[Grain yield estimation of wheat-maize rotation cultivated land based on Sentinel-2 multi-spectral image: A case study in Caoxian County, Shandong, China]. / 基于Sentinel-2å¤šå ‰è°±å½±åƒçš„å°éº¦-玉米轮作耕地粮食产量估测­­ä»¥æ›¹åŽ¿ä¸ºä¾‹.

Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm (R2=0.843, root mean square error=2.822 kg·hm-2), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation.

[Remote sensing inversion of cultivated land fertility at county scale based on SWCI-NDVI feature space]. / 基于SWCI-NDVI特征空间的县域耕地地力遥感反演.

It is objective needs during utilization and management of regional cultivated land resource to use remote sensing to accurately and efficiently retrieve the status of cultivated land fertility at county level and realize the gradation of cultivated land rapidly. In this study, with Dongping County as a case, using Landsat TM satellite imagery and cultivated land fertility evaluation data, the moisture vegetation fertility index (MVFI) was constructed based on surface water capacity index (SWCI) and normalized difference vegetation index (NDVI), and then the optimal inversion model was optimized to obtain the best inversion model, which was further applied and verified at the county scale. The results showed that the correlation coefficient between MVFI and integrated fertility index (IFI) was -0.753, which could comprehensively reflect the growth of winter wheat, soil moisture and land fertility, and had clear biophysical significance. The best inversion model was the quadratic model, with high inversion accuracy. This model was suitable for the inversion of cultivated land fertility in the county. The spatial distribution and uniformity of the inversion results were similar to the results of soil fertility evaluation. The area differences between the high, medium and low grades were all less than 2.9%. This study provided a remote sensing inversion method of cultivated land fertility based on the feature space theory, which could effectively improve the evaluation efficiency and prediction accuracy of cultivated land fertility at the county scale.

[Multi-scale spatial variability of soil salinity in typical fields of the Yellow River Delta in summer]. / é»„æ²³ä¸‰è§’æ´²å¤å­£å ¸åž‹ç”°å—åœŸå£¤ç›åˆ†çš„å¤šå°ºåº¦ç©ºé—´å˜å¼‚.

Soil salinization severely hinders the development of agricultural economy in the Yellow River Delta. Clarifying the spatial variability of soil salinity at multiple scales in the field is of great significance for the improvement and utilization of saline soils and agricultural production. In this study, by dividing the three dimensions of field, plot and ridge, we collceted 152 sets of conducti-vity data through field survey sampling in a summer maize field in Kenli County of the Yellow River delta. The methods of classic statistics, geostatistics and Kriging interpolation were used to analyze the spatial variability and scale effects of multi-scale soil salt in the field. The results showed that soil in this area was moderately salinized, with the extent of soil salinity moderately varying at three scales. From the field, plot to the ridge scale, with the decreases of sampling scale, the variability of soil salinity increased and the standard deviation increased. The ridge and plot scales showed strong spatial correlation. The optimal model was Gaussian model, which was mainly affected by structural factors. The field scale was of medium spatial correlation, with exponential model as the optimal one, which was influenced by both random factors and structural factors. The spatial distribution characteristics of soil salinity at different scales were significantly different. The spatial chara-cteristics at small scale were masked at large scale, showing obvious scale effect. The distribution of soil salinity at the micro-ridge scale between ridges had obvious variation. Soil salt content gradually decreased with the micro-topography from high to low, while vegetation coverage changed from sparse to dense.

Improving the Accuracy of the Hyperspectral Model for Apple Canopy Water Content Prediction using the Equidistant Sampling Method.

The influence of the equidistant sampling method was explored in a hyperspectral model for the accurate prediction of the water content of apple tree canopy. The relationship between spectral reflectance and water content was explored using the sample partition methods of equidistant sampling and random sampling, and a stepwise regression model of the apple canopy water content was established. The results showed that the random sampling model was Y = 0.4797 - 721787.3883 × Z3 - 766567.1103 × Z5 - 771392.9030 × Z6; the equidistant sampling model was Y = 0.4613 - 480610.4213 × Z2 - 552189.0450 × Z5 - 1006181.8358 × Z6. After verification, the equidistant sampling method was verified to offer a superior prediction ability. The calibration set coefficient of determination of 0.6599 and validation set coefficient of determination of 0.8221 were higher than that of the random sampling model by 9.20% and 10.90%, respectively. The root mean square error (RMSE) of 0.0365 and relative error (RE) of 0.0626 were lower than that of the random sampling model by 17.23% and 17.09%, respectively. Dividing the calibration set and validation set by the equidistant sampling method can improve the prediction accuracy of the hyperspectral model of apple canopy water content.

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

[Soil Salinity Estimation Based on Near-Ground Multispectral Imagery in Typical Area of the Yellow River Delta].

This study chooses the core demonstration area of 'Bohai Barn' project as the study area, which is located in Wudi, Shandong Province. We first collected near-ground and multispectral images and surface soil salinity data using ADC portable multispectral camera and EC110 portable salinometer. Then three vegetation indices, namely NDVI, SAVI and GNDVI, were used to build 18 models respectively with the actual measured soil salinity. These models include linear function, exponential function, logarithmic function, exponentiation function, quadratic function and cubic function, from which the best estimation model for soil salinity estimation was selected and used for inverting and analyzing soil salinity status of the study area. Results indicated that all models mentioned above could effectively estimate soil salinity and models using SAVI as the dependent variable were more effective than the others. Among SAVI models, the linear model (Y = -0.524x + 0.663, n = 70) is the best, under which the test value of F is the highest as 141.347 at significance test level, estimated R2 0.797 with a 93.36% accuracy. Soil salinity of the study area is mainly around 2.5 per thousand - 3.5 per thousand, which gradually increases from southwest to northeast. The study has probed into soil salinity estimation methods based on near-ground and multispectral data, and will provide a quick and effective technical soil salinity estimation approach for coastal saline soil of the study area and the whole Yellow River Delta.

[Land salinization information extraction method based on HSI hyperspectral and TM imagery].

This paper chose the typical salinization area in Kenli County of the Yellow River Delta as the study area, selected HJ-1A satellite HSI image at March 15, 2011 and TM image at March 22, 2011 as source of information, and pre-processed these data by image cropping, geometric correction and atmospheric correction. Spectral characteristics of main land use types including different degree of salinization lands, water and shoals were analyzed to find distinct bands for information extraction Land use information extraction model was built by adopting the quantitative and qualitative rules combining the spectral characteristics and the content of soil salinity. Land salinization information was extracted via image classification using decision tree method. The remote sensing image interpretation accuracy was verified by land salinization degree, which was determined through soil salinity chemical analysis of soil sampling points. In addition, classification accuracy between the hyperspectral and multi-spectral images were analyzed and compared. The results showed that the overall image classification accuracy of HSI was 96.43%, Kappa coefficient was 95.59%; while the overall image classification accuracy of TM was 89.17%, Kappa coefficient was 86.74%. Therefore, compared to multi-spectral TM data, the hyperspectral imagery could be more accurate and efficient for land salinization information extraction. Also, the classification map showed that the soil salinity distinction degree of hyperspectral image was higher than that of multi-spectral image. This study explored the land salinization information extraction techniques from hyperspectral imagery, extracted the spatial distribution and area ratio information of different degree of salinization land, and provided decision-making basis for the scientific utilization and management of coastal salinization land resources in the Yellow River Delta.

[Estimation of chlorophyll content in apple tree canopy based on hyperspectral parameters].

The hyperspectral reflectance of apple tree canopy during spring shoots stopping growth period was measured using ASD FieldSpec3 field spectrometer. Original spectral data were processed in deviation forms, and significant spectrum parameters correlated with chlorophyll content were found out with correlation analysis. The best vegetation indices were chosen and the apple canopy chlorophyll content estimation model was established by analyzing vegetation index of two-band combination in the sensitive region 400-1 350 nm. The result showed that (1) The sensitive band region of apple canopy chlorophyll content is 400-1 350 nm. (2) The vegetation index CCI(D(794)/D(763)) can commendably estimate the apple canopy chlorophyll content. (3) The model with CCI(D(794)/D(763)) as the independent variables was determined to be the best for chlorophyll content prediction of apple tree canopy. Therefore, using hyperspectral technology can estimate apple canopy chlorophyll content more rapidly and accurately, and provides a theoretical basis for rapid apple tree canopy nutrition diagnosis and growth monitoring.

[Hyperspectral estimation of kalium content in apple florescence canopy based on fuzzy recognition].

The objective of the present paper is fast and nondestructive estimate of kalium content using ASD FieldSpec3 spectrometer determined hyperspectral data in apple florescence canopy. According to detection of hyperspectral data of the apple florescence canopy and kalium content data at laboratory in Qixia city of experimental orchards in 2008 and 2009, the correlation analysis of hyperspectral reflectance and its eleven transforms with kalium content was proceeded. The biggest correlation coefficient as independent variable and the estimation model of kalium content were established based on fuzzy recognition algorithms. The model was tested by sample inspection in 2008 and verified by data in 2009. The results showed that the correlation is less for the original spectral reflectance (R) and its reciprocal(1/R), logarithm (lgR), square root (R1/2) and the kalium content, but it is enhanced obviously for their first derivative and second derivative. The correlation coefficient(r) of kalium content estimating model y = 11.344 5h + 1.309 7 is 0.985 1, the total root mean square difference (RMSE) is 0.355 7 and F statistics is 3 085.6. The average relative error of measured values and estimated values for 24 inspection sample is 9.8%, estimation accuracy is 90.2% and verification accuracy is 83.3% utilizing test data in 2009. It was showed that this model is more stable by estimating apple florescence canopy of kalium content and the model precision is able to meet the needs of production.

[Hyper spectral estimation method for soil alkali hydrolysable nitrogen content based on discrete wavelet transform and genetic algorithm in combining with partial least squares DWT-GA-PLS)].

Taking the Qihe County in Shandong Province of East China as the study area, soil samples were collected from the field, and based on the hyperspectral reflectance measurement of the soil samples and the transformation with the first deviation, the spectra were denoised and compressed by discrete wavelet transform (DWT), the variables for the soil alkali hydrolysable nitrogen quantitative estimation models were selected by genetic algorithms (GA), and the estimation models for the soil alkali hydrolysable nitrogen content were built by using partial least squares (PLS) regression. The discrete wavelet transform and genetic algorithm in combining with partial least squares (DWT-GA-PLS) could not only compress the spectrum variables and reduce the model variables, but also improve the quantitative estimation accuracy of soil alkali hydrolysable nitrogen content. Based on the 1-2 levels low frequency coefficients of discrete wavelet transform, and under the condition of large scale decrement of spectrum variables, the calibration models could achieve the higher or the same prediction accuracy as the soil full spectra. The model based on the second level low frequency coefficients had the highest precision, with the model predicting R2 being 0.85, the RMSE being 8.11 mg x kg(-1), and RPD being 2.53, indicating the effectiveness of DWT-GA-PLS method in estimating soil alkali hydrolysable nitrogen content.

[Mapping environmental vulnerability from ETM + data in the Yellow River Mouth Area].

The environmental vulnerability retrieval is important to support continuing data. The spatial distribution of regional environmental vulnerability was got through remote sensing retrieval. In view of soil and vegetation, the environmental vulnerability evaluation index system was built, and the environmental vulnerability of sampling points was calculated by the AHP-fuzzy method, then the correlation between the sampling points environmental vulnerability and ETM + spectral reflectance ratio including some kinds of conversion data was analyzed to determine the sensitive spectral parameters. Based on that, models of correlation analysis, traditional regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the spectral reflectance and the environmental vulnerability. With this model, the environmental vulnerability distribution was retrieved in the Yellow River Mouth Area. The results showed that the correlation between the environmental vulnerability and the spring NDVI, the September NDVI and the spring brightness was better than others, so they were selected as the sensitive spectral parameters. The model precision result showed that in addition to the support vector model, the other model reached the significant level. While all the multi-variable regression was better than all one-variable regression, and the model accuracy of BP neural network was the best. This study will serve as a reliable theoretical reference for the large spatial scale environmental vulnerability estimation based on remote sensing data.

[Satellite remote sensing retrieval of canopy nitrogen nutritional status of apple trees at blossom stage].

Taking Qixia City of Shandong, China as the study area, and based on the Landsat-5 TM and ALOS AVNIR-2 images, the canopy retrieval reflectance of apple trees at blossom stage was acquired. In combining with the measured reflectance of sample trees, the nitrogen-sensitive spectral indices were constructed and selected. By using the sensitive spectral indices as the independent variables, the nitrogen retrieval models were established, and the model with the best accuracy was used for spatial retrieve. The correlations between the spectral indices and the nitrogen nutritional status were in the order of canopy > leaf > flower. The sensitive indices were mainly composed of green, red, and near infrared bands. The accuracy of the retrieval models was in the order of support vector regression > multi-variable stepwise regression > one-variable regression. The retrieval results based on different images were similar, and showed that the leaf nitrogen content was mainly of grades 3-4 (27-33 g x kg(-1)), and the canopy nitrogen nutrient indices were mainly of grades 2-4 (TM: 38-47 g x kg(-1); ALOS: 32-41 g x kg(-1)). The spatial distribution of the retrieval nitrogen nutritional status based on different images also showed the similar trend, i. e., the nitrogen nutritional status was higher in the north and south than that in the middle part of the study area, and the areas with the high grades of leaf nitrogen and canopy nitrogen were mainly located in Sujiadian Town and Songshan subdistrict in the northwest, Zangjiazhuang Town and Tingkou Town in the northeast, and Shewopo Town in the south, which were consistent with the distribution of the key towns for apple production in Qixia City. This study provided a feasible method for the acquisition of nitrogen nutritional status of apple trees on macroscopic scale, and also, provided reference for other similar remote sensing retrievals.

[Quantitative remote sensing retrieval of apple tree canopy reflectance at blossom stage in hilly area].

By using the TM and ALOS images with different resolutions at the prosperous blossom stage of apple trees in Qixia City of Shandong Province, and taking the slope aspect coefficient and the ratio of canopy flower to leaf into account, the ground surface reflectance was retrieved through radiometric correction. The canopy reflectance of the apple trees was further retrieved by pixel unmixing method, and the retrieval effect and accuracy were assessed by the comparison of the retrieved reflectance with the measured canopy reflectance and apparent reflectance of 30 sample apple orchards. The results showed that radiometric correction effectively weakened the effects of atmosphere and topography, recovered the ground objects in the shadows, and obviously enhanced the analytical ability of ground surface retrieval reflectance images. Either TM or ALOS images, both the absolute and relative errors between retrieval reflectance and measured reflectance of apple tree canopy were the smallest. The relative errors of all bands were consistent, and its variation trend among the 30 sample apple orchards was also consistent with the measured reflectance, which showed the necessary of pixel unmixing. Moreover, the changes of the reflectance among the sample apple orchards showed similar characteristics when the retrieval method was used for different resolution images. The images with high resolution were more superior, but, because of band limitation, it would be better to integrate the high resolution images with moderate resolution images.

[Hyperspectral estimation models of chlorophyll content in apple leaves].

The present study chose the apple orchard of Shandong Agricultural University as the study area to explore the method of apple leaf chlorophyll content estimation by hyperspectral analysis technology. Through analyzing the characteristics of apple leaves' hyperspectral curve, transforming the original spectral into first derivative, red edge position and leaf chlorophyll index (LCI) respectively, and making the correlation analysis and regression analysis of these variables with the chlorophyll content to establish the estimation models and test to select the high fitting precision models. Results showed that the fitting precision of the estimation model with variable of LCI and the estimation model with variable of the first derivative in the band of 521 and 523 nm was the highest. The coefficients of determination R2 were 0.845 and 0.839, the root mean square errors RMSE were 2.961 and 2.719, and the relative errors RE% were 4.71% and 4.70%, respectively. Therefore LCI and the first derivative are the important index for apple leaf chlorophyll content estimation. The models have positive significance to guide the production of apple cultivation.

[Spatial distribution of wintering Engraulis japonicus and its relationship with the inter-annual variations of water temperature in central and southern Yellow Sea].

Abstract: This paper studied the inter-annual variations in the spatial distribution of wintering anchovy (Engraulis japonicus) in central and southern Yellow Sea, based on the 1986-2010 bottom trawl survey data and related sea surface temperature (SST) data obtained by remote sensing, and approached the relationships between the inter-annual variations in the spatial distribution of the wintering anchovy and the SST, by using GIS technique, spatial analysis and correlation analysis. In 1986-2010, the wintering anchovy in the study area had apparent inter-annual variations in spatial distribution, with its abundance dropped to the lowest level and its distribution moved shoreward in 2004, and the abundance rebounded and centralized in the eastern waters in 2010. The centralized distribution regions of the anchovy's capture locations and stock density in longitudinal and latitudinal directions also had apparent inter-annual variations. There was a significant correlation between the latitude of the anchovy's stock density center and the mean latitude of the representative isotherms, suggesting that the variations in water temperature had effects on the latitudinal distribution of the wintering anchovy, and whether the anchovy distributed shoreward or not was determined by the inter-annual variations of Yellow Sea Warm Current. Among the factors affecting the anchovy distribution, fishing pressure was the main factor affecting the changes in anchovy abundance, and water temperature mainly determined the changes in anchovy spatial distribution.

[Remote sensing techniques of apple orchard information extraction based on linear spectral unmixing with measured data].

Taking Qixia City, Shandong Province of China as the research region, and by using pixel unmixing for the TM image at apple flowering stage, the apple orchard information was extracted. Based on the measured spectral end-members, wavelet transform was adopted to improve the linear unmixing model. The improved linear spectral unmixing model, measured end-member based linear spectral unmixing model, and TM image end-member based linear spectral unmixing model were employed to extract the apple orchard information, and the ALOS data were used for accuracy estimation. After the accurate atmospheric and topographic correction, it was feasible to use the measured spectral end-members for pixel unmixing, and the area precision of apple orchard information acquisition was greater than 97%. The regression analysis on the NDVI of abundance image and the average NDVI of ALOS data showed that the R2 was higher than 0.8. Therefore, using wavelet transform to improve the linear spectral unmixing model could improve the unmixing accuracy to a certain degree.

[Hyper-spectral estimation of soil organic matter content based on wavelet transformation].

A total of 60 soil samples with approximate contents of N, P, and K and greatly different content of organic matter were selected by statistical analysis. Through hyper-spectral detection and analysis, the first derivative spectrum of the soil logarithmic reflectance was obtained, and was decomposed by the Bior 1.3 wavelet function. The approximative signal of the lowest frequency and the noise signal of the highest frequency were removed from the input spectrum so as to obtain the characteristic spectrum corresponding to soil physical and chemical parameters. The sensitive bands of soil organic matter were selected by correlation analysis, and the forecasting models were built by multiple regression analysis, based on the sensitive bands and the characteristic spectrum, respectively. Through comparison analysis, the optimal wavelet decomposing resolution for extracting the characteristic spectrum of soil organic matter was ascertained, and the best forecasting model was established. The best wavelet decomposing resolution was 9, followed by 8 and 10. Based on the characteristic spectrum of wavelet decomposing of 9 resolutions, the model R2 reached 0.89, which was increased by 0.31 as compared to the model based on sensitive bands, and increased by 0.10 as compared to the model based on the original spectrum.

[Quantitative models between canopy hyperspectrum and its component features at apple tree prosperous fruit stage].

Hyperspectral technique has become the basis of quantitative remote sensing. Hyperspectrum of apple tree canopy at prosperous fruit stage consists of the complex information of fruits, leaves, stocks, soil and reflecting films, which was mostly affected by component features of canopy at this stage. First, the hyperspectrum of 18 sample apple trees with reflecting films was compared with that of 44 trees without reflecting films. It could be seen that the impact of reflecting films on reflectance was obvious, so the sample trees with ground reflecting films should be separated to analyze from those without ground films. Secondly, nine indexes of canopy components were built based on classified digital photos of 44 apple trees without ground films. Thirdly, the correlation between the nine indexes and canopy reflectance including some kinds of conversion data was analyzed. The results showed that the correlation between reflectance and the ratio of fruit to leaf was the best, among which the max coefficient reached 0.815, and the correlation between reflectance and the ratio of leaf was a little better than that between reflectance and the density of fruit. Then models of correlation analysis, linear regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the hyperspectral reflectance and the ratio of fruit to leaf with the softwares of DPS and LIBSVM. It was feasible that all of the four models in 611-680 nm characteristic band are feasible to be used to predict, while the model accuracy of BP neural network and support vector regression was better than one-variable linear regression and multi-variable regression, and the accuracy of support vector regression model was the best. This study will be served as a reliable theoretical reference for the yield estimation of apples based on remote sensing data.

[Standardization of technical methods for apple fluorescence canopy spectral detection].

Aiming at spectral detection of apple fluorescence canopy, the present paper carried out spectral detection tests under different weather conditions, different detection times, and different detection heights and angles to apple canopy in the two years of 2008 and 2009, so as to analyze impacts of these factors on apple canopy spectral characteristics and explore standardized spectral detection methods for apple fluorescence canopy. The results indicated the regularity in spectral reflectance of apple fluorescence canopy to a certain degree under different conditions, especially in the 760-1 350 nm near-infrared bands. The authors found that canopy spectral reflectance declined along with the decrease in sunshine and it is appropriate to detect canopy spectrum in sunny days with few clouds. In addition, spectral reflectance tended to be stable when the wind scale was below grade 2. The discrepancy of canopy spectra is small during the time period from 10:00 to 15:00 of a day compared to that of other times. For maintaining stable spectral curves, the height of detector to apple canopy needed to be adjusted to cover the whole canopy within the field of view according to detection angle of the detector. The vertical or approximately vertical detection was the best for canopy spectral reflectance acquisition. The standardization of technical methods of spectral detection for apple fluorescence canopy was proposed accordingly, which provided theoretical references for spectral detection and information extraction of apple tree canopy.

[Hyperspectrum based prediction model for nitrogen content of apple flowers].

The present paper aims to quantitatively retrieve nitrogen content in apple flowers, so as to provide an important basis for apple informationization management. By using ASD FieldSpec 3 field spectrometer, hyperspectral reflectivity of 120 apple flower samples in full-bloom stage was measured and their nitrogen contents were analyzed. Based on the apple flower original spectrum and first derivative spectral characteristics, correlation analysis was carried out between apple flowers original spectrum and first derivative spectrum reflectivity and nitrogen contents, so as to determine the sensitive bands. Based on characteristic spectral parameters, prediction models were built, optimized and tested. The results indicated that the nitrogen content of apple was very significantly negatively correlated with the original spectral reflectance in the 374-696, 1 340-1 890 and 2 052-2 433 nm, while in 736-913 nm they were very significantly positively correlated; the first derivative spectrum in 637-675 nm was very significantly negatively correlated, and in 676-746 nm was very significantly positively correlated. All the six spectral parameters established were significantly correlated with the nitrogen content of apple flowers. Through further comparison and selection, the prediction models built with original spectral reflectance of 640 and 676 nm were determined as the best for nitrogen content prediction of apple flowers. The test results showed that the coefficients of determination (R2) of the two models were 0.825 8 and 0.893 6, the total root mean square errors (RMSE) were 0.732 and 0.638 6, and the slopes were 0.836 1 and 1.019 2 respectively. Therefore the models produced desired results for nitrogen content prediction of apple flowers with average prediction accuracy of 92.9% and 94.0%. This study will provide theoretical basis and technical support for rapid apple flower nitrogen content prediction and nutrition diagnosis.