Beijing Agriculture Information Technology Research Center

Beijing, China

Beijing Agriculture Information Technology Research Center

Beijing, China

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Liu R.,Beijing Normal University | Liu R.,Beijing Agriculture Information Technology Research Center | Huang W.,Beijing Agriculture Information Technology Research Center | Ren H.,Beijing Normal University | And 3 more authors.
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2011

Based on the theory of radiation transfer model, this paper modified the Simultaneous Heat and Water model to calculate FPAR vertical distribution in maize canopy and analyzed the relationships between FPAR and some parameters like maize canopy structure, solar zenith, soil reflectance, etc. The validation results using field measurements prove the model to be accurate. © 2011 IEEE.


Gu X.,Beijing Agriculture Information Technology Research Center | Han L.,Tottori University | Wang J.,Beijing Agriculture Information Technology Research Center | Huang W.,Beijing Agriculture Information Technology Research Center | He X.,Beijing Agriculture Information Technology Research Center
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2012

The integration of mid-coarse-resolution remote sensing images provides abundant information, and therefore tends to be a popular way in large scale crop planting area estimation. This research utilized conventional MODIS and TM records to present an instance in large area maize planting area estimation. The wavelet fusion was adopted for obtaining normalized difference vegetation index (NDVI) with a spatial resolution of 30 m from both MODIS and TM images. And the standard growing curves of main fall crops were then constructed with the NDVI time series, which indicating crops difference in phenology. Minimum distance classification was carried out with the NDVI time series for mapping maze sown area in a typical maize-planting county, Yuanyan, Henan province. The result was validated with the in-situ parcels, which showing a better gross and position accuracies (89% and 90%) than those with either MODIS or TM records. The research can provide an efficient way with abundance information from both mid and coarse resolution records, and thus improve the applicability of remote sensing in large area crop area estimation.


Gu X.,Beijing Agriculture Information Technology Research Center | He X.,Beijing Agriculture Information Technology Research Center | Guo W.,Beijing Agriculture Information Technology Research Center | Dong Y.,Beijing Agriculture Information Technology Research Center
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011

The fusion method for the wide range resolution images will contribute to take the advantage of high time-resolution of MODIS data and high spatial-resolution of TM data, which will provide the time-series information matching the crop growth. The paper test the wavelet transform model from wavelet basis, decomposition level and fusion rule. By evaluating the quality of fusion images from several indexes, the paper analyzed the impact of fusion quality of MODIS and TM images from the parameter setting of wavelet transform. According to the comparison of many experiments, the study chose decomposition level 4, BIOR 6.8 of wavelet basis and high-replace-low of fusion rule. The study showed that the fusion method of wavelet transform could reserve the spectral feature of time-series information and enhance the spatial resolution from 250 meter to 30 meter. The time-series fusing images could be applied for crop monitoring. © 2011 SPIE.


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.


Gu X.,Beijing Agriculture Information Technology Research Center | He X.,Beijing Agriculture Information Technology Research Center | Huang W.,Beijing Agriculture Information Technology Research Center
Advanced Science Letters | Year: 2012

The inconsistency on spatial resolution and estimating accuracy of remote sensing images are the primary limiting factor for the estimation of the large-scale planting area for maize. The main purpose of this paper is to develop a method of integrating mid- and low-resolution images for the estimation of large-scale crop planting areas. A mid-scale time-series normalized difference vegetation index (NDVI) dataset, which was derived from the fusion of the moderate-resolution imaging spectroradiometer (MODIS) and TM images based on the wavelet transform, was established. The maize planting area was estimated by using the minimum distance model and the accuracy was evaluated by contrasting the method of supervised classification with in-situ samples. The results show that the estimation of the maize planting area based on the time-series NDVI information of the fusing images reached high levels of gross and position accuracy, which were better than that of supervised classification. It indicated that this method could fully utilize the time-series information from the MODIS images and the spatial resolution of a TM image. The difference in phenophases among fall crops enables the effective classification of the spatial distribution of these crops. The fusion of time-series MODIS and TM images fusing information analysis will provide an available method for estimating maize planting area of large-scale. © 2012 American Scientific Publishers. All rights reserved.


Tang J.-M.,Chongqing University | Liao Q.-H.,Chongqing University | Liu Y.-Q.,Chongqing University | Yang G.-J.,Beijing Agriculture Information Technology Research Center | And 2 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2015

The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R2 and RMSE of estimation model were 0.52, 0.45 (p<0.01), respectively. For red-edge vegetation index, CIred edge took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI. NDSI(569.00, 654.80) and RSI(597.60, 654.80) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R2 of estimation model used NDSI(569.00, 654.80) was 0.77(p<0.000 1), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI(569.00, 654.80) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy. ©, 2015, Science Press. All right reserved.


Liao Q.,Zhejiang University | Liao Q.,Beijing Agriculture Information Technology Research Center | Gu X.,Beijing Agriculture Information Technology Research Center | Li C.,Beijing Agriculture Information Technology Research Center | And 6 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2012

Hyperspectral remote sensing technology had been widely used in the estimation of soil organic matter due to its non-destructive, rapid, and high spectral resolution characteristics. The 64 fluvo-aquic soil organic matter (SOM) obtained from Beijing Shunyi district had been estimated successfully by using the hyperspectral reflectance based on continuous wavelet transform (CWT), the results had also been compared with four common spectral transformation methods, it showed that the fluvo-aquic soil had the same spectral curves with other types soils, and the absorption peaks appeared in the visible and near infrared bands after the spectral curves removed by hull curve. The sensitive bands for estimating the SOM were 1194nm, 486nm, and 866nm, and the corresponding wavelet decomposition scales were 2, 3, and 4. The R2of multiple linear regression model built between the wavelet energy coefficients and SOM was 0.67 by using the CWT, the R2and RMSE between the measured value and predicted value were 0.75 and 0.21, respectively, while the highest R2 of estimation model built by the four common spectral transformation methods was 0.09, which showed that the CWT is more suitable for estimating the fluvo-aquic soil organic matter content. Through the interpolation analysis of Kringing, the more sampling points should be increased in the southeast of Shunyi district for improving the precision of models.


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.


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.


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.

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