This work aims at quantifying the winter wheat growth spatial heterogeneity captured by hyperspectral airborne images. The field experiment was conducted in 2001 and 2002 and airborne hyperspectral remote-sensing data was acquired at noon on 11 April 2001 using an operational modular imaging spectrometer (OMIS). Totally 12 winter fields which covered by both dense and sparse winter wheat canopies were selected to analysis the winter wheat growth heterogeneity. The experimental semi-variograms for bands covered from invisible to mid-infrared were computed for each field then the theoretical models were be fitted with least squares algorithm for spherical model, exponential model. The optimization model was selected after evaluated by R-square. Three key terms in each model, the sill, the range, and nugget variance were then calculated from the models. The study results show that the sill, range and nugget for same field wheat were varied with the wavelength from blue to mid infrared bands. Although wheat growth in different fields showed different spatial heterogeneity, they all showed an obvious sill pattern. The minimum of mean range value was 7.52 m for mid-infrared bands while the maximum value was 91.71 m for visible bands. The minimum of mean sill value ranged from 1.46 for visible bands to 39.76 for NIR bands, the minimum of mean nugget value ranged from 0.06 for visible bands to5.45 for mid-infrared bands. This study indicate that remote sensing image is important for crop growth spatial heterogeneity study. But it is necessary to explore the effect of different wavelength of image data on crop growth semi-variogram estimation and find out which band data could be used to estimate crop semi-variogram reliably.
This study focused on the wheat grain protein content (GPC) estimation based on wheat canopy chlorophyll parameters which acquired by hand-held instrument, Multiplex 3. Nine fluorescence spectral indices from Multiplex sensor were used in this study. The wheat GPC estimation experiment was conducted in 2012 at the National Experiment Station for Precision Agriculture in Changping district, Beijing. A square with area of 1.1 ha was selected and divided to 110 small plots by 10×10m in this study. In each plot, four 1-m2 area distributed in the square were selected for canopy fluorescence spectral measurements, physiological and biochemical analyses. Measurements were performed five times at wheat raising, jointing, heading stage, milking and ripening stage, respectively. The wheat plant samples for each plot were then collected after the measurement and sent to Lab for leaf N concentration (LNC) and canopy nitrogen density (CND) analyzed. GPC sampling for each plot was collected manually during the harvested season. Then, statistical analysis were performed to detect the correlation between fluorescence spectral indices and wheat CND for each growth stage, as well as GPC. The results indicate that two Nitrogen Balance Indices, NBI_G and NBI_R were more sensitive to wheat GPC than other fluorescence spectral indices at milking stage and ripening stage. Five linear regression models with GPC and fluorescence indices at different winter wheat growth stages were then established. The R2 of GPC estimated model increased form 0.312 at raising stage to 0.686 at ripening stage. The study reveals that canopy-level fluorescence spectral parameters were better indicators for the wheat group activity and could be demonstrated to be good indicators for winter wheat GPC estimation.
Winter wheat is one of the most important crops planted in Beijing suburb. In recent 20 years, winter wheat planted area
decreased obviously in Beijing area owning to the urbanization process. This study focuses on the winter wheat planted area
transformation monitoring of Beijing suburb from 1992 to 2009 through remote sensing technique. Multi-temporal Landsat-
TM images are collected during the winter wheat growth season of 1992,2000 and 2009 and used to analyze the trend and
characteristics of winter wheat field variation in Beijing suburb in recent two decades years. The PCA analysis and Tasseled
Cap transform technique are adopted in this study for feature classification. The study result shows that the winter wheat
planted area in 1992,2000 and 2009 in Beijing is 113671 ha,84322 ha and 61529 ha, respectively. It indicates that winter
wheat planting area in Beijing has a significantly decreasing trend and the total reduced area is 52143 ha from 1992 to 2009.
Winter wheat planted area is decreased by 29349 ha from 1992 to 2000. Most of reduced wheat fields are transformed into
bare land or used for urban land accounting for 42.8% and 39.7%. Others wheat fields are used for greenhouse and water
bodies (fish ponds and water fields), accounting for 13.3% and 3%. The winter wheat field decreased by 22794 ha from 2000
to 2009, more than 41.93% of wheat field is turned into bare land. Reduce field for greenhouse land and water bodies (ponds
or water fields) are account for21.61% and 7.79%, respectively.
Near-infrared micro-imaging will not only provide the sample’s spatial distribution information, but also the spectroscopic information of each pixel. In this thesis, it took the artificial sample of wheat flour and formaldehyde sodium sulfoxylate distribution given for example to research the data processing method for enhancing the quality of near-infrared micro-imaging. Near-infrared spectroscopic feature of wheat flour and formaldehyde sodium sulfoxylate being studied on, compare correlation imaging and 2nd derivative imaging were applied in the imaging processing of the near-infrared micro-image of the artificial sample. Furthermore, the two methods were combined, i.e. 2nd derivative compare correlation imaging was acquired. The result indicated that the difference of the correlation coefficients between the two substances, i.e. wheat flour and formaldehyde sodium sulfoxylate, and the reference spectrum has been increased from 0.001 in compare correlation image to 0.796 in 2nd derivative compare correlation image respectively, which enhances the imaging quality efficiently. This study will, to some extent, be of important reference significance to near-infrared micro-imaging method research of agricultural products and foods.
Infrared (IR) micro-imaging technology, also named chemical imaging, has potential applications in analytical technology. The IR micro-image provides not only the full spectrum information but also the component and structural information for each pixel point. Therefore, various groups of pesticides appear corresponding to the absorption peaks in the specific area of IR spectrum. In this study, the feasibility of using IR micro-imaging to detect chlopyrifos residuals on the surface of apple skin was explored. Different concentrations of chlorpyrifos solutions including 10,000, 1000, and 100 mg/L were sprayed on apple skin. Then IR micro-images were collected with a wavelength range of 4000 to 750 cm −1 . The IR micro-image of chlorpyrifos pure powders was also collected for the contrasted analysis of characteristic absorption peaks. The results showed that there were seven, five, and four characteristic absorption peaks attributed to chlorpyrifos in the IR spectra of apple skin that was sprayed 10,000, 1000, and 100 mg/L chlorpyrifos solutions, respectively. With the decrease of chlorpyrifos solution concentration, the number of characteristic absorption peaks decreased. When the chlorpyrifos solution concentration was as low as 100 mg/L , the peak intensity could be still recognized. The second derivative spectra further validated the above conclusions. The detection limit of quantitative analysis was approximately 0.98 mg/L . The present study indicated that rapid detection of chlorpyrifos on the surface of an apple by IR micro-imaging technology was feasible. It provided useful foundation for the detection of fruits' and vegetables' pesticide residues by IR micro-imaging technology.
An imaging spectrometer was used to acquire hyperspectral images of 120 strains of wheat ears and seeds under four different watering treatments. Whether wheat preharvest sprouting occurred could be reflected by spectral characteristics. Therefore, it was possible to judge whether wheat ears sprouted according to changes of spectral curve at 675 nm. According to principal component analysis of mean spectral reflectivity values of wheat seeds, it was found that wheat seeds watered three times every day and wheat seeds watered once every day were significantly different from nonsprouting wheat seeds soaked all day and original dry seeds due to significant sprouting situations, suggesting that imaging spectra can differentiate different extent of wheat preharvest sprouting. Glume had an influence on the hyperspectral images of wheat ears, therefore the hyperspectral images of wheat ears could be used to measure sprouting only when serious sprouting occurred. At an early stage of sprouting, only the hyperspectral images of wheat seeds could be used to analyze the sprouting of wheat.
An auto-development pushbroom imaging spectrometer (PIS) with wavelength range of 400-1000 nm was applied to
measure the chlorophyll content of wheat seedling. It showed that according to images of spectral imaging for leaves of
Chinese Spring (Salt-sensitive), Zhouyuan 9369(common and high-yield) and Changwu 134(salt-tolerant) wheat
seedling under salt stress, growth of salt-sensitive Chinese Spring wheat seedling was inhibited and it was feasible to
carry out qualitative analysis. Images could intuitively reflect morphological information of growth status of wheat
seedling and could show spectral differences of different leaves and different locations of one leave. Also, it was feasible
to identify green and yellow locations of leaf and to carry out qualitative analysis. The tested sites of spectrum and the
chlorophyll content measured sites were on the same area of single leaf. After measuring the hyperspectral image of leaf,
the mean reflectance spectra of each leaf was calculated Totally, 126 samples were collected, which were then divided
into a calibration set and a prediction set. Partial least square regression (PLSR) method was used to build the calibration
model. Results showed that the extracted hyperspectral spectra had high correlation with chlorophyll content. The
correlation coefficient of the calibration model is R=0.8138, the standard error of prediction is SEP=4.75. The results
indicated that hyperspectral imaging were suitable for the non-invasive detection of chlorophyll content of wheat
seedling.
Height is one of important parameters for evaluating winter wheat growth. It can be not only used to indicate growth
status of winter wheat, but also play a very important role in wheat growth environmental simulating models. Remote
sensing images can reflect vegetation information and variation trend on different spatial scales, and using remote
sensing has become a very important means of retrieving crop growth indices such as H(height), F(vegetation coverage
fraction), LAI(leaf area index) and so on. In the paper, firstly LAI was estimated with a gradient-expansion algorithm by
combining remote sensing images of Landsat5 TM with field data of winter wheat measured in Shunyi&Tongzhou
District, Beijing in 2008, and then applied the dimidiate pixel model with NDVI (Normalized Difference Vegetation
Index) from landsat5 TM to calculate F(vegetation coverage fraction), lastly taking the ratio of LAI and F as the factor
built the model to estimate winter wheat growth height. The result displayed that the determinant coefficient R2 arrived at
0.48 between the field measured and the fit value by the wheat height estimating model, which showed it was feasible to
apply the model with multispectral remote sensing images to estimate the wheat height.
An uneven growing winter wheat will be slower to reach full ground cover and will be lead to uneven yield and quality
for cropland. The traditional investigation of crop uniformity is mainly depends on manpower. Remote sensing technique
is a potentially useful tool for monitoring the crop uniformity status for it can provide an area global view for entire field
within the crop growth season with scathelessness. The objective of this study was to use remote sensing imagery to
evaluate the crop growth uniformity, as well as the yield and grain quality variation for a winter wheat study area. One
Quickbird image on winter wheat booting stage was collected and processed to monitoring the uniformity of wheat
growth. The results indicated that the spectrum parameters of Quickbird image can reflect the spatial uniformity of
winter wheat growth in the study areas. Meanwhile the spatial uniformity of wheat growth in early stage can reflect the
uniformity of yield and grain quality. The wheat growth information at the booting stage has strong positive correlations
with yield, and strong negative correlation with grain protein. The correlation coefficient between OSAVI (optimized
soil adjusted vegetation index) and wheat yield was 0.536. It was -0.531 for GNDVI (Greeness-normalized difference
vegetation index) and grain protein content. The study also indicated that diverse spectrum parameters had different
sensitivity to the wheat growth spatial variance. So it is feasible to use remote sensing data to investigate the crop growth
and quality spatial uniformity.
KEYWORDS: Image fusion, Information fusion, Earth observing sensors, High resolution satellite images, Distortion, Near infrared, Remote sensing, Image segmentation, 3D modeling, Sun
Shadow exists obviously in high resolution remote sensing images. Automatic extracting shadow is quite important for
removing shadow as noise or for mining shadow information. A new method of IKONOS shadow extraction in urban
region was presented in this paper based on the principal component (PC) fusion information distort. First, the NIR (near
infrared) band with more shadow information was selected for shadow extraction, and the information distort of PC
fusion was assessed; it was found that shadow was sensitive to difference index. Second, a relative difference index was
structured to enhance shadow information, as a result the values of relative difference index in shadow region were
higher and the ones in non-shadow region were lower. Third, possible shadow was distinguished from non-shadow by
threshold. Finally standard deviation was used to differentiate shadow from water for possible shadow, and the shadow
was extracted. The results show that this shadow extraction method was simple with high accuracy, not only the shadow
of high building but also that of low trees were all detected.
Construction of network clusters and identifying hub nodes from networks has attracted more and more attentions in
spatial network analysis. In this paper, we proposed clustering algorithm and outlying node detection algorithm for
spatial road network analysis. Network clustering algorithm consists of constructing clusters and creating a simplified
structure of the network. When performing clustering on the network, we introduced the definitions of strong cluster and
weak cluster, where each node has more connections within the cluster than with the rest of the graph, for achieving
reliable and reasonable clusters. For users' understanding the structure of the network, we constructed a simplified graph
approximation of the network, whose nodes were representative nodes in clusters of the network, and edges were the
connections between those representative nodes. In outlying node detection algorithm, a node is identified as an outlier,
not because of its distribution different from that of other nodes but for its unexpected statistical information. Whether a
node is an outlier or not is examined with centrality index. The larger the node has centrality indexes, the more
probabilistically it may be identified as an outlier. The experimental results on artificial data sets demonstrated that two
algorithms are very efficient and effective.
The Advanced technology in space-borne determination of grain crude protein content (CP) by remote sensing can help
optimize the strategies for buyers in aiding purchasing decisions, and help farmers to maximize the grain output by
adjusting field nitrogen (N) fertilizer inputs. We performed field experiments to study the relationship between grain
quality indicators and foliar nitrogen concentration (FNC). FNC at anthesis stage was significantly correlated with CP,
while spectral vegetation index was significantly correlated to FNC. Based on the relationships among nitrogen
reflectance index (NRI), FNC and CP, a model for CP prediction was developed. NRI was able to evaluate FNC with a
higher coefficient of determination of R2=0.7302. The method developed in this study could contribute towards
developing optimal procedures for evaluating wheat grain quality by ASTER image at anthesis stage. The RMSE was
0.893 % for ASTER image model, and the R2 was 0.7194. It is thus feasible to forecast grain quality by NRI derived
from ASTER image.
The advanced technology in site-specific and spaceborne determination of grain crude protein content (CP) by remote sensing can help optimize the strategies for buyers in aiding purchasing decisions, and help farmers to maximize the grain output by adjusting field nitrogen (N) fertilizer inputs. We performed field experiments to study the relationship between grain quality indicators and foliar nitrogen concentration (FNC). FNC at anthesis stage was significantly correlated with CP, while spectral vegetation index was significantly correlated to FNC. Based on the relationships among nitrogen reflectance index (NRI), FNC and CP, a model for CP prediction was developed. NRI was able to evaluate FNC with a higher coefficient of determination of R2= 0.7302 in Experiment A. The relationship between laboratory measured and remotely sensed FNC had a coefficient of determination of R2=0.7279 in Experiment B. The method developed in this study could contribute towards developing optimal procedures for evaluating wheat grain quality by in situ canopy-reflected spectrum and ASTER image at anthesis stage. CP derived from both in situ spectrum and the ASTER image exhibited high accuracy and the precision in Experiment C. The RMSE were 0.893 % for in situ spectrum model and 1.654 % for ASTER image model, and the R2 were 0.7661 and 0.7194 for both, respectively. It is thus feasible to forecast grain quality by NRI derived from in situ canopy-reflected spectrum and ASTER image. Our results indicated that the inversion of FNC and the evaluation of CP by NRI were surprisingly good.
Advanced technology in airborne detection of crop growth can help optimize the strategies of fertilization, and help
maximize the grain output by adjusting field inputs. In this study, Push-broom Hyperspectral Image sensor (PHI) was
used to investigate the influence of soil nitrogen supplied and variable-rate fertilization to the growth of winter wheat.
The objective was to determine to what extent the reflectance obtained in the 80 visible and near-infrared (NIR)
wavebands (from 410nm to 832nm) might be related to differences of variance of soil nitrogen and variable-rate
fertilization. Management plots were arranged at Beijing Precision Farming Experimental Station. Three flights were
made during the wheat growing season. Several field experiments, including the crop sampling, soil sampling and
variable-rate fertilization were carried out in the field. Data were analyzed for each flight and each band separately.
Some spectrum indices were derived from PHI images and statistical correlation analysis were carried out among the
spectrum indices and soil nitrogen, variable-rate fertilization amount. In addition, the spectrum indices difference
between elongation stage and grain filling stage are calculated and the correlation analysis was also carried out. The
analysis results indicated that the reflectance of winter wheat is significantly influenced at certain wavelength by the soil
nitrogen and the variable-rate fertilization. The soil nitrogen effect was detectable in all the three flights. Differences in
response due to soil nitrogen variance were most evident at spectrum indices, such as dλ red, INFLEX, Green/Red, NIRness,
DVI and RDVI. Furthermore, analysis results also indicated that the variable fertilization can reduce the growth
difference of winter wheat caused by spatial distribution difference of soil nitrogen.
Advanced site-specific determination of grain protein content by remote sensing can provide opportunities to optimize the strategies for purchasing and pricing grain, and to maximize the grain output by adjusting field inputs. Field experiments were performed to study the relationship between grain quality indicators and foliar nitrogen concentration. Foliar nitrogen concentration at the anthesis stage is suggested to be significantly correlated with grain protein content, while spectral vegetation index is significantly correlated to foliar nitrogen concentration around the anthesis stage. Based on the relationships among nitrogen reflectance index (NRI), foliar nitrogen concentration, and grain protein content, a statistical evaluation model of grain protein content was developed. NRI proved to be able to evaluate foliar nitrogen concentration with a coefficient of determination of R2= 0.7302 in year 2002. The relationship between measured and remote sensing derived foliar nitrogen concentration had a coefficient of determination of R2=0.7279 in year 2003. The results mentioned above indicate that the inversion of foliar nitrogen concentration and the evaluation of grain protein content by NRI are surprisingly good.
In this paper, a portable diagnostic instrument for crop quality analysis was designed and tested, which can measure the normalized difference vegetation index (PRI) and structure insensitive pigment index (NRI) of crop canopy in the field. The instrument have a valid survey area of 1m×1m when the height between instrument and the ground was fixed to 1.3 meter. The crop quality can be assessed based on their PRI and NRI values, so it will be very important for crop management to get these values. The instrument uses sunlight as its light source. There are six special different photoelectrical detectors within red, blue and near infrared bands, which are used for detecting incidence sunlight and reflex light from the canopy of crop. This optical instrument includes photoelectric detector module, signal process and A/D convert module, the data storing and transmission module and human-machine interface module. The detector is the core of the instrument which measures the spectrums at special bands. The microprocessor calculates the NDVI and SIPI value based on the A/D value. And the value can be displayed on the instrument's LCD, stored in the flash memory of instrument and can also be uploaded to PC through the PC's RS232 serial interface. The prototype was tested in the crop field at different view directions. It reveals the on-site and non-sampling mode of crop growth monitoring by fixed on the agricultural machine traveling in the field. Such simple instruments can diagnose the plant growth status by the acquired spectral response.
Winter wheat canopy spectrum is dominated by wheat canopy closures, in this study. Our purpose is to study the quantitative influence of canopy closures on field canopy spectrum by quantitative reduced canopy stem densities. It indicated that canopy reflectance of winter wheat under different canopy stem densities has significant difference in near infrared bands. It has line relationship between spectral reflectance of 100% canopy stem densities and spectral reflectance under canopy stem densities, all the coefficients of determination (R2) for the equations are exceeding 0.8452, and all the results are surprised well. Canopy reflectance difference of winter under different stem densities were also studied, they all have line relationships between canopy reflectance of 100% canopy stem densities and quantitative reduced canopy stem densities, the simulation equations are different for the erective cultivars and loose cultivars. Relationship between canopy closures and canopy stem densities were also studied too, it has positive relationship between canopy closures and canopy stem densities, it reveals a very good agreement between canopy closures and canopy stem densities, with a coefficient of determination (R2) 0.8467, so the canopy stem densities can be simulated by canopy closures.
Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under four levels of nitrogen and irrigation treatments. Firstly, the influence of cultivars and environment factors on grain quality were studied, the effective factors were cultivars, irrigation, fertilization, et al. Secondly, total nitrogen content around winter wheat anthesis stage was proved to be significant correlative with grain protein content, and spectral vegetation index significantly correlated to total nitrogen content around anthesis stage were the potential indicators for grain protein content. Accumulation of total nitrogen content and its transfer to grain is the physical link to produce the final grain protein, and total nitrogen content at anthesis stage was proved to be an indicator of final grain protein content. The selected normalized photochemical reflectance index (NPRI) was proved to be able to predict of grain protein content on the close correlation between the ratio of total carotenoid to chlorophyll a and total nitrogen content. The method contributes towards developing optimal procedures for predicting wheat grain quality through analysis of their canopy reflected spectrum at anthesis stage. Regression equations were established for forecasting grain protein and dry gluten content by total nitrogen content at anthesis stage, so it is feasible for forecasting grain quality by establishing correlation equations between biochemical constitutes and canopy reflected spectrum.
This study was to develop the time-specific and time-critical method to overcome the limitations of traditional field sampling methods for variable rate fertilization. Farmers, agricultural managers and grain processing enterprises are interested in measuring and assessing soil and crop status in order to apply adequate fertilizer quantities to crop growth. This paper focused on studying the relationship between vegetation index (OSAVI) and nitrogen content to determine the amount of nitrogen fertilizer recommended for variable rate management in precision agriculture. The traditional even rate fertilizer management was chosen as the CK. The grain yield, ear numbers, 1000-grain weight and grain protein content were measured among the CK, uniform treatments and variable rate fertilizer treatments. It indicated that variable rate fertilization reduced the variability of wheat yield, ear numbers and dry biomass, but it didn't increased crop yield and grain protein content significantly and did not decrease the variety of 1000-grain weight, compared to traditional rate application. The nitrogen fertilizer use efficiency was improved, for this purpose, the variable rate technology based on vegetation index could be used to prevent under ground water pollution and environmental deterioration.
A field trial was conduct to investigate the relationship between spectral feature of winter wheat canopy and LAI as well as leaf nitrogen (N) under different status of leaf water in field situation. The objective of this study is to investigate effect of water status in plants on the accuracy of estimating leaf area index (LAI) and plant nitrogen. The new defined spectral index, IAFC = (R2224-R2054)/ (R2224+R2054), where R is the reflectance at 2224nm or 2054nm, was significantly (α=0.05) or extremely significantly (α=0.01) correlated with LAI at all the six dates for water insufficient plants, but not significantly correlated for water sufficient plants at five of the six dates and the difference of leaf water content between the water insufficient plants and water sufficient plants was only about 2% at some dates. The study provided strong evidence that leaf water has a strong masking effect on the 2000-2300nm spectral feature, which could be strongly associated with LAI and leaf N even when the leaf water content was as high as about 80% if the water was insufficient for plant growth. The results indicated that the masking effect of leaf water on the 2000-2300nm spectral feature was not only dependent on the absolute plant water content but also on the water status and that remotely sensed data in the 2000-2300nm region could be of potential in monitoring plant canopy biophysics and biochemistry in drought condition.
Investigations have been made on agronomy parameters as leaf area index (LAI), chlorophyll content (Chl), total Nitrogen (TN) and specific leaf weight (SLW) to describe growth status of winter wheat. More comprehensive parameters such as chlorophyll index (CI), projective chlorophyll index (CIp), Nitrogen index (NI) and projective Nitrogen index (NIp) have been developed to describe the dynamic growth information for foliage vertical layers by studying their vertical distribution characteristics along canopy and their spectral reflectance. Results are that from the canopy top to the ground surface, TN and Chl have shown an obvious gradient decreasing trend, while LAI and SLW have shown the ovate distribution. Compared with NI, CI and LAI, the absolute values of NIp, CIp and LAIp are more affected by canopy shape. The ratio of NIp to NI in different layers of erective varieties is significantly lower than that of loose varieties. Correlation analysis between canopy spectral reflectance and those developed parameters in different foliage layers at stage of anthesis shows that foliage Chl in upper layer is very sensitive to 400 nm-750 nm and 1300 nm-1750 nm while that in the middle layer is very sensitive to 750 nm -1300 nm. Higher correlation coefficient between spectral reflectance and TN is found in middle-under layer and it decreases upward.
The hyperspectral image used in this study was acquired by the airborne operative modular imaging spectrometer (OMIS) in Xiaotangshan area, Beijing, on April 26th, 2001. Accurate geometry correction and reflectance transformation was conducted on this image so that 43 image spectra were extracted to match with the canopy-level total nitrogen concentration (TN) of wheat precisely. By using methods of stepwise regression and spectrum feature analysis, characteristic bands and parameters were selected and developed for TN retrieval from the image spectra. Nitrogen distribution map was obtained by applying the best estimation equation to all wheat pixels. It turned out, the absorption depths and areas within spectral ranges 590-756nm,1096-1295nm and 1295-1642nm could be used to estimate TN. NDVI(NRCA1175.8,NRCA733.9) and NDVI(dr745,dr699.2) was the best estimator of TN (R2 = 0.8145 and 0.769 respectively). In addition, the value and distribution of TN map was quite consistent with the field measurements and growth status.
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