of analysis result is thus affected.
[0005] In view of the shortcomings and improvement requirements of the related art,
the disclosure provides a method for determining water use efficiency of vegetation layer
and an evapotranspiration water-gross primary production-water use efficiency (ET-GPP
WUE) measuring device. The purpose of the disclosure is to calculate WUE by coupling
ET and GPP through land surface temperature T and leaf area. The method of the
disclosure requires less variables (only temperature, specific humidity, leaf area, etc.), has
high spatial resolution, and is more adaptable for field/forest scales, large regional scales,
etc.
[0006] In order to achieve the above purpose, according to the first aspect of the
disclosure, a method for determining the water use efficiency of vegetation layer is provided. The water use efficiency (WUE) is the ratio of the gross primary production
(GPP) and the evapotranspiration water (ET).
[0007] The method includes the following. The GPP is calculated based on a
temperature and greenness model, and the ET is calculated based on an improved
maximum entropy production model; the input parameters adopted in the improved maximum entropy production model include a land surface specific humidity, a land
surface temperature, a vegetation leaf area, a net radiation of land surface, a net radiation
of vegetation, an air specific humidity and an air temperature. The formula for
calculating the evapotranspiration water ET in the improved maximum entropy production
model is as follows:
[0008] ET = (1 - )x E, + x E
[0009] In the formula, E, represents soil water evaporation, Ev represents vegetation
transpiration S, represents the vegetation leaf area in the measured region, and B
represents the area of the measured region.
[0010] The input parameters adopted for the improved maximum entropy production
model include a land surface specific humidity, a vegetation leaf area, a net radiation of
land surface, a net radiation of vegetation, an air specific humidity, and an air temperature,
and the above input parameters are collected from the vegetation layer in the measured
region on the spot in real time. The input parameter adopted for the temperature and
greenness model includes a leaf area index, which is obtained by calculating the vegetation
leaf area collected on the spot in real time. The land surface temperature as the input
parameter adopted for the improved maximum entropy production model and the land
surface temperature as the input parameter adopted for the temperature and greenness
model are collected from the measured region by using the same temperature sensor on the
spot in real time.
[0011] Preferably, the calculation formula for vegetation transpiration Ev is as follows:
[0012] Ev = +Rn
[0013] B(a1) = 6 1+ ai -1
[0014] = - cpRy TS
[0015] In the formula, R, 1 represents the net radiation of vegetation, B(-) represents
the reciprocal of Bowen ratio, o, represents the dimensionless function of air temperature
and surface water vapor density, k represents the latent heat of water phase change, Rv represents the constant of water vapor, c, represents air specific heat under normal pressure, qsi represents air specific humidity, and Ti represents air temperature.
[0016] In order to achieve the above purpose, according to the second aspect of the
disclosure, an ET-GPP-WUE measuring device adaptable for different vegetation layers is
provided, and the device includes: a fixing mechanism, an adjusting mechanism, a
measuring mechanism, an integrated control center and a power supply.
[0017] The fixing mechanism is a height-adjustable telescopic rod. During the
measurement, one end of the fixing mechanism is inserted into the measured region to fix
the entire measuring device. The height of the fixing mechanism is adjusted according
to the height of the vegetation layer in the measured region, and the other end of the fixing
mechanism is connected with the adjusting mechanism.
[0018] The adjusting mechanism includes a support and a bearing, the support is
connected to the fixing mechanism, and the bearing is connected to the measuring
mechanism, which is configured to adjust the measuring angle of the measuring
mechanism.
[0019] The measuring mechanism includes: a land surface specific humidity collector,
a land surface temperature collector, a vegetation leaf area collector, a net radiation
collector of land surface, a net radiation collector of vegetation, an air specific humidity
collector and an air temperature collector, which carry out instant communication through
Bluetooth and an integrated control center.
[0020] The integrated control center adopts the method described in the first aspect to
determine ET, GPP and WUE.
[0021] The power supply is connected to the measuring mechanism and the integrated
control center, and supplies power to them during the measurement process.
[0022] Preferably, the land surface specific humidity collector, the land surface
temperature collector, the net radiation collector of land surface are integrated in the same
position, and the net radiation collector of vegetation, the air specific humidity collector
and the air temperature collector are integrated in the other same position. The integrated
collector that collects the land surface is close to the land surface, and the integrated
collector that collects vegetation/air is close to and above the vegetation layer, and they are
typically disposed at a position which is 1 to 2 m above the vegetation layer.
[0023] Preferably, the integrated control center transmits the measurement results of ET,
GPP, and WUE to the data memory or database in a wired or wireless manner.
[0024] Preferably, the vegetation leaf area collector includes: a laser scanner and a
vegetation leaf area calculating module.
[0025] The laser scanner is configured to emit two parallel laser beams, which are
emitted onto the surface of the leaves in the forest/field to collect leaf images.
[0026] The vegetation leaf area calculating module is configured to calculate the
vegetation leaf area based on the leaf image. The calculation formula is as follows:
[0027] S, = (a * d * d)/(s * s)
[0028] In the formula, a represents the pixel of the leaf on the image, d represents the
distance between the two laser points, and s represents the distance between the light points
on the image.
[0029] Preferably, the vegetation layer temperature collector, the air specific humidity
collector, and the net radiation collector of vegetation layer respectively adopt an infrared
temperature sensor, a near infrared humidity sensor (eg. Finna Sensors), and a net radiation
meter.
[0030] Preferably, the device further includes a display module for visually displaying net radiation, specific humidity, vegetation leaf area, leaf area index, surface temperature, and measurement results of ET, GPP, and WUE. In summary, through the above technical solutions conceived in the disclosure, the following advantageous effects can be achieved:
[0031] (1) The disclosure adopts the temperature and greenness model to calculate GPP
and adopts an improved maximum entropy production model to calculate ET. The input
parameters adopted for the improved maximum entropy production model include land
surface specific humidity, vegetation leaf area, net radiation of land surface, net radiation
of vegetation, air specific humidity and air temperature, which are collected from the
vegetation layer in the measured region on the spot in real time. The leaf area index as
the input parameter of the temperature and greenness model is obtained by calculating the
vegetation leaf area collected on the spot in real time. The land surface temperature as
the input parameter adopted for the improved maximum entropy production model and the
land surface temperature as the input parameter adopted for the temperature and greenness
model are collected from the measured region by using the same temperature sensor on the
spot in real time. Changes in temperature and leaf area take place in a continuous state,
and it is difficult for remote sensing data to reflect the changes through continuous
monitoring. By coupling ET and GPP through surface temperature T and leaf area, and
adopting the data that is measured on the spot in real time, the disclosure can avoid the
inconsistency between ET and GPP models which are internally independent from each
other. Accordingly, the method of the disclosure has high simulation accuracy, requires
less variables, can be easily implemented, is more reliable and less susceptible to weather,
and can be easily obtained.
[0032] (2) The disclosure improves the MEP model, which not only takes into consideration the influence of temperature and solar radiation on the model, but also takes into account the influence of vegetation coverage on the estimation of ET. The improved
MEP model only needs leaf area index, surface temperature, specific humidity, and net
radiation to calculate the continuous change of ET.
[0033] (3) The disclosure forms a data database by transmitting data to the terminal in
a wireless manner, and can perform in-depth analysis through the online analytical
processing (OLAP) technology of data collection, which is more conducive to in-depth
evaluation of the coupling effect of ET and GPP.
[0034] FIG. 1 is a flowchart of a method for determining water use efficiency of
vegetation layer provided in the disclosure.
[0035] FIG. 2 is a schematic structural diagram of an ET-GPP-WUE measuring device
adaptable for different vegetation layers provided in the disclosure.
[0036] In order to achieve the objectives, technical solutions and advantages of the
present disclosure clearer, the following further describes the present disclosure in detail
with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present
disclosure, but not to limit the present disclosure. In addition, the technical features
involved in the various embodiments of the present disclosure described below can be
combined with each other as long as they do not conflict with each other.
[0037] As shown in FIG. 1, the disclosure provides a method for determining water use
O efficiency of the vegetation layer. Water use efficiency (WUE) is the ratio of the gross primary production (GPP) and the evapotranspiration water (ET).
[0038] The method includes the following. The GPP is calculated based on a
temperature and greenness model, and the ET is calculated based on an improved
maximum entropy production model; the input parameters adopted in the improved
maximum entropy production model include a land surface specific humidity, a land
surface temperature, a vegetation leaf area, a net radiation of land surface, a net radiation
of vegetation, an air specific humidity and an air temperature. The formula for
calculating the evapotranspiration water ET in the improved maximum entropy production
model is as follows:
[0039] ET =1 - Bx E, + XE
[0040] In the formula, E, represents soil water evaporation, E. represents vegetation
transpiration, S, represents the vegetation leaf area in the measured region, and B
represents the area of the measured region.
[0041] The input parameters adopted for the improved maximum entropy production
model include the land surface specific humidity, the vegetation leaf area, the net radiation
of land surface, the net radiation of vegetation, the air specific humidity, and the air
temperature, and the above input parameters are collected from the vegetation layer in the
measured region on the spot in real time. The input parameter adopted for the
temperature and greenness model includes leaf area index, which is obtained by calculating
the vegetation leaf area collected on the spot in real time. The land surface temperature
as the input parameter adopted for the improved maximum entropy production model and
the land surface temperature as the input parameter adopted for the temperature and
greenness model are collected from the measured region by using the same temperature sensor on the spot in real time.
[0042] Preferably, the calculation formula for vegetation transpiration E, is as follows:
[0043] E, = Ba 1
[0044] B(a) = 6 1+ ai
[0045] ai =X2 cpRy Tsi
[0046] In the formula, Rn represents the net radiation of vegetation, B(-)represents the
reciprocal of Bowen ratio, o, represents the dimensionless function of air temperature
and surface water vapor density, X represents the latent heat of water phase change, R,
represents the constant of water vapor, cp represents air specific heat under normal
pressure, qsi represents air specific humidity, and Tsi represents air temperature.
[0047] As shown in FIG. 2, the disclosure provides an evapotranspiration water-gross
primary production-water use efficiency (ET-GPP-WUE) measuring device adaptable for
different vegetation layers, and the measuring device mainly includes: an infrared
temperature sensor, a near infrared (NIR) humidity sensor (eg.Finna Sensors), a net
radiation meter, a graph acquisition and processing system, an integrated control center, a
database storage system, a data transmission interface, a video monitoring system, a
display, a laser tube, a housing, and a bracket.
[0048] The image acquisition and processing system adopt advanced Internet of Things,
cloud computing, big data, Internet and other information technologies to analyze and
process information related to vegetation layer altogether, such as temperature, humidity,
net radiation, and leaf area index in the detected region. The results obtained through
analysis of the system are presented to managers in an intuitive way. The managers can
be informed of GPP, ET, WUE and other information from data processing results through
1 nl real-time video. The image acquisition and processing system can facilitate improving the ability to manage water and carbon resources.
[0049] Specifically, the infrared temperature sensor can extract multi-point surface
temperature information from the vegetation layer information. The near infrared (NIR)
humidity sensor is configured to extract multi-point surface specific humidity from the
vegetation layer information.
[0050] The laser tube is fixed on the bracket and emits two parallel laser beams, which
are emitted onto the surface of leaf in the forest/field. The camera collects images. The
image processing system analyzes the collected status information and process quantity
information to obtain feature parameters and thresholds.
[0051] Suppose the distance between two laser points is d, the distance between the light
points on the image is s, and the pixel of leaf on the image is a, then the calculation formula
for leaf area Sv is as follows.
[0052] Sv = (a x d x d)/(s x s)
[0053] In the formula, the pixel a is obtained by program counting, and the distance s is
obtained by the image processing program, and the gray-gravity formula is adopted.
ZfZfj (T-f ij)
[0054] x, =' 19
Zif If iTf
[0055] yc 0J f 'fZf(T-f ij)
[0056] In the formula, io is the first coordinate of the i-th row, if is the last coordinate
of the i-th row, jo is the first coordinate of the j-th column, jf is the last coordinate of
the j-th column, T is the light spot image gray-scale threshold, f is the gray-scale value of
each pixel point, and x and y are the gray-scale center coordinates of the light point. The difference between the gray-gravity coordinates of the two laser points formed by the two beams emitted by laser light is the distance s.
[0057] The accuracy of the gray-scale center of gravity algorithm is 0.02 pixels, and
each pixel in the image processing system corresponds to 0.1 mm, and the light spot
calibration accuracy of this device is 0.002 mm.
[0058] LAI (leaf area index) or greenness value can be calculated according to the
formula LAI = S,/B. In the formula, B is the area of entire region occupied by the
relative plant's leaf area in the scanner; S, is the corresponding area of the plant's leaf area
occupied in the raster image in the image presented by the scanner. That is, the area of
the non-white part occupied in the entire image layer is calculated by the image processing
system.
[0059] The method of using the integrated acquisition device for plant's leaf
transpiration, gross primary production, and water use efficiency is as follows. A non
contact multi-sensor (infrared temperature sensor, a near infrared (NIR) humidity sensor,
laser scanner) is adopted to acquire the temperature, humidity, leaf area and so on of the
field vegetation layer. Based on the characteristics of the set platform, the vegetation
layer LAI (leaf area index or greenness value) is calculated according to the type of
vegetation. Multi-point surface temperature information is extracted from the vegetation
layer information, and the gross primary production based on the temperature and
greenness model is obtained. The required net radiation is collected and processed by a
net radiometer. Multi-point surface specific humidity is extracted from the vegetation
layer information, and is calculated based on actual evapotranspiration of the improved
maximum entropy production model, thereby calculating the water use efficiency
according to GPP/ET. The analysis and storage of data in the disclosure can be wirelessly transmitted to the data memory or database of the notebook computer terminal that is provided.
[0060] The improved maximum entropy production (MEP) evapotranspiration model is
established on basis of Bayesian probability theory, information entropy concepts, non
equilibrium thermodynamics theory and atmospheric boundary layer turbulence similarity
theory, thus a brand new land surface evapotranspiration theory framework is established
and overcomes the main defects of conventional models. The calculation formula of the
MEP-ET model is as follows.
[0061] Es = B(or)H
[0062] Q= IHI6H 0J Io
[0063] Es+H+Q= Rn
[0064] B(u) = 6 (1+ a
[0065] In the formula, H is the (turbulent) sensible heat flux entering the atmosphere, Q
is the conductive heat flux entering the surface medium (soil or vegetation), Rn represents
the net radiation of land surface, B(-) represents the reciprocal of Bowen ratio, a represents
the dimensionless function of surface temperature and surface water vapor density, which
is an important parameter in the model. The parameter a quantitatively describes the
relative importance of surface moisture temperature condition to the evapotranspiration
process, especially the dominating role of water phase change in the surface energy balance
and the exchange process of ground air, water, and heat. See the following formula for
details.
or =Va Pqs
[0066] =R$T2
[0067] In the formula, qs is the air specific humidity on the evaporating surface; Ts (K) is the surface temperature of the evaporator; X is the latent heat of water phase change
(J/kg); cp is the air (constant pressure) specific heat; R, is the air constant of water vapor
[461 J/(kg-K)]; a is the ratio of the turbulent diffusivity of water vapor in the boundary
layer to the thermal diffusivity. Theoretically, the two diffusivities can be different, and
it is generally assumed that c=1.
[0068] Ev = n 1+B(c)
[0069] ET = (1 - )x Es + x Ev
[0070] LAI is the measured value, Es is soil evaporation, Evis vegetation emission, E is
evapotranspiration rate (latent heat flux), S, is vegetation area, and B is the area of entire
region occupied by the relative plant's leaf area in the scanner.
[0071] The calculation of GPP adopts the TG (temperature and greenness) model. The
TG model is a GPP estimation model based on remote sensing and does not require ground
observation data as model input. TG model is a GPP model based entirely on remote
sensing data.
[0072] GPP = mX (LAIscaled X LSTscaled)
[0073] LAIscaled = LAI - 0.1
[0074] LSTscaled = min[(LST/30); (2.5 - (0.05LST))|
[0075] In the formula, the parameter m is obtained through model calibration; LST
(Land surface temperature) is the land surface temperature.
[0076] The integrated control center is the core module of the disclosure. On the one
hand, the integrated control center receives data information from a variety of sensors, and
performs signal processing, feature extraction, threshold calculation and other operations
on the sensing data; on the other hand, the integrated control center sends vegetation layer
information and original data used in the online monitoring process to the terminal through
1 A the data analysis results.
[0077] The measurement result is displayed on the terminal of notebook computer, and
transmitted to the database storage system in a wired or wireless manner through the data
transmission interface. The measurement result can be record on the U disk file by the
serial port (USB) module, and the serial port secure digital (SD) module can record the
measurement result on the SD card file. The serial port wireless module can be connected
to the server database wirelessly, and can also be connected to the Internet through the
demand-side platform (DSP) network interface. Transmission can be realized through
Bluetooth transmission, wireless network and so on. The data transmission interface is mainly configured for data communication between the integrated control center and the
video monitoring center.
[0078] The information of vegetation layer in forests and fields is acquired through non
contact multi-sensors. Through the configured platform, the laser is emitted onto the
surface of leaf in forests and fields, and then the acquired feature parameters and thresholds are processed through the image acquisition and processing system. The processed data
is transmitted to the integrated control center to calculate and obtain the leaf area index (or
greenness value) of the vegetation layer. The infrared temperature sensor and the near
infrared (NIR) humidity sensor transmit the acquired surface temperature information and
specific humidity to the integrated control center, and perform calculation to obtain the actual T value based on the improved maximum entropy production (MEP) model. In
the integrated control center, the leaf area index (or greenness) is coupled to the multi
point surface temperature information, thereby obtaining GPP based on the temperature
and greenness model, and finally the water use efficiency is obtained according to GPP/ET.
[0079] T and GPP are coupled together for collective acquisition and rapid calculation.
Such method and device have high temporal and spatial resolution and a certain degree of
spatial integration (that is, the entire forest canopy is regarded as a whole, rather than
focusing on a single plant). In addition, "near-Earth" remote sensing can be more specific
on the target than satellite remote sensing. High-resolution digital images can also be
used as ground verification information, and digital image processing is much easier than
satellite image processing. Such method and device will have a broad and far-reaching
impact on the estimation and application of GPP in the future.
[0080] Those skilled in the art can easily understand that the above descriptions are only
preferred embodiments of the present disclosure and are not intended to limit the present
disclosure. Any modification, equivalent replacement and improvement, etc. made
within the spirit and principle of the present disclosure should fall within the protection
scope of the present disclosure.
Claims (5)
1. A method for determining water use efficiency of a vegetation layer, wherein a
water use efficiency (WUE) is a ratio of a gross primary production (GPP) and an
evapotranspiration water (ET), and the method comprises the following:
the GPP is calculated based on a temperature and greenness model, and the ET is
calculated based on an improved maximum entropy production model, input parameters
adopted in the improved maximum entropy production model comprise a land surface
specific humidity, a land surface temperature, a vegetation leaf area, a net radiation of land
surface, a net radiation of vegetation, an air specific humidity and an air temperature,
wherein a formula for calculating the evapotranspiration water ET in the improved
maximum entropy production model is as follows:
(SV SV ET= (1 B)X Es +-Bx E
wherein, E, represents soil evaporation, Ev represents vegetation emission, S,
represents a vegetation leaf area in a measured region, and B represents an area of the
measured region;
the input parameters adopted for the improved maximum entropy production model
comprise the land surface specific humidity, the vegetation leaf area, the net radiation of
land surface, the net radiation of vegetation, the air specific humidity, and the air
temperature, and the above input parameters are collected from a vegetation layer in the
measured region on the spot in real time; an input parameter adopted for the temperature
and greenness model comprises a leaf area index, which is obtained by calculating the
vegetation leaf area collected on the spot in real time; the land surface temperature as the
input parameter adopted for the improved maximum entropy production model and the
land surface temperature as the input parameter adopted for the temperature and greenness model are collected from the measured region by using a same temperature sensor on the spot in real time.
2. The method according to claim 1, wherein a calculation formula for vegetation
emission E, is as follows:
E, Rni 1 + B( 1
) B (u1) = 6 1+ o6 -Y 1
A2 qs 1
wherein, Rni represents the net radiation of vegetation, B() represents a reciprocal
of Bowen ratio, ai represents a dimensionless function of the air temperature and a
surface water vapor density, X represents a latent heat of a water phase change, R,
represents a constant of water vapor, c, represents an air specific heat under a normal
pressure, qsl represents an air specific humidity, and Tsi represents the air temperature.
3. An evapotranspiration water-gross primary production-water use efficiency (ET
GPP-WUE) measuring device adaptable for different vegetation layers, wherein the device
comprises a fixing mechanism, an adjusting mechanism, a measuring mechanism, an
integrated control center and a power supply;
wherein the fixing mechanism is a height-adjustable telescopic rod, during a
measurement process, one end of the fixing mechanism is inserted into the measured region
to fix the entire measuring device, a height of the fixing mechanism is adjusted according
to a height of the vegetation layer in the measured region, and the other end of the fixing
mechanism is connected with the adjusting mechanism;
the adjusting mechanism comprises a support and a bearing, the support is connected to the fixing mechanism, and the bearing is connected to the measuring mechanism, which is configured to adjust a measuring angle of the measuring mechanism; the measuring mechanism comprises a land surface specific humidity collector, a land surface temperature collector, a vegetation leaf area collector, a net radiation collector of land surface, a net radiation collector of vegetation, an air specific humidity collector and an air temperature collector, which carry out instant communication through Bluetooth and the integrated control center; the integrated control center adopts the method claimed in claim 1 or 2 to determine
ET, GPP and WUE; the power supply is connected to the measuring mechanism and the integrated control
center, and supplies power to them during the measurement process.
4. The device according to claim 3, wherein the land surface specific humidity
collector, the land surface temperature collector, the net radiation collector of land surface
are integrated in the same position, and the net radiation collector of vegetation, the air specific humidity collector and the air temperature collector are integrated in the same
position.
5. The device according to claim 3, wherein the vegetation layer temperature
collector, the air specific humidity collector, and the net radiation collector of vegetation
layer respectively adopt an infrared temperature sensor, a near infrared humidity sensor ,
and a net radiation meter,
wherein the integrated control center transmits measurement results of ET, GPP, and
WUE to a data memory or a database in a wired or wireless manner,
wherein the device further comprises a display module for visually displaying a net
radiation, a specific humidity, a vegetation leaf area, a leaf area index, a surface temperature, and the measurement results of ET, GPP, and WUE.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011305367.6A CN112395765A (en) | 2020-11-19 | 2020-11-19 | Method for determining vegetation layer water utilization efficiency and ET-GPP-WUE measuring device |
| CN202011305367.6 | 2020-11-19 |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115795819A (en) * | 2022-11-11 | 2023-03-14 | 南京大学 | Water utilization efficiency simulation method based on SEBS model and CASA model |
| CN117314184A (en) * | 2023-09-19 | 2023-12-29 | 沈阳农业大学 | Method, device and equipment for determining time-space change result of annual total primary productivity |
| CN117314184B (en) * | 2023-09-19 | 2024-04-30 | 沈阳农业大学 | Method, device and equipment for determining time-space change result of annual total primary productivity |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113034302B (en) * | 2021-03-11 | 2022-03-08 | 中国水利水电科学研究院 | Remote sensing evaluation method for farmland scale crop water utilization efficiency |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US8712592B2 (en) * | 2011-03-29 | 2014-04-29 | Hydropoint Data Systems, Inc. | Controlling a resource demand system |
| CN203100701U (en) * | 2013-03-22 | 2013-07-31 | 胡长华 | Portable non-contact leaf area measuring instrument |
| CN103630091B (en) * | 2013-11-05 | 2017-02-08 | 河南理工大学 | Leaf area measurement method based on laser and image processing techniques |
| CN105466558B (en) * | 2015-11-16 | 2018-01-05 | 北京师范大学 | Vegetation canopy photosynthetically active radiation absorptance and surface albedo observation system and method |
| CN109405769A (en) * | 2018-12-10 | 2019-03-01 | 中国气象局兰州干旱气象研究所 | Vegetation canopy leaf area index measuring device |
| CN110147620B (en) * | 2019-05-23 | 2021-12-10 | 中国科学院西北生态环境资源研究院 | Freezing ring hydrological process analysis method and system |
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2020
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115795819A (en) * | 2022-11-11 | 2023-03-14 | 南京大学 | Water utilization efficiency simulation method based on SEBS model and CASA model |
| CN117314184A (en) * | 2023-09-19 | 2023-12-29 | 沈阳农业大学 | Method, device and equipment for determining time-space change result of annual total primary productivity |
| CN117314184B (en) * | 2023-09-19 | 2024-04-30 | 沈阳农业大学 | Method, device and equipment for determining time-space change result of annual total primary productivity |
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