Increased drought frequency in Australia is a pressing concern for scholars. In 2018, a severe drought in eastern Australia was recorded by the Emergency Events Database (EM-DAT). To investigate the main causes and impacts of this drought across southeastern Australia, this work presents an overview of the drought mechanism and depicts its evolutionary process. The Standardized Precipitation Evapotranspiration Index (SPEI) from the Global Drought Monitor was used to identify the drought event and characterize its spatiotemporal distribution. The Normalized Difference Vegetation Index (NDVI) and the sun-induced chlorophyll fluorescence (SIF) were used to investigate the drought impacts on vegetation growth. In addition, the effects of drought response measures on Sustainable Development Goals (SDGs) were analyzed. Our results showed that the exceptional drought occurred across southeastern Australia from April to December, and it was most severe in July, owing to an extreme lack of precipitation and increase in temperature. Moreover, we identified profound and long-lasting impacts of the drought on NDVI and SIF levels, especially for cropland. Furthermore, we also found that SIF was superior to NDVI in detecting drought impacts. This study advised on how to formulate timely and effective drought-response measures and supports sustainable socioeconomic development in Australia.

Drought has become one of the major constraints on agricultural development, particularly in areas lacking water. Studying the effects of different water stresses on photosynthesis, growth, yield, water use efficiency (WUE) and water productivity (IWP) of winter wheat will provide scientific irrigation strategies for developing water-saving agriculture. According to the water field capacity, four different water stress levels were set, i.e., 30–40% of water field capacity (severe stress), 40–50% (moderate stress), 50–60% (mild stress) and 60–80% (well-watered), through an automatic irrigation system by controlling the irrigation amount. The results showed that the diurnal and 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. The Pn of mild stress only slightly decreased compared to that of well-watered and was even higher after May 16th. As a result, the dry biomass and 1000-grain weight under mild stress increased 2.07% and 1.95% compared with well-watered. Under all water stresses, the heights and straw weights of the winter wheat significantly decreased. It was also found that mild water stress increased the WUE and IWP, which further resulted in the negligible decrease of the fresh weight of the aboveground biomass, dry biomass weight, spike weight, grain weight. Conversely, WUE and IWP significantly decreased under moderate and severe stress, which can affect the growth of winter wheat. So the fresh weight of the aboveground biomass, dry biomass weight, spike weight, grain weight also significantly decreased under moderate and severe stress. Thus, mild stress results in the optimal use of water resources without a significant reduction in yield in the North China Plain. Therefore, mild stress can be considered as a suitable environment for winter wheat growth in arid areas.

Solar-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis, and is considered as a direct and non-invasive indicator of the functional status of photosynthetic machinery; the normalized difference vegetation index (NDVI) can also reflect photosynthetic characteristics. Both are related to surface soil moisture (SSM), which is susceptible to drought, and phenology plays an important role in these vegetation-moisture relationships. Based on a variety of gridded SIF, NDVI, and SSM datasets obtained from satellite sensors, we presented the detailed relationships of SSM with SIF and NDVI in different phenological stages. Results showed that SIF and NDVI were significantly correlated with SSM in different phenological stages, especially during the maturity and senescence stages. For different vegetation types, SIF was more sensitive than NDVI to SSM anomalies in grasslands and forests during the maturity stage and rainfed croplands during the senescence stage. These relationships were regulated by precipitation and mean temperature, and decreased precipitation had the most significant impact when accompanied by increased temperature during the maturity stage or decreased temperature during the senescence stage. Our findings shed light on the role that phenology has in the relationships of SSM with SIF and NDVI on a large scale, which can further improve the understanding of vegetation-moisture relationships.

Understanding spatiotemporal vegetation dynamics and their driving factors is critical for ecological conservation and ecosystem functioning under environmental change. However, the relative roles of climate change and human activities in shaping long-term vegetation dynamics remain unclear. Here, we integrated multi-source remote sensing data to construct a long-term NDVI dataset and investigated vegetation dynamics in the Yellow River Basin from 1982 to 2020 under the Grain for Green Project. Sensitivity analysis and residual trend analysis were applied to quantify the contributions of climate factors (temperature, precipitation, and solar radiation) and human activities. Our results revealed a significant increasing trend of vegetation dynamics from 1982 to 2020, with the significantly increased area rising from 28.86% (1982–2000) to 49.59% (2000–2020). Temperature and precipitation were significant positive with vegetation change, and account for 65.27% of the vegetation change. Human activities substantially promoted vegetation growth, with their positive contribution increasing from 42.35% to 68.92% between the two periods. Moreover, the area experiencing a significant increase in vegetation cover attributable to human activities expanded by 22.55% after 2000. This study highlights the critical role of climate change and human activity in driving the spatiotemporal vegetation dynamics, and provide a scientific reference for ecological conservation and land management strategies.

The future trajectory of tropical vegetation greening remains highly controversial, and the dry-season dynamics of tropical rainforests represent a critical knowledge gap. To advance understanding, this study integrates a suite of multisource satellite vegetation indexes (VIs) and process-based ecosystem models, aiming to comprehensively assess long-term trends in both canopy greenness and photosynthetic capacity during the dry season, and to critically evaluate the inter-product discrepancies in trend detection. Furthermore, we applied partial least squares regression (PLS) to isolate the relative contributions of major climatic factors to dry-season vegetation change. Results showed that great discrepancies in trends of vegetation greenness and productivity from different VIs, presenting that, dry-season canopy greenness (leaf area index, normalized difference vegetation index), and vegetation photosynthesis (sun-induced chlorophyll fluorescence), consistently displayed an increasing trend in tropical rainforest areas during the dry season over the past four decades at the average rate of 5.0 × 10⁻³ m³·m⁻³·yr⁻¹, 0.3 × 10⁻³ yr⁻¹, and 0.9 × 10⁻³ W·m⁻²·nm⁻¹·sr⁻¹·yr⁻¹, however, vegetation productivity (gross primary productivity, and vegetation optical depth with C, X, and Ku bands) showed large decreasing trends at a rate of −5.0 × 10⁻³ gC·m⁻²·yr⁻¹, −0.7, −0.1, and −0.7 × 10⁻³ yr⁻¹, respectively, and it was occupied with great spatial disparities that the initial increasing GPP in Congo weakened over time, shifting around the early 2000s from a positive trend (1982–2002) to a negative trend thereafter (2002–2018), while this trend was overwhelmingly contrary to GPP in Southeast Asia rainforest. Multisource remote-sensed VIs did not consistently support a significant greening tendency across the tropical rainforests during the dry season. However, process-based models consistently predicted a robust increasing trend of LAI and GPP in the dry-season tropical rainforests throughout the 21st century at the rate of 0.9 × 10⁻³ m³·m⁻³·yr⁻¹ and 2.4 × 10⁻³ gC·m⁻²·yr⁻¹. CO₂, vapor pressure deficit (VPD), temperature, and solar radiation, defined as the heat-related variables, played a largely negative role in contributing to vegetation growth in dry-season tropical rainforests from different VIs, and the water-related factors, including PRE, SM, and ET, positively contributed to vegetation growth. In terms of contributions, rising CO₂ concentration, temperature, and VPD were detected as three of the most important drivers of changes in tropical rainforests, with a contribution degree of 45%, 25%, and 20%, respectively. This study emphasizes that a comprehensive evaluation framework for data assimilation between multisource remote-sensed data and process-based model data in the tropics, which helps reduce uncertainties induced by intensifying climate change and the absence of observation stations, needs to be further improved and refined.