Meichen Feng

Monitoring winter wheat freeze injury using multi-temporal MODIS data.

Freeze injury is an usual disaster for winter wheat in Shanxi Province, China, and monitoring freeze injury is of important economic significance. The aim of this article is to monitor and analyze the winter wheat freeze injury using remote sensing data, to monitor the occurrence and spatial distribution of winter wheat freeze in time, as well as the severity of the damage. The winter wheat freeze injury was monitored using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data, combined with ground meteorological data and field survey data, the change of normalized difference vegetation index (NDVI) before and after freeze injury was analyzed, as well as the effect of winter wheat growth recovery rate on yield. The results showed that the NDVI of winter wheat decreased dramatically after the suffering from freeze injury, which was the prominent feature for the winter wheat freeze injury monitoring. The degrees of winter wheat freeze injury were different in the three regions, of which, Yuncheng was the worst severity and the largest freeze injury area, the severity of freeze injury correlates with the breeding stage of the winter wheat. The yield of winter wheat showed positive correlation with its growth recovery rate (r = 0.659**) which can be utilized to monitor the severity of winter wheat freeze injury as well as its impact on yield. It can effectively monitor the occurrence and severity of winter wheat freeze injury using horizontal and vertical profile distribution and growth recovery rate, and provide a basis for monitoring the winter wheat freeze injury in Shanxi Province.

Integrating Remote Sensing and GIS for Prediction of Winter Wheat (Triticum aestivum) Protein Contents in Linfen (Shanxi), China

In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R2 (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (RRMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China.

Impact of Water Content and Temperature on the Degradation of Cry1Ac Protein in Leaves and Buds of Bt Cotton in the Soil

Determining the influence of soil environmental factors on degradation of Cry1Ac protein from Bt cotton residues is vital for assessing the ecological risks of this commercialized transgenic crop. In this study, the degradation of Cry1Ac protein in leaves and in buds of Bt cotton in soil was evaluated under different soil water content and temperature settings in the laboratory. An exponential model and a shift-log model were used to fit the degradation dynamics of Cry1Ac protein and estimate the DT50 and DT90 values. The results showed that Cry1Ac protein in the leaves and buds underwent rapid degradation in the early stage (before day 48), followed by a slow decline in the later stage under different soil water content and temperature. Cry1Ac protein degraded the most rapidly in the early stage at 35°C with 70% soil water holding capacity. The DT50 values were 12.29 d and 10.17 d and the DT90 values were 41.06 d and 33.96 d in the leaves and buds, respectively. Our findings indicated that the soil temperature was a major factor influencing the degradation of Cry1Ac protein from Bt cotton residues. Additionally, the relative higher temperature (25°C and 35°C) was found to be more conducive to degradation of Cry1Ac protein in the soil and the greater water content (100%WHC) retarded the process. These findings suggested that under appropriate soil temperature and water content, Cry1Ac protein from Bt cotton residues will not persist and accumulate in soil.

Hyperspectral estimation of soil organic matter based on different spectral preprocessing techniques

ABSTRACT In this study, the potentiality of visible and near-infrared reflectance spectroscopy to estimate soil organic matter was assessed. Six preprocessing methods were implemented to process the original spectra. The partial least-squares regression approach was also applied to construct predictive models and evaluate the optimal spectral preprocessing method. The significant wavelengths of soil organic matter were determined by using the correlation analysis and the partial least-squares regression analysis. The results were: (i) visible and near-infrared reflectance spectroscopy was proved to be an ideal approach in the soil organic matter estimation; (ii) different preprocessed spectra could improve their correlation with soil organic matter; the combination of first-order derivative and Savitzky–Golay smoothing method outperformed other preprocessing methods; (iii) the soil organic matter predictive models based on spectra processed by derivatives and Savitzky–Golay smoothing together presented a satisfactory accuracy, yielding the determination coefficient and root mean square error values of 0.986 and 0.077, respectively, for first-order derivative; and 0.973 and 0.105, respectively, for second-order derivative. The combination of first-order derivative and Savitzky–Golay smoothing was ultimately recommended the preferable preprocessing method; and (iv) the wavelengths of 417, 1853, 1000, and 2412 nm were determined as the significant wavelengths associated with soil organic matter. The study will provide a reference for the site specific management of agricultural inputs by using the visible and near-infrared reflectance spectroscopy technology.

Extraction of Sensitive Bands for Monitoring the Winter Wheat (Triticum aestivum) Growth Status and Yields Based on the Spectral Reflectance.

To extract the sensitive bands for estimating the winter wheat growth status and yields, field experiments were conducted. The crop variables including aboveground biomass (AGB), soil and plant analyzer development (SPAD) value, yield, and canopy spectra were determined. Statistical methods of correlation analysis, partial least squares (PLS), and stepwise multiple linear regression (SMLR) were used to extract sensitive bands and estimate the crop variables with calibration set. The predictive model based on the selected bands was tested with validation set. The results showed that the crop variables were significantly correlated with spectral reflectance. The major spectral regions were selected with the B-coefficient and variable importance on projection (VIP) parameter derived from the PLS analysis. The calibrated SMLR model based on the selected wavelengths demonstrated an excellent performance as the R2, TC, and RMSE were 0.634, 0.055, and 843.392 for yield; 0.671, 0.017, and 1.798 for SPAD; and 0.760, 0.081, and 1.164 for AGB. These models also performed accurately and robustly by using the field validation data set. It indicated that these wavelengths retained in models were important. The determined wavelengths for yield, SPAD, and AGB were 350, 410, 730, 1015, 1185 and 1245 nm; 355, 400, 515, 705, 935, 1090, and 1365 nm; and 470, 570, 895, 1170, 1285, and 1355 nm, respectively. This study illustrated that it was feasible to predict the crop variables by using the multivariate method. The step-by-step procedure to select the significant bands and optimize the prediction model of crop variables may serve as a valuable approach. The findings of this study may provide a theoretical and practical reference for rapidly and accurately monitoring the crop growth status and predicting the yield of winter wheat.

Hyperspectral prediction of leaf area index of winter wheat in irrigated and rainfed fields

The growth status of winter wheat in irrigated field and rainfed field are obviously different and the field types may have an effect on the predictive accuracy of hyperspectral model. The objectives of the present study were to understand the difference of spectral sensitive wavelengths for leaf area index (LAI) in two field types and realize its hyperspectral prediction. In study, a total of 31 and 28 sample sites in irrigated fields and rainfed fields respectively were selected from Wenxi County, and the LAI and canopy spectra were also collected at the main grow stage of winter wheat. The method of successive projections algorithm (SPA) was applied by selecting the important wavelengths, and the multiple linear regression (MLR) and partial least squares regression (PLSR) were used to construct the predictive model based on the important wavelengths and full wavelengths, respectively. Moreover, the parameters of variable importance project (VIP) and B-coefficient derived from PLSR analysis were implemented to validate the evaluated wavelengths using the SPA method. The sensitive wavelengths of LAI for irrigated field and rainfed field were 404, 407, 413, 417, 450, 677, 715, 735, 816, 1127 and 404, 406, 432, 501, 540, 679, 727, 779, 1120, 1290 nm, respectively, and these wavelengths proved to be highly correlated with LAI. Compared with the model performance based on the SPA-MLR and PLSR methods, the method of SPA-MLR was proved to be better (rainfed field: R2 = 0.736, RMSE = 1.169, RPD = 1.6245; irrigated field: R2 = 0.716, RMSE = 1.059, RPD = 1.538). Moreover, the predictive model of LAI in rainfed fields had a better accuracy than the model in irrigated fields. The results from this study indicated that it was necessary to classify the field type while monitoring the winter wheat using the remote sensing technology. This study also demonstrated that the multivariate method of SPA-MLR could accurately evaluate the sensitive wavelengths and construct the predictive model of LAI.

Impact of spectral saturation on leaf area index and aboveground biomass estimation of winter wheat

ABSTRACT The saturation problem associated with the use of normalized difference vegetation index for crop variable estimation is well known. However, its physical mechanism is not systemically explored. The wavebands computing the vegetation indices also suffered saturation when the leaf area index and aboveground biomass reached to 2.5 and 1 kg m−2, respectively. We thought that the saturation might be not only referred to normalized difference vegetation index, but also to certain wavebands. Furthermore, the performances of seven different vegetation indices were assessed on overcoming the saturation. The findings will improve our understanding of the spectral saturation.

Evaluating winter wheat (Triticum aestivum L.) nitrogen status using canopy spectrum reflectance and multiple statistical analysis

ABSTRACT Reasonable adoption of nitrogen fertilizers is significant in increasing wheat production, improving wheat quality, and environmental protection. This research applies the multiple statistical analysis technique to extract sensitive spectral bands and establish a spectrum monitoring model in determining accumulation nitrogen deficit. The major bands of the spectrum monitoring model in different nitrogen models are 440 and 610 nm. The higher value of the coefficient of the determination in estimating the model indicates lower root mean squared error and relative error. Therefore, appropriate nitrogen fertilization can be achieved by observing the winter wheat spectrum before the flowering stage.

Estimation of Water Content in Winter Wheat (Triticum aestivum L.) and Soil Based on Remote Sensing Data–Vegetation Index

In this study, a suitable and near-real-time water status monitoring approach for winter wheat before harvest was developed with remotely sensed satellite data. Seven vegetation indices were extracted as remote-sensing parameters by making full use of the land surface reflection and land surface temperature transmitted by moderate resolution imaging spectroradiometer (MODIS) data. The correlation of each vegetation index and measured values of winter wheat and soil water contents in different crop growth periods was established. The simulation models, combining vegetation index, soil water content (SWC), and plant water content (PWC) in different winter wheat growth periods, were constructed to predict water content by using remote-sensing data. We found that the correlations between the difference vegetation index (DVI) and the perpendicular vegetation index (PVI) in the beginning of the stem elongation period with SWC were highly significant (P < 0.01); the correlation between the global environmental monitoring index (GEMI) in the ear emergency period and SWC was highly significant (P < 0.01). Furthermore, the correlation between the PVI in maturing period and SWC was highly significant (P < 0.01). Data of different coefficients of vegetation indices and PWC in different winter wheat growth periods illustrated that correlation between the DVI in the beginning of stem elongation period and PWC was highly significant (P < 0.01), while the correlation between the PVI in the maturing period and PWC was highly significant. Our results indicated that spatial and temporal vegetation indices were closely related to soil moisture and winter wheat water content in Wenxi County, Shanxi Province (P. R. China). The vegetation index is conceptually and computationally straightforward and may be used in prediction of environmental hydrological status.

Persistence and effect of cry1ac protein in fields

Abstract The persistence of Cry1Ac protein in the soil and its effect on soil microbial communities are a core issue in assessing the ecological risk of transgenic Bacillus thuringiensis (Bt) cotton. In this study a field experiment was conducted on the cultivation of transgenic Bt cotton (Jin 26 and BtJi 668) with the immediate returning of residues to the fields, in order to quantify the Cry1Ac protein content in the fields and investigate its effects on the functional diversity of soil microbial communities. Cry1Ac protein in the residue-soil mixture was gradually degraded in the transgenic Bt cotton fields. After transgenic Bt cotton straw was returned to the fields for 30 d, 63.73% and 58.33% of the initial amounts of Cry1Ac protein were degraded in the Jin 26 and BtJi 668 fields, respectively. Before the crops were sown in the following year (180 d after returning the straw), no Cry1Ac protein was detected in the fields. After returning the cotton straw to the fields for 30 d, the Shannon-Wiener and McIntosh indices of soil microbial communities in the transgenic Bt cotton fields were significantly higher than those in the non-transgenic cotton fields. Meanwhile, the utilization of carbon sources including amino acids, amines, and carbohydrates by the soil microbial communities significantly increased. Both the McIntosh index and the utilization of carbohydrates increased until 180 d. Principal component analysis revealed that amino acids, amides, and carbohydrates were the main carbon sources distinguishing the two principal component factors. These findings indicated that Cry1Ac protein did not accumulate in the fields after transgenic Bt cotton was planted for one year and the residues were immediately returned to the fields; however, the original functional diversity of soil microbial communities was affected continuously.