Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta.

Soil salinization is an important factor affecting winter wheat growth in coastal areas. The rapid, accurate and efficient estimation of soil salt content is of great significance for agricultural production. The Kenli area in the Yellow River Delta was taken as the research area. Three machine learning inversion models, namely, BP neural network (BPNN), support vector machine (SVM) and random forest (RF) were constructed using ground-measured data and UAV images, and the optimal model is applied to UAV images to obtain the salinity inversion result, which is used as the true salt value of the Sentinel-2A image to establish BPNN, SVM and RF collaborative inversion models, and apply the optimal model to the study area. The results showed that the RF collaborative inversion model is optimal, R2 = 0.885. The inversion results are verified by using the measured soil salt data in the study area, which is significantly better than the directly satellite remote sensing inversion method. This study integrates the advantages of multi-scale data and proposes an effective "Satellite-UAV-Ground" collaborative inversion method for soil salinity, so as to obtain more accurate soil information, and provide more effective technical support for agricultural production.

Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage.

Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R² = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R² = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.

Spatial variability of soil salinity in coastal saline soil at different scales in the Yellow River Delta, China.

The objectives of this study were to explore the spatial variability of soil salinity in coastal saline soil at macro, meso and micro scales in the Yellow River delta, China. Soil electrical conductivities (ECs) were measured at 0-15, 15-30, 30-45 and 45-60 cm soil depths at 49 sampling sites during November 9 to 11, 2013. Soil salinity was converted from soil ECs based on laboratory analyses. Our results indicated that at the macro scale, soil salinity was high with strong variability in each soil layer, and the content increased and the variability weakened with increasing soil depth. From east to west in the region, the farther away from the sea, the lower the soil salinity was. The degrees of soil salinization in three deeper soil layers are 1.14, 1.24 and 1.40 times higher than that in the surface soil. At the meso scale, the sequence of soil salinity in different topographies, soil texture and vegetation decreased, respectively, as follows: depression >flatland >hillock >batture; sandy loam >light loam >medium loam >heavy loam >clay; bare land >suaeda salsa >reed >cogongrass >cotton >paddy >winter wheat. At the micro scale, soil salinity changed with elevation in natural micro-topography and with anthropogenic activities in cultivated land. As the study area narrowed down to different scales, the spatial variability of soil salinity weakened gradually in cultivated land and salt wasteland except the bare land.

[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.

[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.

[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.

[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.

[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.

Combination of Aspergillus niger MJ1 with Pseudomonas stutzeri DSM4166 or mutant Pseudomonas fluorescens CHA0-nif improved crop quality, soil properties, and microbial communities in barrier soil.

Soil salinization and acidification seriously damage soil health and restricts the sustainable development of planting. Excessive application of chemical fertilizer and other reasons will lead to soil acidification and salinization. This study focus on acid and salinized soil, investigated the effect of phosphate-solubilizing bacteria, Aspergillus niger MJ1 combined with nitrogen-fixing bacteria Pseudomonas stutzeri DSM4166 or mutant Pseudomonas fluorescens CHA0-nif on crop quality, soil physicochemical properties, and microbial communities. A total of 5 treatments were set: regular fertilization (T1), regular fertilization with MJ1 and DSM4166 (T2), regular fertilization with MJ1 and CHA0-nif (T3), 30%-reducing fertilization with MJ1 and DSM4166 (T4), and 30%-reducing fertilization with MJ1 and CHA0-nif (T5). It was found that the soil properties (OM, HN, TN, AP, AK, and SS) and crop quality of cucumber (yield production, protein, and vitamin C) and lettuce (yield production, vitamin C, nitrate, soluble protein, and crude fiber) showed a significant response to the inoculated strains. The combination of MJ1 with DSM4166 or CHA0-nif influenced the diversity and richness of bacterial community in the lettuce-grown soil. The organismal system-, cellular process-, and metabolism-correlated bacteria and saprophytic fungi were enriched, which were speculated to mediate the response to inoculated strains. pH, OM, HN, and TN were identified to be the major factors correlated with the soil microbial community. The inoculation of MJ1 with DSM4166 and CHA0-nif could meet the requirement of lettuce and cucumber growth after reducing fertilization in acid and salinized soil, which provides a novel candidate for the eco-friendly technique to meet the carbon-neutral topic.

[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.

[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.

[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.

[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.

[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.

Hyper-spectral response and estimation model of soil degradation in Kenli County, the Yellow River Delta.

The ecological environment of the Yellow River Delta is fragile, and the soil degradation in the region is serious. Therefore it is important to discern the status of the soil degradation in a timely manner for soil conservation and utilization. The study area of this study was Kenli County in the Yellow River Delta of China. First, physical and chemical data of the soil were obtained by field investigations and soil sample analyses, and the hyper-spectra of air-dried soil samples were obtained via spectrometer. Then, the soil degradation index (SDI) was constructed by the key indicators of soil degradation, including pH, SSC, OM, AN, AP, AK, and soil texture. Next, according to a cluster analysis, soil degradation was divided into the following three grades: light degradation, moderate degradation, and heavy degradation. Moreover, the spectral characteristics of soil degradation were analyzed, and an estimation model of SDI was established by multiple stepwise regression. The results showed that the overall level of reflectance spectra increased with increased degree of soil degradation, that both derivative transformation and waveband reorganization could enhance the spectral information of soil degradation, and that the correlation between SDI and the spectral parameter of (Rλ2+Rλ1)/(Rλ2-Rλ1) was the highest among all the spectral parameters studied. On this basis, the optimum estimation model of SDI was established with the correlation coefficient of 0.811. This study fully embodies the potential of hyper-spectral technology in the study of soil degradation and provides a technical reference for the rapid extraction of information from soil degradation. Additionally, the study area is typical and representative, and thus can indirectly reflect the soil degradation situation of the whole Yellow River Delta.

[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.

[Study on hyperspectral characteristics of apple florescence canopy].

The present study aims to systematically analyze the hyperspectral characteristics of apple florescence canopy and explore the sensitive spectra to provide the theoretical basis for large area apple information extracting and remote sensing retrieval for nutrition diagnosis. Based on the 120 hyperspectral data of apple florescence canopy acquired with ASD Field Spec 3 portable object spectrometer, the effects of different sample numbers on hyperspectral characteristics were analyzed. Using variance analysis method, the hyperspectral characteristics of apple florescence canopy and the sensitive wave bands were obtained. The results showed that with the increase in cumulative sample numbers, the hyperspectrum curves of apple florescence became stable and smooth. At the 550 nm green peak and the 760-1,300 nm reflection plateau, the reflection rate reduced with the increase in flowering amount, while in the red valley of 670 nm, the reflection rate increased with the increase in flowering amount; At the wave bands of 350-500, 600-680 and 760-1,300 nm, the variance analysis results showed very significant differences, indicating that they were sensitive wave bands of florescence canopy. With the increase in flowering amount, the red-edge position, the red-edge slope and red edge area tended to decrease gradually.

Area extraction and spatiotemporal characteristics of winter wheat-summer maize in Shandong Province using NDVI time series.

The use of remote sensing to rapidly and accurately obtain information on the spatiotemporal distribution of large-scale wheat and maize acreage is of great significance for improving the level of food production management and ensuring food security. We constructed a MODIS-NDVI time series dataset, combined linear interpolation and the Harmonic Analysis of Time Series algorithm to smooth the time series data curve, and classified the data with random forest algorithms. The results show that winter wheat-summer maize planting areas were mainly distributed in the western plains, southern region, and north-eastern part of the middle mountainous regions while the eastern hilly regions were less distributed and scattered. The winter wheat-summer maize planting areas in the study area continued to grow from 2004-2016, with the most significant growth in the northern part of the western plains and Yellow River Delta. The spatial planting probability reflected the planting core area and showed an intensive planting pattern. During the study period, the peak value and time for the NDVI of the winter wheat were significantly different and showed an increasing trend, while these parameters for the summer maize were relatively stable with little change. Therefore, we mapped a spatial distribution of the winter wheat and summer maize, using the time series data pre-processing synthesis and phenology curve random forest classification methods. Through precision analysis, we obtained satisfactory results, which provided a straightforward and efficient method to monitor the winter wheat and summer maize.