Wenjiang Huang

Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust (Biotroph Puccinia striiformis) in wheat (Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season (r2xa0=xa00.91, nxa0=xa064), which was insensitive to both variety and stage of crop development from Zadoks stage 3–9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r2xa0=xa00.97 (nxa0=xa080). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r2xa0=xa00.91 (nxa0=xa0120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat.

Identifying Crop Leaf Angle Distribution Based on Two-Temporal and Bidirectional Canopy Reflectance

The effect of crop leaf angle on the canopy-reflected spectrum cannot be ignored in the inversion of leaf area index (LAI) and the monitoring of the crop-growth condition using remote-sensing technology. In this paper, experiments on winter wheat (Triticum aestivum L.) were conducted to identify the crop leaf angle distribution (LAD) by two-temporal (erecting and elongation stages) and bidirectional in situ reflected spectrum and the Airborne Multiangle Thermal Infrared (TIR) Visible Near-Infrared (VNIR) Imaging System (AMTIS) images. The distribution characters of the leaf angle for different LAD varieties were expressed using the beta-distribution function and the SAILTH radiative transfer models. The proportion of the leaf angle in 5deg angle classes (from 5deg to 90deg) for erectophile, planophile, and horizontal varieties was dominated by 75deg, 55deg, and 35deg. The different LAD varieties had a similar canopy reflectance in 680 nm (red) and 800 nm (near-infrared band) at the erecting stage, while they had significant differences at the elongation stage. The ratio of the canopy reflectance of 800 nm at the erecting stage [R800(B)] to the canopy reflectance of 800 nm at the elongation stage [R800(A)] was used to identify the different LAD varieties through the selected two-temporal canopy reflectance. A method based on the semiempirical model of the bidirectional reflectance distribution function (BRDF) was also introduced in this paper. The structural parameter-sensitive index (SPEI) was used in this paper for crop LAD identification. SPEI is proved to be more sensitive to identify erectophile, planophile, and horizontal LAD varieties than the structural scattering index and the normalized difference f-index. We found that it is feasible to identify horizontal, planophile, and erectophile LAD varieties of wheat by studying two-temporal and bidirectional canopy-reflected spectrum

An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery

Two key weaknesses of STDFA including sensor difference and spatial variability were adjusted.Three wildly used spatial and temporal fusion methods were compared.The correlation coefficient r had a negative exponential relationship with ratio of land cover change pixels.The accuracy of ISTDFA method had a logarithmic relationship with the size of applied area. Because of low temporal resolution and cloud influence, many remote-sensing applications lack high spatial resolution remote-sensing data. To address this problem, this study introduced an improved spatial and temporal data fusion approach (ISTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the weaknesses of the spatial and temporal data fusion approach (STDFA) method, including the sensor difference and spatial variability. A weighted linear mixed model was used to adjust the spatial variability of surface reflectance. A linear-regression method was used to remove the influence of differences in sensor systems. This method was tested and validated in three study areas located in Xinjiang and Anhui province, China. The other two methods, the STDFA and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), were also applied and compared in those three study areas. The results showed that the ISTDFA algorithm can generate daily synthetic Landsat imagery accurately, with correlation coefficient r equal to 0.9857 and root mean square error (RMSE) equal to 0.0195, which is superior to the STDFA method. The ISTDFA method had higher accuracy than ESTARFM in areas greater than 200?× 200 MODIS pixels while the ESTARFM method had higher accuracy than the ISTDFA method in small areas. The correlation coefficient r had a negative power relation with ratio of land-cover change pixels. A land-cover change of 20.25% pixels can lead to a reduced correlation coefficient r of 0.295 in the blue band. The accuracy of the ISTDFA method indicated a logarithmic relationship with the size of the applied area, so it is recommended for use in large-scale areas.

Monitoring Powdery Mildew of Winter Wheat by Using Moderate Resolution Multi-Temporal Satellite Imagery

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.

Critical Nitrogen Curve and Remote Detection of Nitrogen Nutrition Index for Corn in the Northwestern Plain of Shandong Province, China

The nitrogen nutrition index (NNI) is calculated from the measured N concentration and the critical nitrogen (N) curve. It can be used to determine the N required by a crop and is helpful for optimizing N application in the field. Our objectives were to validate the existing corn critical N curve for the northwestern plain of Shandong Province and to design a more accurate remote detection method for the NNI. For this purpose, field measurements were conducted weekly to acquire the biomass and N concentrations during the corn growing season of 2011. Additionally, nearly 60 corn canopy spectra were collected during field campaigns. First, limiting and non-limiting N points were selected from sampled data, and they were used to validate the existing critical N curve. Second, an NNI estimation model based on a Principal Component Analysis method and Back Propagation Artificial Neural Network (PCA-BP-ANN) model was established. The collected canopy spectra and corresponding NNI were used to compare the performances of the above mentioned method and other for NNI estimation. The results showed that the N curve proposed in the literature is suitable for the study region. Among the three remote detection methods, PCA-BP-ANN provided the best results with highest R value and lowest root mean square error value.

High-resolution Leaf Area Index estimation from synthetic Landsat data generated by a spatial and temporal data fusion model

Leaf area index (LAI) is an important input parameter for biogeochemical and ecosystem process models. Mapping LAI using remotely sensed data has been a major objective in remote sensing research to date. However, the current LAI product mapped by remote sensing is both spatially and temporally discontinuous as a result of cloud cover, seasonal snows, and instrumental constraints. This has limited the application of LAI to ground surface process simulations, climatic modeling, and global change research. To fill these gaps in LAI products, this study develops an algorithm to provide high spatial and temporal resolution LAI products with synthetic Landsat data, generated by a spatial and temporal data fusion model (STDFA). The model has been developed and validated within the Changping District of Beijing, China. Using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data and real Landsat data, this method can generate LAI data whose spatial (temporal) resolution is the same as that of the Landsat (MODIS) data. Linear regression analysis was performed to compare the modeled data with field-measured LAI data, and indicates that this new method can provide accurate estimates of LAI, with R-2 equal to 0.977 and root mean square error (RMSE) equal to 0.1585 m(2) m(-2) (P < 0.005), which is superior to the standard MODIS LAI product. Further, various STDFA model application strategies were tested, with the results showing that the application strategy of the STDFA model has an important influence on the accuracy of LAI estimation: the vegetation index fusion strategy produced a better result than the reflectance fusion strategy. The applications of the STDFA model to eight commonly used vegetation indices were also compared. The results show that some vegetation indices (e.g., Enhanced Vegetation Index (EVI), Normalized difference vegetation index (NDVI), and Normalized difference infrared index (NDII)) exhibited better performance than others (e.g., Infrared simple ratio (ISR), Reduced infrared simple ratio (RISR), Reduced normalized difference vegetation Index (RNDVI), Reduced simple ratio (RSR), and Simple ratio (SR)). However, ISR, RISR, and NDII data produced lower saturation effects than other spectral vegetation indices in the estimation of LAI values higher than 2 m(2) m(-2)

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat

Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405-835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI). To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient (R2) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e.g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVI-like in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results.

Integrating Remotely Sensed and Meteorological Observations to Forecast Wheat Powdery Mildew at a Regional Scale

The prevalence of powdery mildew (PM) in winter wheat field has a severe impact on crop production. An effective and timely forecast of the disease at a regional scale is necessary to control and prevent it. In this study, both meteorological and remotely sensed observations associated with crop characteristics and habitat traits were integrated for modeling the PM occurrence probability. With an effective feature selection procedure, four meteorological factors, including precipitation, temperature, sun radiation, humidity, and two remotely sensed features including reflectance of red band (RR) demonstrate that the disease risk maps were able to depict the approximately spatial distribution of PM and its temporal dynamic in the study area. Compared with the model constructed with meteorological data only, the integrated model constructed with both remote sensing and meteorological data has produced a higher accuracy (increasing overall accuracy from 69% to 78%) of forecasting the PM occurrence. This suggests that there would be a great potential for predicting the PM occurrence probability by integrating both meteorological and remote sensing data at a regional scale. In the future, multiple forms of information (e.g., Web sensors networks data) are expected to be incorporated in the disease-forecasting model to further improve its performance for forecasting the disease occurrence (e.g., PM) at a regional scale.