Drought has become one of the major constraints to agricultural development, particularly in areas that lack water. Studying the effects of different water stresses on the photosynthesis, growth, yield, water use efficiency (WUE) and irrigation water productivity (IWP) of winter wheat will provide data for the development of scientific irrigation strategies for water-saving agricultural methods. According to the size of the field water capacity, four different water stress levels were set, i.e., 30–40% (severe stress), 40–50% (moderate stress), 50–60% (mild stress) and 60–80% (well-watered) of field water capacity, controlling the amount of irrigation through an automatic irrigation system. The results showed that the seasonal changes in photosynthetic parameters, such as net photosynthetic rate (Pn), intercellular carbon concentration (Ci), stomatal conductance (Gs) and transpiration (E), significantly decreased under moderate and severe stress. As a result, the height, biomass and grain size of winter wheat decreased significantly, which led to low WUE and IWP. The Pn of the mild stress group only slightly decreased compared to that of the well-watered group, and was actually higher during the flowering and grain-filling stages, resulting in increases in dry biomass and 1000 grain weight of 2.07% and 1.95%, respectively. Higher WUE and IWP were attributed to higher yields and less water use. Thus, mild stress (60–80% field water capacity) resulted in the optimal use of water resources without a significant reduction in yield in the North China Plain (NCP). Therefore, mild stress can be considered a suitable environment for winter wheat growth in arid areas.

Accurate knowledge of soil moisture is crucial for agricultural drought monitoring. Data assimilation has proven to be a promising technique for improving soil moisture estimation, and various studies have been conducted on soil moisture data assimilation based on land surface models. However, crop growth models, which are ideal tools for agricultural simulation applications, are rarely used for soil moisture assimilation. Moreover, the role of data assimilation in agricultural drought monitoring is seldom investigated. In the present work, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product into the Decision Support System for Agro-technology Transfer (DSSAT) model to estimate surface and root-zone soil moisture, and we evaluated the effect of data assimilation on agricultural drought monitoring. The results demonstrate that the soil moisture estimates were significantly improved after data assimilation. Root-zone soil moisture had a better agreement with in situ observation. Compared with the drought index based on soil moisture modeled without remotely-sensed observations, the drought index based on assimilated data could improve at least one drought level in agricultural drought monitoring and performed better when compared with winter wheat yield. In conclusion, crop growth model-based data assimilation effectively improves the soil moisture estimation and further strengthens soil moisture-based drought indices for agricultural drought monitoring.

Understanding the winter wheat yield responses to drought are the keys to minimizing drought-related winter wheat yield losses under climate change. The research goal of our study is to explore the response patterns of winter wheat yield to drought in the North China Plain (NCP) and then further to study which climatic factors drive the response patterns. For this purpose, winter wheat yield was simulated by the Environmental Policy Integrated Climate (EPIC) crop model. Drought was quantified by standardized precipitation evapotranspiration index (SPEI), and the contributions of the various climatic factors were evaluated using predictive discriminant analysis (PDA) method. The results showed that the responses of winter wheat yield to different time-scale droughts have obvious spatial differences from the north part to the south part in the NCP. Winter wheat yield is more sensitive to the medium (6–9 months) and long (9–12 months) time-scale droughts that occurred in the key growth periods (April and May). The different response patterns of winter wheat yield to the different time-scale droughts are mainly controlled by temperature and water balance (precipitation minus potential evapotranspiration) in winter in the NCP. Compared with the water balance, temperature plays a more important role in driving the response pattern characteristics. These findings can provide a reference on how to reduce drought influences on winter wheat yield in the NCP.

Drought is one of the most devastating disasters and a serious constraint on agricultural development. The reflectance-based vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), have been widely used for drought monitoring, but there is a lag in the response of VIs to the changes of photosynthesis induced by drought. Solar-induced chlorophyll fluorescence (SIF) is closely related to photosynthesis of vegetation and can capture changes induced by drought timely. This study investigated the capability of SIF for drought monitoring. An intelligent irrigation control system (IICS) utilizing the Internet of Things was designed and constructed. The soil moisture of the experiment plots was controlled at 60–80% (well-watered, T1), 50–60% (mild water stress, T2), 40–50% (moderate water stress, T3) and 30–40% (severe water stress, T4) of the field water capacity using the IICS based on data collected by soil moisture sensors. Meanwhile, SIF, NDVI, Normalized Difference Red Edge (NDRE) and Optimized Soil Adjusted Vegetation Index (OSAVI) were collected for a long time series using an automated spectral monitoring system. The differences in the responses of SIF, NDVI, NDRE and OSAVI to different drought intensities were fully analyzed. This study illustrates that the IICS can realize precise irrigation management strategies and the construction of regulated deficit irrigation treatments. SIF significantly decreased under mild stress, while NDVI, NDRE and OSAVI only significantly decreased under moderate and severe stress, indicating that SIF is more sensitive to drought. This study demonstrates the excellent ability of SIF for drought monitoring and lays the foundation for the future application of SIF in agricultural drought monitoring.