[Show abstract][Hide abstract] ABSTRACT: Coupling remote sensing data with a crop growth model has become an effective tool for estimating grain yields and assessing grain quality. In this study, a data assimilation approach using a particle swarm optimization algorithm was developed to integrate remotely sensed data into the DSSAT-CERES model for estimating the grain yield and protein content of winter wheat. Our results showed that the normalized difference red edge index produced the most accurate selection of spectral indices for estimating canopy N accumulation, with R2 and RMSE values of 0.663 and 34.05 kg ha-1, respectively. A data assimilation method (R2 = 0.729 and RMSE = 32.02 kg ha-1) performed better than the spectral indices method for estimation of canopy N accumulation. Simulation of grain yield by the data assimilation method agreed well with the measured grain yield, with R2 and RMSE values of 0.711 and 0.63 ton ha-1, respectively. Estimating grain protein content by gluten type could improve the estimation accuracy, with R2 and RMSE of 0.519 and 1.53 %, respectively. Our study showed that estimating wheat grain yield, and especially quality, could be successfully accomplished by assimilating remotely sensed data into the DSSAT-CERES model.
European Journal of Agronomy 11/2015; 71. DOI:10.1016/j.eja.2015.08.006 · 2.92 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.
PLoS ONE 11/2014; 9(11). DOI:10.1371/journal.pone.0111642 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A fast and novel analytical method was developed for the determination of trace levels of sulfonylurea herbicides in water and soil samples. Graphene was used as a sorbent for extraction and ultra high performance liquid chromatography with tandem mass spectrometry was used for quantification. Five sulfonylurea herbicides were preconcentrated from water samples using a graphene-loaded packed cartridge, while extraction from soil samples was performed in a single step using graphene-supported matrix solid-phase dispersion. Under the optimized conditions, the calibration plots were linear in the range between 5 and 1000 ng/Lfor water samples, and between 1 and 200 ng/g for soil samples. All correlation coefficients (R) were >0.99. The limits of detection for water and soil samples were 0.28–0.53 ng/Land 0.08–0.26 ng/g, respectively. This method was successfully applied to the analysis of spiked samples of environmental water and soil, with recoveries ranging from 84.2–109.3 and 86.12–103.2%, respectively, all with relative standard deviations of <10%.This article is protected by copyright. All rights reserved
[Show abstract][Hide abstract] ABSTRACT: The method of drilling and blasting is widely used in the construction of an underground cavern group. In the whole process, the slagging occupies 40–60% of overall cycle time. Therefore, it is important to optimize transport machinery configuration in order to reduce the time of slagging, and improve the overall efficiency of construction. In this paper, sensitivity analysis was applied to study the relations among the time of slagging, configuration of truck, and the number and capacity of loader when trackless transport was utilized in the construction of an underground cavern group. The analysis was based on the data of underground cavern group in JINPING II hydropower construction. The analysis results could be used to guide the configuration of mechanical equipment.
[Show abstract][Hide abstract] ABSTRACT: Grain protein content (GPC) is generally not uniform across cropland due to changes in landscape position, nutrient availability, soil chemical, physical properties, cropping history and soil type. It is necessary to determine the winter wheat GPC quality for different croplands in a collecting area in order to optimize the grading process. GPC quality evaluation refers not only the GPC value, but also the GPC uniformity across a cropland. The objective of this study was to develop a method to evaluate the GPC quality for different croplands through remote sensing technique. Three Landsat5 TM images were acquired on March 27, April 28 and May 30, 2008, corresponding to erecting stage, booting stage and grain filling stage of wheat. The wheat GPC was determined after harvest. Then multi linear regression (MLR) analysis with the enter method was calculated using the TM spectral parameters and the measured GPC data. The GPC MLR model was established based on multi-temporal spectral parameters. The accuracy of the model was R-2 > 0.521, RMSE < 0.66%. The GPC mean value and standard deviation value for each cropland was calculated based on the ancillary cropland boundary data and the grain protein monitoring map. Winter wheat filed GPC quality was evaluated by the GPC mean value and GPC uniformity parameter - coefficients of variation (CV). The evaluation result indicated that the super or good level winter wheat croplands mainly lie in Tongzhou, Daxing and Shunyi County, while the middle or low GPC level croplands are mainly distributed on the Fangshang county. This study indicates that the remote sensing technique provides valuable opportunities to monitor and evaluate grain protein quality.
[Show abstract][Hide abstract] ABSTRACT: Estimation of canopy biophysical and biochemical parameters using remote sensing data is important for
regional crop-growth condition monitoring and yield assessment. The inversion of the radiative transfer model PROSAIL based on the look-up table (LUT) approach is widely used for this purpose, taking remotely sensed reflectance as input. For the LUT-based parameter mapping, the main part is searching for the optimal solution from a large LUT. Due to the computational complexity of the sorting algorithm and size of the LUT for the solution search, a substantial amount of time is normally needed for estimation. In order to speed up the mapping of parameters using remote sensing observations, a faster method is developed for searching the LUT by introducing a binary search algorithm. The results of the experiments based on
SPOT-5 imagery show that the proposed method can increase the mapping speed by about 70 times compared to the sorting algorithm.
Proceedings of the International Geoscience and Remote Sensing Symposium, Remote Sensing for a Dynamic Earth (IGARSS); 07/2014
[Show abstract][Hide abstract] ABSTRACT: A fast and reliable ultra-high performance liquid chromatography–tandem mass spectrometry method was developed for the determination of aflatoxins B1, B2, G1, and G2 in cereal. The analytes were extracted by accelerated solvent extraction with methanol/water (80:20). A polymeric solid-phase extraction column was used for sample preparation. Under optimum conditions, the analyte recoveries for samples spiked at different concentration levels in rice and maize ranged from 71.2 to 94.0%, with relative standard deviations less than 16.4%. Limits of detection (signal-to-noise ratio, 3:1) for the aflatoxins ranged from 0.25 to 0.93 ng/g. The developed method was applied to the determination of aflatoxins in ten rice and maize samples. One maize sample tested positive with an aflatoxin B1 concentration of 2.7 ng/g.
[Show abstract][Hide abstract] ABSTRACT: Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km(2) typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods-artificial neural network, mahalanobis distance, and maximum likelihood classifier-were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat.
[Show abstract][Hide abstract] ABSTRACT: Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale.
PLoS ONE 04/2014; 9(4):e93107. DOI:10.1371/journal.pone.0093107 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Estimating leaf area index (LAI) of crop canopies with regional optical remote sensing observations is the normal way for crop growth monitoring and yield estimation in the research field of agriculture quantitative remote sensing. One fundamental difficulty for multi-scale crop LAI estimation is the spatial scale effects of LAI, due to the spatial heterogeneity in crop canopies and the non-linearity of LAI estimation model. In this study, we proposed a new data upscaling scheme to estimate crop LAI at different spatial scales based on statistical distribution theory, aiming to reduce the influences of scale effects and enhance the LAI estimation precision. Numerical experiments based on the observations in farm underlying surface and suburban underlying surface were conducted to compare and analyze the effectiveness and applicability of the normal upscaling scheme and the new proposed scheme. Experimental results indicated that the estimated multi-scale LAI based on the new proposed data upscaling scheme were significantly more reduced in scale effects and more enhanced in estimation precision under non-uniform underlying surface.
[Show abstract][Hide abstract] ABSTRACT: Aiming to reduce the noise of hyperspectral data, the Rudin-Osher-Fatemi (ROF) model is selected to quantitatively describe the statistical distribution characteristics of noise data, and the Chambolle's algorithm is chosen for ROF model numerical solving to achieve denoised hyperspectral
data. The key point of using ROF model for data denoising is setting its filtering parameters. In this study, we proposed an automatic parameters selection method for ROF model. Winter wheat was taken as the experimental object, and the performance of ROF model in noise reduction was verified
on two datasets, i.e., hyperspectral canopy reflectance dataset simulated with PROSAIL (PROSPECT (Leaf Optical Property Model) + SAIL (Scattering by Arbitrarily Inclined Leaves)) model, and ground hyperspectral canopy reflectance dataset measured in field experiments. Numerical results demonstrated
the effectiveness of the ROF model in reducing noise of hyperspectral data, as well as keeping the important features of reflectance spectra. Also, the results indicated that the denoised hyperspectral data based on the ROF model were in better quality, compared to the denoised hyperspectral
data based on moving average filtering algorithm, Savitzky-Golay filtering algorithm, and wavelet denoising algorithm.
[Show abstract][Hide abstract] ABSTRACT: Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher's linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level.
Field Crops Research 02/2014; 156:199–207. DOI:10.1016/j.fcr.2013.11.012 · 2.61 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Crop aboveground biomass estimates are critical for assessing crop growth and predicting yield. In order to ascertain the optimal methods for winter wheat biomass estimation, this study compared the utility of univariate techniques involving narrow band vegetation indices and red-edge position (REP), as well as multivariate calibration techniques involving the partial least square regression (PLSR) analyses using band depth parameters, and the combination of band depth parameters and hyperspectral indices including narrow band indices and REP. Narrow band indices were calculated in the form of normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) using all possible two-band combinations for selecting optimal narrow band indices. Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area extracted from a red absorption region (550 nm–750 nm) were utilized as band depth parameters. The results indicated that: (1) Compared with the traditional NDVI and SAVI constructed with bands at 670 nm and 800 nm and REP, the selected narrow band indices (optimal NDVI-like and optimal SAVI-like) produced higher estimation accuracy of the winter wheat biomass; (2) the PLSR models based on band depth parameters produced lower root mean square error, relative to the models based on the selected narrow band indices; and (3) the PLSR model based on the combination of optimal NDVI-like and BDR produced the best estimated result of the winter wheat biomass (R2 = 0.84, RMSE = 0.177 kg/m2). The results of this study suggest that PLSR analysis using the combination of optimal NDVI-like and band depth parameters could significantly improve estimation accuracy of winter wheat biomass.
Computers and Electronics in Agriculture 01/2014; 100:51–59. DOI:10.1016/j.compag.2013.10.010 · 1.49 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Detection of yellow rust is of great importance in disease control and reducing the use of fungicide. Spectral analysis is an important method for disease detection in terms of remote sensing. In this study, an emerging spectral analysis method known as continuous wavelet analysis (CWA) was examined and compared with several conventional spectral features for the detection of yellow rust disease at a leaf level. The leaf spectral measurements were made by a spectroradiometer at both Zodaks 37 and 70 stages with a large sample size. The results showed that the wavelet features were able to capture the major spectral signatures of yellow rust, and exhibited considerable potential for disease detection at both growth stages. Both the accuracies of the univariate and multivariate models suggested that wavelet features outperformed conventional spectral features in quantifying disease severity at leaf level. Optimal accuracies returned a coefficient of determination (R2) of 0.81 and a root mean square error (RMSE) of 0.110 for pooled data at both stages. Furthermore, wavelet features showed a stronger response to the yellow rust at Zodaks 70 stage than at Zodaks 37 stage, indicating reliable estimation of disease severity can be made until the Zodaks 70 stage.
Computers and Electronics in Agriculture 01/2014; 100:79–87. DOI:10.1016/j.compag.2013.11.001 · 1.49 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Remote sensing has great potential to serve as a useful means in crop disease detection at regional scale. With the emerging of remote sensing data on various spectral settings, it is important to choose appropriate data for disease mapping and detection based on the characteristics of the disease. The present study takes yellow rust in winter wheat as an example. Based on canopy hyperspectral measurements, the simulative multi-spectral data was calculated by spectral response function of ten satellite sensors that were selected on purpose. An independent t-test analysis was conducted to access the disease sensitivity for different bands and sensors. The results showed that the sensitivity to yellow rust varied among different sensors, with green, red and near infrared bands been identified as disease sensitive bands. Moreover, to further assess the potential for onboard data in disease detection, we compared the performance of most suitable multi-spectral vegetation index (MVI)-GNDVI and NDVI based on Quickbird band settings with a classic hyperspectral vegetation index (HVI) and PRI (photochemical reflectance index). The validation results of the linear regression models suggested that although the MVI based model produced lower accuracy (R 2 = 0.68 of GNDVI, and R 2 = 0.66 of NDVI) than the HVI based model (R 2 = 0.79 of PRI), it could still achieve acceptable accuracy in disease detecting. Therefore, the probability to use multi-spectral satellite data for yellow rust monitoring is illustrated in this study.
[Show abstract][Hide abstract] ABSTRACT: In recent years, the fake Xihu Longjing Tea has damaged its brand image and reputation. This paper based on the Fisher's discriminant analysis using the fixed-size moving window evolving factor analysis to find characteristic spectra through analyzing the near infrared spectroscopy of Xihu Longjing Tea and Zhejiang Longjing Tea. The Fisher's discriminant analysis was used to reduce the data dimension combined with the principal component analysis. A discrimination model was set up for the identification of the Xihu Longjing Tea and Zhejiang Longjing Tea. The model's accuracy is 97.3%. The results proved that this model is feasible to identify the differences between the Xihu Longjing Tea and Zhejiang Longjing Tea. Unlike other methods, the tea does not need to be made into a powder. It also lays out a theoretical foundation for developing an identification instrument for Xihu Longjing Tea.
[Show abstract][Hide abstract] ABSTRACT: Powdery mildew is one of the most serious diseases, which has a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study examines the potential of a moderate resolution multispectral satellite image in disease monitoring at regional scale. At the suburban area around Beijing, a large size ground survey sample (n=90) and the corresponding HJ-CCD image were acquired at the grain filling stage of winter wheat. A number of spectral features were found to be sensitive to powdery mildew through an independent t-test. Based on these spectral features, classification models were established using both spectral information divergence (SID) and spectral angle mapper (SAM), respectively. The results showed that the overall accuracies of disease identification and severity estimation were moderate. The estimation of normal and seriously infected samples yielded higher accuracies than slightly infected samples. The single phase HJ-CCD can only be used for locating the infected areas of powdery mildew, whereas is unable to discriminate the severity levels of disease. The presence of several stressors and disturbances other than disease is a possible reason of the unsatisfactory performance of disease monitoring models. Therefore, the integration of multi-phase onboard data and some relevant ancillary data is necessary to improve the accuracy and reliability of disease monitoring at regional scale.
Optik - International Journal for Light and Electron Optics 11/2013; 124(21):4734-4738. DOI:10.1016/j.ijleo.2013.01.103 · 0.77 Impact Factor