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Zhao J.,Nanjing University of Information Science and Technology | Zhao J.,CAS Institute of Remote Sensing | Zhang Y.-H.,Nanjing University of Information Science and Technology | Huang W.-J.,CAS Institute of Remote Sensing | And 4 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2014

Aimed to deal with the limitation of canopy geometry to crop LAI inversion accuracy a new LAI inversion method for different geometrical winter wheat was proposed based on hotspot indices with field-measured experimental data. The present paper analyzed bidirectional reflectance characteristics of erective and loose varieties at red (680 nm) and NIR wavelengths(800 nm and 860 nm) and developed modified normalized difference between hotspot and dark-spot (MNDHD) and hotspot and dark-spot ratio index (HDRI) using hotspot and dark-spot index (HDS) and normalized difference between hotspot and dark-spot (NDHD) for reference. Combined indices were proposed in the form of the product between HDS, NDHD, MNDHD, HDRI and three ordinary vegetation indices NDVI, SR and EVI to inverse LAI for erective and loose wheat. The analysis results showed that LAI inversion accuracy of erective wheat Jing411 were 0.9431 and 0.9092 retrieved from the combined indices between NDVI and MNDHD and HDRI at 860 nm which were better than that of HDS and NDHD, the LAI inversion accuracy of loose wheat Zhongyou9507 were 0.9648 and 0.8956 retrieved from the combined indices between SR and HDRI and MNDHD at 800 nm which were also higher than that of HDS and NDHD. It was finally concluded that the combined indices between hotspot-signature indices and ordinary vegetation indices were feasible enough to inverse LAI for different crop geometrical wheat and multi-angle remote sensing data was much more advantageous than perpendicular observation data to extract crop structural parameters. Source

Lu J.-J.,CAS Institute of Remote Sensing | Lu J.-J.,University of Science and Technology of China | Huang W.-J.,CAS Institute of Remote Sensing | Zhang J.-C.,Beijing Agriculture Information Technology Research Center | Jiang J.-B.,University of Science and Technology of China
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2016

Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent and serious winter wheat diseases in the field, which caused heavy yield loss of winter wheat all over the world. It is necessary to quantitatively identify different diseases for spraying specific fungicides. This study examined the potential of quantitative distinction of powdery mildew and yellow rust by using hyperspectral data with continuous wavelet transform at canopy level. Spectral normalization was processing prior to other data analysis, given the differences of the groups in cultivars and soil environment. Then, continuous wavelet features were extracted from normalized spectral bands using continuous wavelet transform. Correlation analysis and independent t-test were used conjunctively to obtain sensitive spectral bands and continuous wavelet features of 350~1 300 nm, and then, principal component analysis was done to eliminate the redundancy of the spectral features. After that, Fisher linear discriminant models of powdery mildew, stripe rust and normal sample were built based on the principal components of SBs, WFs, and the combination of SBs & WFs, respectively. Finally, the methods of leave-one-out and 55 samples which have no share in model building were used to validate the models. The accuracies of classification were analyzed, it was indicated that the overall accuracies with 92.7% and 90.4% of the models based on WFs, were superior to those of SFs with 65.5% and 61.5%; However, the classification accuracies of Fisher 80-55 were higher but no different than leave-one-out cross validation model, which was possibly related to randomness of training samples selection. The overall accuracies with 94.6% and 91.1% of the models based on SBs & WFs were the highest; The producer' accuracies of powdery mildew and healthy samples based on SBs & WFs were improved more than 10% than those of WFs in Fisher 80-55. Focusing on the discriminant accuracy of different disease, yellow rust can be discriminated in the model based on both WFs and SBs & WFs with higher accuracy; the user' accuracy and producer' accuracy were all up to 100%. The results show great potential of continuous wavelet features in discriminating different disease stresses, and provide theoretical basis for crop disease identification in wide range using remote sensing image. © 2016, Peking University Press. All right reserved. Source

Liang D.,Anhui University | Guan Q.,Anhui University | Guan Q.,CAS Institute of Remote Sensing | Huang W.,CAS Institute of Remote Sensing | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

The method of inverting leaf area index (LAI) using a single vegetation index (VI) was influenced by different degrees of saturation and each index could contain in general two bands of information. This paper proposed the method of using support vector machine regression (SVR) for leaf area index inversion, which could use more band information as input parameters in order to improve LAI inversion accuracy. Using the winter wheat's actual spectra measurement and leaf area index data in the period of erecting stage, elongation stage and filling stage, we established a NDVI-LAI and RVI-LAI model with the statistical regression method respectively, and established regression prediction model using NDVI, RVI, as well as blue, green, red and near-infrared four-band data as input parameters with the support vector machine regression (SVR) method, namely the NDVI-SVR, RVI-SVR and NRGB-SVR model. The above five models used the corresponding period environment HJ-CCD data for validation respectively. The results showed that: the RMSE of 0.98, 0.97 with the prediction accuracy value of 59.2%, 59.3% was obtained using the NDVI-LAI and RVI-LAI regression model respectively, and the RMSE of 0.71, 0.83 with the prediction accuracy value of 70.4%, 67.1% was obtained using NDVI-SVR and RVI-SVR regression model respectively. With blue (B), green (G), red (R) and near infrared (NIR) bands as input parameters of support vector machine regression and prediction, the RMSE value is 0.39, the prediction accuracy value is 81.7%. Support vector machine regression (SVR) prediction has a better fitting effect, and can input more band information to improve the leaf area index remote sensing inversion accuracy which is suitable for winter wheat's multiple birth period. Source

Wang T.,CAS Institute of Remote Sensing | Wang T.,Anhui University | Huang W.,CAS Institute of Remote Sensing | Dong B.,Anhui University | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2015

Photosynthetically active radiation (PAR) is an important parameter in agricultural applications. Some researchers showed that many parameters, such as leaf area index (LAI), leaf angle distribution (LAD) and the heterogeneity of vegetation, were concerned with the distribution of PAR. Many models were used to simulate the distribution of PAR effectors, such as the structure of canopy and sun zenith angle. This paper simulated the vertical distribution of PAR in the canopy and analyzed the relationships between PAR and some parameters, such as solar zenith angle, LAD, LAI, maize canopy structure, special for the heterogeneous canopies such as that crop with width and narrow ridges. It took account the effects of structural features of different type canopies. In this study, the distributions of solar radiation at different heights in maize canopy were simulated based on the radiation transfer model. The row structure model was used to simulate the vertical distribution of PAR in canopies. It accounted for direct radiation including radiation paths going through one or more rows. Leaf angle distribution was generated by using this model to measure leaf angle distributions in canopies. Intensive investigation was made on the effect of these canopy architecture on the penetration of total visible radiation into the canopy at various solar zenith angle. The simulation result of PAR in maize canopy was validated with the measured data, and it appeared good accuracy. By simulation with box model, the regularity of vertical distribution of PAR in the canopy showed that: 1) the transmittance of solar incidence was affected by the effective light path; 2) the attenuation of light in the canopy was diminishing exponentially along the light path. It could be reflected by the vertical distribution of light extinction coefficient (K); 3) the solar altitude angle varying from 60° to 45° or 30° solar altitude angle with the RMSE value of 0.07 or 0.08, it could improve PAR estimation accuracy. The vertical distribution of leaf area was affected by the light attenuation through the canopy. In this paper, we proposed the method based on the regularity of vertical distribution of PAR by using Beer-Lambert law for inversion of the vertical distribution of LAI in maize canopy. The relationships between the vertical distribution of the leaf area index and solar zenith angle was analyzed, and the results were validated with the measured data. Results showed that the algorithm of Bonhomme & Chartier was proved to be effective for inversion of the vertical distribution of LAI. There were differences in inversion results with different solar zenith angles. In the upper canopy, the solar altitude angle varying from 30° to 45° solar altitude angle could improve LAI estimation accuracy with the RMSE of 0.18, and from 45 to 30 solar altitude angle with the RMSE of 0.30 in the middle canopy, 30° and 45° with the RMSE value of 0.11 and 0.09 in the under canopy. The result showed that it had a fairly good agreement between calculated and observed data, which proved the validity of the theoretical model. ©, 2014, Chinese Society of Agricultural Engineering. All right reserved. Source

Huang W.J.,CAS Institute of Remote Sensing | Yang Q.Y.,CAS Institute of Remote Sensing | Yang Q.Y.,Anhui University | Peng D.L.,CAS Institute of Remote Sensing | And 3 more authors.
IOP Conference Series: Earth and Environmental Science | Year: 2014

Nitrogen is a key factor for plant photosynthesis, ecosystem productivity and leaf respiration. Under the condition of nitrogen deficiency, the crop shows the nitrogen deficiency symptoms in the bottom leaves, while excessive nitrogen will affect the upper layer leaves first. Thus, timely measurement of vertical distribution of foliage nitrogen content is critical for growth diagnosis, crop management and reducing environmental impact. This study presents a method using bi-directional reflectance difference function (BRDF) data to invert foliage nitrogen vertical distribution. We developed upper-layer nitrogen inversion index (ULNI), middle-layer nitrogen inversion index (MLNI) and bottom-layer nitrogen inversion index (BLNI) to reflect foliage nitrogen inversion at upper layer, middle layer and bottom layer, respectively. Both ULNI and MLNI were made by the value of the ratio of Modified Chlorophyll Absorption Ration Index to the second Modified Triangular Vegetation Index (MCARI/MTVI2) referred to as canopy nitrogen inversion index (CNII) in this study at ±40° and ±50°, and at ±30° and ±40° view angles, respectively. The BLNI was composed by the value of nitrogen reflectance index (NRI) at ±20° and ±30° view angles. These results suggest that it is feasible to measure foliage nitrogen vertical-layer distribution in a large scale by remote sensing. Source

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