Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.

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.

Wheat is the third most producing crop in China after maize and rice. In order to enhance the nitrogen use efficiency (NUE) and grain yield of winter wheat, a two-year field experiment was conducted to investigate the effect of different nitrogen ratios and doses at various development stages of winter wheat (Triticum aestivum L.). A total of five N doses (0, N75, N150, N225, and N300 kg ha⁻¹) as main plots and two N ratios were applied in split doses (50%:50% and 60%:40%, referring to 50% at sowing time and 50% at jointing stage, 50% at sowing time + 50% at flowering stage, 50% at sowing time + 50% at grain filling stage, and 60% + 40% N ratio applied as a 60% at sowing time and 40% at jointing stage, 60% at sowing time and 40% at flowering stage, and 60% at sowing time and 40% at grain filling stage in subplots). The results of this study revealed that a nitrogen dose of 225 kg ha⁻¹ significantly augmented the plant height by 27% and above ground biomass (ABG) by 24% at the grain filling stage, and the leaf area was enhanced by 149% at the flowering stage under 60 + 40% ratios. Furthermore, the N225 kg ha⁻¹ significantly prompted the photosynthetic rate by 47% at the jointing and flowering stages followed by grain filling stage compared to the control. The correlation analysis exhibited the positive relationship between nitrogen uptake and nitrogen content, chlorophyll, and dry biomass, revealing that NUE enhanced and ultimately increased the winter wheat yield. In conclusion, our results depicted that optimizing the nitrogen dose (N225 kg/ha⁻¹) with a 60% + 40% ratio at jointing stage increased the grain yield and nitrogen utilization rate.

Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R² = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.

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.