Cunjun Li

Comparison and Analysis of Data Assimilation Algorithms for Predicting the Leaf Area Index of Crop Canopies

Data assimilation as an approach for crop Leaf Area Index (LAI) estimation has been rapidly developed in the field of agricultural remote sensing. Many studies have attempted to integrate sequential remotely sensed observations in the dynamical operation of physical models, aiming to improve model performance of LAI estimation by using various data assimilation schemes. In this study, a new data assimilation algorithm is proposed. For this algorithm, the background field of free parameters is constructed according to the ensemble construction scheme of EnKF algorithm, and a cost function is constructed based on the cost function construction strategy in 4DVAR algorithm in order to analyse and update the free parameters. The last updated free parameters are input into the model for LAI estimation until all the observations are assimilated. Additionally, the cost function in data assimilation procedure is optimised using VFSA algorithm. Winter wheat in Beijing in 2002 is selected as the experimental object. The crop growth model CERES-Wheat and the radiative transfer model PROSAIL are coupled in the assimilation process. Sequentially observed NDVI of winter wheat are assimilated into the coupled model using different assimilation algorithms to quantitatively compare and analyse the LAI estimation results. The performance of the new algorithm is better than CERES-Wheat model, EnKF algorithm, and 4DVAR algorithm, with a lesser RMSE by 43.68%, 41.67%, and 28.99%, respectively, and with increased R

Estimating growth height of winter wheat with remote sensing

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Assessing Rice Chlorophyll Content with Vegetation Indices from Hyperspectral Data

by 110.53%, 90.48%, and 33.33%, respectively. Moreover, for the subset LAI

Hyperspectral Discrimination and Response Characteristics of Stressed Rice Leaves Caused by Rice Leaf Folder

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Application and Evaluation of Wavelet-Based Denoising Method in Hyperspectral Imagery Data

3.00, higher estimation precision and greater efficiency of the new algorithm are obtained.

Crop monitoring using a Multiple Cropping Index based on multi-temporal MODIS data

Height is one of important parameters for evaluating winter wheat growth. It can be not only used to indicate growth status of winter wheat, but also play a very important role in wheat growth environmental simulating models. Remote sensing images can reflect vegetation information and variation trend on different spatial scales, and using remote sensing has become a very important means of retrieving crop growth indices such as H(height), F(vegetation coverage fraction), LAI(leaf area index) and so on. In the paper, firstly LAI was estimated with a gradient-expansion algorithm by combining remote sensing images of Landsat5 TM with field data of winter wheat measured in Shunyi&Tongzhou District, Beijing in 2008, and then applied the dimidiate pixel model with NDVI (Normalized Difference Vegetation Index) from landsat5 TM to calculate F(vegetation coverage fraction), lastly taking the ratio of LAI and F as the factor built the model to estimate winter wheat growth height. The result displayed that the determinant coefficient R2 arrived at 0.48 between the field measured and the fit value by the wheat height estimating model, which showed it was feasible to apply the model with multispectral remote sensing images to estimate the wheat height.

Use of Ceres-Wheat Model for Wheat Yield Forecast in Beijing

Leaf chlorophyll content is not only an important biochemical parameter for determinating the capacity of rice photosynthesis, but also a good indicator of crop stress, nutritional state. Due to the reliable, operational and non-destructive advantages, hyperspectral remote sensing plays a significant role for assessing and monitoring chlorophyll content. In the study, a few of typical vegetation indices (VI) with the combination of 670nm and 800nm band reflectance, Normalized Difference Vegetation Index (NDVI), Modified Simple Ratio index (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI), Transformed Chlorophyll Absorption Ratio Index (TCARI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) are modified by using 705nm and 750nm band reflectance so as to reduce the effect of spectral saturation in 660-680nm absorptive band region, and then used to assess the rice chlorophyll content. The result shows that the five mentioned VIs have better correlation with rice chlorophyll content while using 705nm and 750nm. In addition, in the study the Weight optimization combination (WOC) principle is utilized to further assess the capacity of the five modified VIs for estimating rice chlorophyll content, it is proved that OSAVI and MSR display the better performance.

Phenological characteristics of the main vegetation types on the Tibetan Plateau based on vegetation and water indices

Detecting plant health condition plays an important role in controlling disease and insect pest stresses in agricultural crops. In this study, we applied support vector classification machine (SVC) and principal components analysis (PCA) techniques for discriminating and classifying the normal and stressed paddy rice (Oryza sativa L.) leaves caused by rice leaf folder (Cnaphalocrocis medinalis Guen). The hyperspectral reflectance of paddy rice leaves was measured through the full wavelength range from 350 to 2500nm under the laboratory condition. The hyperspectral response characteristic analysis of rice leaves indicated that the stressed leaves presented a higher reflectance in the visible (430~470 nm, 490~610 nm and 610~680 nm) and one shortwave infrared (2080~2350 nm) region, and a lower reflectance in the near infrared (780~890 nm) and the other shortwave infrared (1580~1750 nm) region than the normal leaves. PCA was performed to obtain the principal components (PCs) derived from the raw and first derivative reflectance (FDR) spectra. The nonlinear support vector classification machine (referred to as C-SVC) was employed to differentiate the normal and stressed leaves with the front several PCs as the independent variables of C-SVC model. Classification accuracy was evaluated using overall accuracy (OA) and Kappa coefficient. OA of C-SVC with PCA derived from both the raw and FDR spectra for the testing dataset were 100%, and the corresponding Kappa coefficients were 1. Our results would suggest that it’s capable of discriminating the stressed rice leaves from normal ones using hyperspectral remote sensing data under the laboratory condition.

Study on the Application of Information Technologies on Suitability Evaluation Analysis in Agriculture

The imaging hyper-spectrometer is highly susceptible to the presence of noise and its noise removal is regularly necessary before any derivative analysis. A wavelet-based(WT) method is developed to remove noise of hyperspectral imagery data, and commonly used denoising methods such as Savitzky-Golay method(SG), moving average method(MA), and median filter method(MF) are compared with it. Smoothing index(SI) and comprehensive evaluation indicator(η) are designed to evaluate the performance of the four denoising methods quantitatively. The study is based on hyperspectral data of wheat leaves, collected by Pushbroom Imaging Spectrometer (PIS) and ASD Fieldspec-FR2500 (ASD) in the key growth periods. According to SI andη, the denoising performance of the four methods shows that WT>SG=MA>MF and WT>MA>MF>SG, respectively. The comparison results reveal that WT works much better than the others with the SI value 0.28 and η value 5.74E-05. So the wavelet-based method proposed in this paper is an optimal choice to filter the noise, in terms of balancing the contradiction between the smoothing and feature reservation ability.

MONITORING WINTER WHEAT MATURITY BY HYPERSPECTRAL VEGETATION INDICES

Food shortage and security attracts global attention and at this moment in time, intensive farming has a large impact on agricultural resources. In this respect, multiple cropping is an effective agricultural practice increasing the combined yield of crops and agricultural output. Over-cropping however, is a major cause of cultivated land degradation. The multiple cropping index (MCI) is an important parameter in arable farming systems. It reflects the utilization of water, soil, incoming radiation, as well as other natural resources. Hence, MCI monitoring is an important activity in the resources and food security assessment of agriculture. Therefore, the objective of this paper is to investigate the MCI monitoring method using multi-temporal moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, for the time period of 2001 to 2004 in the study area of Southeastern China. The annual cycle of crop phenology inferred from remote sensing is characterized by four key transition periods: (1) greenup; (2) maturity; (3) senescence and (4) dormancy. The maximum of the NDVI time-series profile for cropland is a proxy for maximum leaf area. Hence, MCI of arable land in Southeastern China from 2001 to 2004 was monitored by the acquisition of peak frequencies in NDVI time series profiles. The results showed that the MCI increases from north to south for every year, 41.18% areas of Southeastern China had the largest MCI in 2004. The MCI from the MODIS-NDVI elicit a significant correlation with statistical MCI, and most of the relative errors were less 10%. All these results indicated that the method used to estimate MCI described in this paper is dependable.