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Acid rain is considered one of the most serious plant abiotic stresses. Photosynthesis is the basis of crop growth and development. The effect of acid rain on barley photosynthesis remains unclear. A glasshouse experiment was conducted, and the photosynthetic rate, chlorophyll (Chl) fluorescence, and pigment content of barley were measured in simulated acid rain (SAR) under pH 6.5, 5.5, 4.5, and 3.5. The results showed that net photosynthetic rate, maximal photosynthetic rate, and light saturation point decreased and the light compensation point, and dark respiration rate increased with increasing acidity. The results suggested that photosynthesis in barley plants was inhibited by SAR stress. The Chl content and stomatal conductance declined in parallel with the reduced net photosynthetic rate when barley plants were under SAR stress conditions. This indicated that non-stomatal factors may contribute to reduced photosynthesis under acid rain stress. Acid rain had greater effects on the photosynthesis of the acid rain-sensitive plant Zhepi 33 than on non-sensitive Kunlun 12. A significant difference in parameters such as the maximal fluorescence, variable fluorescence, and active PSII reaction centers was found among the SAR treatments and may be used to evaluate the sensitivity of plants to acid rain stress. The visualization model showed that the photosynthetic reaction centers were inactivated in acid rain stressed barley plants. These findings are valuable for the evaluation of the plant sensitivity to acid rain stress and may be used for the detection and monitoring of acid rain effects on plants in the future.

Agricultural drought differs from meteorological, hydrological, and socioeconomic drought, being closely related to soil water availability in the root zone, specifically for crop and crop growth stage. In previous studies, several soil moisture indices (e.g., the soil moisture index, soil water deficit index) based on soil water availability have been developed for agricultural drought monitoring. However, when developing these indices, it was generally assumed that soil water availability to crops was equal throughout the root zone, and the effects of root distribution and crop growth stage on soil water uptake were ignored. This article aims to incorporate root distribution into a soil moisture-based index and to evaluate the performance of the improved soil moisture index for agricultural drought monitoring. The Huang-Huai-Hai Plain of China was used as the study area. Overall, soil moisture indices were significantly correlated with the crop moisture index (CMI), and the improved root-weighted soil moisture index (RSMI) was more closely related to the CMI than averaged soil moisture indices. The RSMI correctly identified most of the observed drought events and performed well in the detection of drought levels. Furthermore, the RSMI had a better performance than averaged soil moisture indices when compared to crop yield. In conclusion, soil moisture indices could improve agricultural drought monitoring by incorporating root distribution.

The hyperspectral imaging technique was used for the non-destructive detection of tomato spotted wilt virus (TSWV) infection in tobacco at an early stage. Spectra ranging from 400 to 1000 nm with 128 bands from inoculated and healthy tobacco plants were analyzed by using three wavelength selection methods (successive projections algorithm (SPA), boosted regression tree (BRT), and genetic algorithm (GA)), and four machine learning (ML) techniques (boosted regression tree (BRT), support vector machine (SVM), random forest (RF), and classification and regression tress (CART)). The results indicated that the models built by the BRT algorithm using the wavelengths selected by SPA as the input variables obtained the best outcome for the 10-fold cross-validation with the mean overall accuracy of 85.2% and area under receiver operating curve (AUC) of 0.932. The band selection results and variable contribution analysis in BRT modeling jointly showed that the near-infrared (NIR) spectral region is informative and important for the differentiation of infected and healthy tobacco leaves. Different stages of post-inoculation were split according to the molecular identification and visual observation. The classification results at different stages indicated that the hyperspectral imaging data combined with ML methods and wavelength selection algorithms can be used for the early detection of TSWV in tobacco, both at the presymptomatic stage and during the period before the systematic infection can be detected by the molecular identification approach.

Timely and accurate monitoring of the onset and evolution of drought in China are important to reduce losses from drought. The Integrated Surface Drought Index (ISDI) which originally established in mideast China shows a large potential for real-time regional drought monitoring. However, ISDI is still at the developmental stage, and the applicability of the index requires further examination especially for China with vast area, climatic conditions, complex topography, and land cover. Furthermore, ISDI model depends on the historical training data corresponding to the study area. ISDI application in China must be remodeled using the historical training data over China. In this paper, we remodeled ISDI over China based on previous work and evaluated its capability for near real-time drought monitoring. Using the Palmer Drought Severity Index (PDSI) as a dependent variable, ISDI integrates climate-based drought indices, satellite-based Vegetation Index (VI) and land surface temperature (LST) with other biophysical and elevation data to produce a 1-km regional drought condition map at 16-day intervals. Strong relationships were determined between the calculated ISDI and PDSI for spring, summer and autumn, and all of the correlation coefficients exceeded 0.8. The initial ISDI results demonstrated a good performance for monitoring droughts in southwestern China from 2009 to 2010, high temperatures and droughts in southern China in 2013, and floods in northeastern China in 2013. The higher spatial resolution and near real-time capability of ISDI can provide important inputs for drought management and mitigation in China.

Drought is a major natural hazard that can have devastating impacts on regional agriculture, water resources and the environment. To assess the variability and pattern of drought characteristics in the Huang-Huai-Hai (HHH) Plain, the daily Standardized Precipitation Evapotranspiration Index (SPEI) is developed based on daily meteorological data in this study. The daily SPEI data are used, including Annual Total Drought Severity (ATDS), Annual Total Drought Duration (ATDD) and Annual Drought Frequency (ADF), which were calculated from 1981 to 2010 at 28 meteorological stations. We used the indices (ATDS, ATDD and ADF), Hovmoller diagrams and the reliable no parameter statistical methods of the Mann–Kendall test to assess the variability and pattern of drought characteristics for the period from 1981 to 2010 in the HHH plain. The results suggested that severe drought occurred in the 1980s, the late 1990s and the early 2000s, severe drought events occurred in 1981, 1986, 1997 and 2002. Decreasing trends for both ATDS and ATDD were found, and the drought situation did not worsen under global warming during the past 30 years, and the drought situation is alleviating in the entire HHH plain. The northeast and southwest regions of the HHH plain have suffered from more severe drought, and the north region is prone to drought. The results of the study can provide a scientific understanding for the adoption of countermeasures of regional defence against drought and also may serve as a reference point for drought hazard vulnerability analysis.

To identify the world's severely drought-prone areas, given that the corresponding ground area for a 0.5-degree grid in different latitudes is different, we proposed a more precise spherical area-based statistical method. The corresponding ground area per 0.5-degree grid is obtained by integral calculation in latitude and longitude directions. The analysis of the drought based on the global Standardized Precipitation Evapotranspiration Index dataset from 1902 to 2008, where global, Northern Hemisphere, Southern Hemisphere, and major crop-planting regions from six continents are treated as statistical units. The interannual variability characteristics of the severe drought area for each statistical unit are investigated. To study the spatial distribution characteristics of the global frequency of severe drought, the drought frequency was calculated based on drought events identified by continuous drought months on a grid level. Six major crops (wheat, maize, rice, soybean, barley, and sorghum) were chosen to study the impact of drought events on agriculture. The results suggested that severe droughts in global, Northern Hemisphere, and Southern Hemisphere areas have indicated a downward trend since 1990, but an upward trend overall in all continents except Oceania. The identified drought-prone areas show a patchy distribution and frequently drought-prone areas (with 10–20% occurrence probability of drought) were distributed in regions surrounding chronically drought-prone areas (with more than 20% probability). Global chronically drought-prone areas have increased significantly, from 16.19% in 1902–1949 to 41.09% in 1950–2008. Chronically drought-prone areas of agriculture are located in the center of southern Europe, South America, and eastern Asia.

Investigation of spatiotemporal patterns of drought is essential to understand the mechanism and influencing factors of drought occurrence and development. Due to the differences in designation of various drought indices, it remains a great challenge to obtain an accurate result in spatiotemporal patterns investigation of drought. In this study, a quantitative drought monitoring index (i.e., Integrated Surface Drought Index, ISDI) was used to identify spatiotemporal patterns of drought and the drought variation trend at the pixel level during 2001-2013 over China. Eco-geographical regionalization was used as an evaluation unit to distinguish the ecological and climatic background of drought over the whole country. The results showed that the spatial distribution of drought intensity has a strong correlation with eco-geographical regionalization in China. The severe drought areas were mainly concentrated in sub-humid regions and semi-arid regions of medium temperate zones, and humid regions of middle subtropical zones. The regions with higher drought probabilities were most distributed in the south and north of China, while the regions in central and western China exhibited lower drought probabilities. The most obvious decreasing trend of ISDI from 2001 to 2013 was located in the northeast of China and south of the Yangtze River. This decrease in ISDI over time indicates a trend for progressive aggravation of drought severity in these areas. This study shows great promise in informing the future drought prevention measures and management policies under the background of more frequent extreme climate events.

Soil moisture is an effective variable for agricultural drought monitoring, and data assimilation is a useful tool to improve soil moisture estimates. In this study, we assimilated remotely sensed soil moisture (SM) and leaf area index (LAI) into DSSAT-CSM-Wheat crop growth model to estimate soil moisture. The results showed that compared to open-loop scenario, assimilating LAI independently could slightly improve soil moisture accuracy with reduction in average RMSE (root mean square error) by 4%, and the average RMSEs were decreased by 7% and 10% for SM and LAI+SM assimilations, respectively. The yield differences with observation were decreased by 379 kg/ha for LAI assimilation, 592 kg/ha for SM assimilation and 866 kg/ha for LAI+SM assimilation. Assimilating LAI and SM jointly received best performances in soil moisture and yield estimation. Hence, assimilating remotely sensed data into crop growth model provides a robust method to improve soil moisture for agricultural drought monitoring.

The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding why the adoption of counter measures against drought is important and direct farmers to choose drought-resistant crops.

High resolution SAR images contain plenty of detailed textural features, and optical images have spectral features. For the different characteristics of the two images, Firstly, we extracted textural measures of TerraSAR-X image based on the Gray Level Co-occurrence Matrix (GLCM) method, and chose the appropriate window. Then data fusion between textural measures of TerraSAR-X image and QuickBird multi-spectral image was implemented based on PCA transform, and the fusion results were quantitatively evaluated, showing that the fusion image keep spectral information well and the spatial information be enhanced.

Data fusion technique is an efficient way to benefit multi-source, multi-platform, and multi-angle remotely sensed information. Optical imagery and SAR (synthetic aperture radar) data are complementary in terms of capability of data acquisition and image characteristics. With their different capability and their unique information content respectively, fusion of high resolution SAR and optical multi-spectral imagery can improve the classification accuracy in land use. Texture information plays an important role for class discrimination especially in SAR imagery for its backscatter is sensitive to the type, orientation, homogeneity and spatial relationship of ground objects. In order to take full advantage of multi-source remotely sensed data and combine different features of them, this paper put forward a data fusion method for high spatial resolution remotely sensed data based on texture analysis. Texture features of high resolution SAR imagery were extracted using GLCM (Grey Level Co-occurrence Matrix) method. The texture features were detected in 0°, 45°, 90° and 135° four directions, and the moving window size of 3×3, 5×5, to 31×31, 41×41, 51×51, and 61×61 were tested to analyze the influences among them. The selected texture features were added with SAR data to make classification next. Both the two imagery were classified using an object-based and rule-based approach. Then, a decision level fusion was implemented and the accuracy of classification result was improved from 78.7% and 83.0% to 88.8%.

Due to the strengths and weaknesses of the airborne LIDAR data and QuickBird multispectral data, an improved classification method is presented for extracting vegetation information, roads, and buildings. A plot located in San Francisco was selected as the study site. Firstly, ground points were extracted from the LIDAR data and resampled to build DEM and DSM, and then derived nDSM by subtracting DEM from DSM. Secondly, the intensity information derived from LiDAR data was processed to be distributed evenly, and then generated an intensity clustering image, which classified LiDAR points into two basic clusters. Finally, add nDSM and intensity clustering images to QuickBird image as two extra bands, and then we can extract vegetation information, roads, and buildings using their height, intensity and spectral information. The results showed that the method combined airborne LIDAR-derived metrics and QuickBird multispectral data has higher classification accuracy. The proposed method in the paper could be applied to larger research area and other fields.

This paper focuses on object-oriented land cover classification using multi-temporal remotely sensed imagery. We proposed an approach by building rules using multi-temporal HJ-1 CCD imagery and other auxiliary data to classify various land cover types in central Shandong province. We analyzed the seasonal dynamics of vegetation indices (EVI (Enhanced Vegetation index) and NDVI). Vegetation index time series of multi-temporal images can help differentiate forest types. Given the difficulties of vegetation classification, especially in mountainous area, more information available such as DEM, slope, spatial features and priori knowledge were also utilized. The overall accuracy and Kappa coefficient of land cover classification are 80.1% and 0.76, respectively. The results show that besides the spectral information, texture, DEM, slope and auxiliary data are very useful for land cover classification. Multi-temporal information can improve the vegetation classification result significantly and meanwhile has much potential to be explored.