Vegetation, a key intermediary linking water, the atmosphere, and the ground, performs extremely important functions in nature and for our existence. Although satellite-based remote-sensing technologies have become important for monitoring vegetation dynamics, selecting the correct remote-sensing vegetation indicator has become paramount for such investigations. This study investigated the consistencies between a photosynthetic activity index (the solar-induced chlorophyll fluorescence (SIF) indicator) and the traditional vegetation index (the Normalized Difference Vegetation Index (NDVI)) among different land-cover types and in different seasons and explored the applicability of NDVI and SIF in different cases by comparing their performances in gross primary production (GPP) and grain-yield-monitoring applications. The vegetation cover and photosynthesis showed decreasing trends, which were mainly concentrated in northern Xinjiang and part of the Qinghai–Tibet Plateau; a decreasing trend was also identified in a small part of Northeast China. The correlations between NDVI and SIF were strong for all land-cover types except evergreen needleleaf forests and evergreen broadleaf forests. Compared with NDVI, SIF had some advantages when monitoring the GPP and grain yields among different land-cover types. For example, SIF could capture the effects of drought on GPP and grain yields better than NDVI. To summarize, as the temporal extent of the available SIF data is extended, SIF will certainly perform increasingly wide applications in agricultural-management research that is closely related to GPP and grain-yield monitoring.

In this study, precipitation, and temperature data from HadGEM2-ES under Representative Concentration Pathways (RCPs) 4.5 and 8.5 were used to evaluate drought in China in the 21st century. The K-means clustering algorithm was used to analyze the regional characteristics of the dry hazard index (DHI) in China, and the impact of climate change on the variation trend and periodicity of regional drought in China was explored. The results show that the temperature and potential evapotranspiration (PET) of all clusters have an increasing trend under the two RCPs, and the precipitation of most clusters shows a significantly increasing trend. The drought index calculated by the standardized precipitation-evapotranspiration index (SPEI) is higher than those calculated by the standardized precipitation index (SPI) and standardized effective precipitation evapotranspiration index (SP*ETI). The variation trends of drought intensity and frequency in China are not significant in the 21st century; however, the local variation trends are significant. The droughts in most parts of the Xinjiang Province, northern Tibet and western Qinghai Province show significantly increasing trends. According to the DHI analyses and the variations in the drought area ratio, with increases in greenhouse gas concentrations, the droughts in central and western China will become more severe, and drought will spread to the eastern areas of China. In the case that both precipitation and temperature may increase in the future, the increase in evapotranspiration caused by temperature rise will greatly affect drought dynamics. The main drought periodicity in China in the 21st century is 1~3.6 years. Drought is affected by climate change but not significantly.

Precipitation, as one of the main components of the hydrological cycle, is known to be significantly impacted by global climate change. In recent years, the frequency of extreme precipitation has increased, resulting in greater destructiveness. Atmospheric circulation has a significant impact on extreme precipitation in a region. This study aims to investigate the prospective changes in extreme precipitation and their relationship with large-scale atmospheric circulation in the Haihe River Basin. The Haihe River Basin is located in the North China Plain. Mountains and plains can be found in both the eastern and western parts of the study region. The summer seasons experience the most precipitation. The monthly and extreme precipitation (based on daily precipitation) results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models were evaluated using observed precipitation data, which was utilized as a reference. The CMIP6 models were used to assess future changes in the characteristics of extreme precipitation in the study region. The relationship between extreme precipitation and large-scale atmospheric circulation was also analyzed using historical observation data. Remote sensing results regarding land cover and soil erosion were used to analyze the risks of extreme precipitation and their influences in the study region. According to the results, their multi-model ensembles (MME) and BCC-CSM2-MR models, respectively, outperformed all other CMIP6 models in simulating monthly and extreme (based on daily precipitation) precipitation over the study region. Extreme precipitation demonstrated a rising degree of contribution and future risk under numerous scenarios. The degrees of contribution of R95p and R99p are anticipated to increase in the future. BCC-CSM2-MR predicted that Rx1day and Rx5day would decline in the future. Generally, extreme precipitation increased to a greater degree under SSP585 than under SSP245. Both the El Niño–Southern Oscillation and the Pacific Decadal Oscillation displayed substantial resonance with the extreme precipitation from 1962 to 1980 and around 1995, respectively. This study not only improves our understanding of the occurrence of extreme precipitation, but it also serves as a reference for flood control and waterlogging prevention in the Haihe River Basin.

Drought has had an increasingly serious impact on humans with global climate change. The drought index is an important indicator used to understand and assess different types of droughts. At present, many drought indexes do not sufficiently consider human activity factors. This study presents a modified drought index and the standardized precipitation evapotranspiration irrigation index (SPEII), considering the human activity of irrigation that is based on the theory of the standardized precipitation evapotranspiration index (SPEI). This study aims to compare the modified drought index (SPEII) and ·SPEI and self-calibrating Palmer drought severity index (scPDSI) in the major crop-producing areas and use SPEII to evaluate the possible future drought characteristics based on CMIP5 Model. The Pearson correlation coefficient was used to assess the relevance between drought indexes (SPEII, SPEI, and scPDSI) and vegetation dynamics. The normalized difference vegetation index (NDVI) was used to represent the vegetation dynamics change. The results showed that SPEII had better performance than the SPEI and scPDSI in monitoring cropland vegetation drought, especially in cropland areas with high irrigation. The winter wheat growth period of the SPEII had better performance than that of summer maize in croplands with higher irrigation levels on the North China Plain (NCP) and Loess Plateau (LP). In general, future drought on the NCP and LP showed small changes compared with the base period (2001–2007). The drought intensity of the winter wheat growth period showed an increasing and steady trend in 2020–2080 under the representative concentration pathway (RCP) 4.5 scenario on the NCP and LP; additionally, the severe drought frequency in the central LP showed an increasing trend between 2020 and 2059. Therefore, the SPEII can be more suitable for analyzing and evaluating drought conditions in a large area of irrigated cropland and to assess the impacts of climate change on vegetation.