Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery.

Rapid and accurate prediction of crop yield is particularly important for ensuring national and regional food security and guiding the formulation of agricultural and rural development plans. Due to unmanned aerial vehicles' ultra-high spatial resolution, low cost, and flexibility, they are widely used in field-scale crop yield prediction. Most current studies used the spectral features of crops, especially vegetation or color indices, to predict crop yield. Agronomic trait parameters have gradually attracted the attention of researchers for use in the yield prediction in recent years. In this study, the advantages of multispectral and RGB images were comprehensively used and combined with crop spectral features and agronomic trait parameters (i.e., canopy height, coverage, and volume) to predict the crop yield, and the effects of agronomic trait parameters on yield prediction were investigated. The results showed that compared with the yield prediction using spectral features, the addition of agronomic trait parameters effectively improved the yield prediction accuracy. The best feature combination was the canopy height (CH), fractional vegetation cover (FVC), normalized difference red-edge index (NDVI_RE), and enhanced vegetation index (EVI). The yield prediction error was 8.34%, with an R2 of 0.95. The prediction accuracies were notably greater in the stages of jointing, booting, heading, and early grain-filling compared to later stages of growth, with the heading stage displaying the highest accuracy in yield prediction. The prediction results based on the features of multiple growth stages were better than those based on a single stage. The yield prediction across different cultivars was weaker than that of the same cultivar. Nevertheless, the combination of agronomic trait parameters and spectral indices improved the prediction among cultivars to some extent.

Photosynthetic rate and chlorophyll fluorescence of barley exposed to simulated acid rain.

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.

Evaluating the utility of solar-induced chlorophyll fluorescence for drought monitoring by comparison with NDVI derived from wheat canopy.

Normalized Difference Vegetation Index (NDVI) has been extensively used in continuous and long-term drought monitoring over large-scale, but with late response to drought-related changes of photosynthesis. Instead, solar-induced chlorophyll fluorescence (SIF) is more closely related to photosynthesis and thus is proposed to track the impacts of drought on vegetation growth. However, the detailed difference between SIF and NDVI in responding to drought has not been thoroughly explored. Here we present continuous ground measurements of NDVI and SIF at 760nm over four plots of wheat with different intensities of drought (well-watered treatment, moderate drought, severe drought and extreme drought). The average values of seasonal SIF were significantly lower under severe drought and extreme drought, while NDVI means only showed significant reduction in extreme drought. In the seasonal patterns, daily SIF could clearly separate the difference of drought gradient, while the difference of daily NDVI was clearer in the end of the field campaign. Daily SIF also significantly and positively correlated with soil moisture, indicating that SIF could be considered as an estimator of soil moisture to detect the information about agricultural drought. Furthermore, in extreme drought plot, the correlation of SIF and soil moisture was higher than that of NDVI and soil moisture in a shorter time lag (<15-day) but weaker in a longer time lag (longer than 30-day). The relationships of growth parameters with SIF and NDVI were further analyzed, showing a saturation of NDVI and unsaturation of SIF at high values of leaf area index and relative water content. These results suggested that SIF is better fit in early drought monitoring, especially over closure canopy, while NDVI is more feasible when drought lasted over a long time scale. Our findings in the study might provide deep insight into the utility of SIF in drought monitoring.

Quantitative and detailed spatiotemporal patterns of drought in China during 2001-2013.

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.

A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model.

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.

A new framework for evaluating the impacts of drought on net primary productivity of grassland.

This paper presented a valuable framework for evaluating the impacts of droughts (single factor) on grassland ecosystems. This framework was defined as the quantitative magnitude of drought impact that unacceptable short-term and long-term effects on ecosystems may experience relative to the reference standard. Long-term effects on ecosystems may occur relative to the reference standard. Net primary productivity (NPP) was selected as the response indicator of drought to assess the quantitative impact of drought on Inner Mongolia grassland based on the Standardized Precipitation Index (SPI) and BIOME-BGC model. The framework consists of six main steps: 1) clearly defining drought scenarios, such as moderate, severe and extreme drought; 2) selecting an appropriate indicator of drought impact; 3) selecting an appropriate ecosystem model and verifying its capabilities, calibrating the bias and assessing the uncertainty; 4) assigning a level of unacceptable impact of drought on the indicator; 5) determining the response of the indicator to drought and normal weather state under global-change; and 6) investigating the unacceptable impact of drought at different spatial scales. We found NPP losses assessed using the new framework were more sensitive to drought and had higher precision than the long-term average method. Moreover, the total and average losses of NPP are different in different grassland types during the drought years from 1961-2009. NPP loss was significantly increased along a gradient of increasing drought levels. Meanwhile, NPP loss variation under the same drought level was different in different grassland types. The operational framework was particularly suited for integrative assessing the effects of different drought events and long-term droughts at multiple spatial scales, which provided essential insights for sciences and societies that must develop coping strategies for ecosystems for such events.