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Flash drought is an extreme phenomenon, characterized by unusually rapid intensification of drought severity, with strong impacts on plant growth especially for crops. However, it's unclear how hydrometeorological changes contribute to flash drought and how vegetation physiology, greenness, and productivity respond to flash drought. In this study, we developed an multivariate integrated framework for flash drought identification using a regression model based on principal-components analysis (PCA): the PCA regression flash drought (PRFD) model. Three criteria are specified to emphasize the rapid intensification of drought and its impacts on vegetation growth and water resources. We applied our new model in two geographical units with different climates and hydrology: one is dominated by agriculture and subject to natural drought (the Loess Plateau) and the other, a natural region with infrequent drought (the Qinling Mountains). We found that high frequency of flash droughts is most likely to occur in the eastern and central Loess Plateau and part of the Qinling Mountains. However, in relatively humid areas, flash drought shows strong intensity such as the Qinling Mountains and western and eastern edges of the Loess Plateau. Trend analysis indicates that PRFD has increased frequency, longer duration, and stronger intensity since the 1990s in both regions. From an ecological perspective, PRFD also showed a spatial pattern consistent with values of vegetation-related proxies that were below the long-term average, demonstrating vegetation transpiration, normalized-difference vegetation index (NDVI), gross primary productivity have obvious feedback on flash drought events. The Loess Plateau's NDVI responded immediately to flash drought, versus a 1-month lag in the Qinling Mountains. Because our proposed framework integrates multiple aspects of drought information, it can be applied in areas outside the study region according to regional hydrometeorological conditions. This has significant implications for improving agricultural management and forecasting future severe impacts of flash drought on plant growth. • A new integrated framework is proposed to identify flash drought (FD) events. • FD with greater frequency, longer pentads, and stronger intensity since 1990s. • Vegetation structure and physiological function respond rapidly to FD. • The response time of plant activity to FD is different in diverse climate-water regions. [ABSTRACT FROM AUTHOR]

As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p > 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. [ABSTRACT FROM AUTHOR]

As the most frequent and longest-lasting climate-related disasters, droughts are complex processes caused by hydrological imbalance, and have significant effects on ecosystem function and socioeconomic development. However, most conventional satellite-based drought indices and combined drought indices are mechanically constrained by the fact that they were developed and evaluated for a specific climate or critical ecological region, especially in China. A comprehensive drought index should be developed for extensive application in different climate-hydrology regions. To address this problem, an optimised principal component analysis method (PCA-RF) was proposed to support drought assessment across the transitional area between the humid and arid regions in central China, including the Loess Plateau (LP) and Qinling Mountains (QL). This method eliminated the causal relationships of non-linear changes for multi-aspect parameters such as land surface temperature, precipitation and soil moisture, and was used in developing a new index named "Meteorological drought index by PCA-RF methods (PMDI)". The results showed that PMDI had higher correlations and lower root mean square error than other drought indices (established using the empirical-weights method) with the 1-month standardized precipitation evapotranspiration index (SPEI-1). That means PMDI performed well in meteorological drought monitoring. Additionally, the normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) were utilised to evaluate PMDI effectiveness in detecting vegetation response to drought, which represents agricultural drought. The results indicated that correlations between PMDI and vegetation indices (VIs) were stronger when severe or extreme droughts occurred, and that their relationships were affected by the high spatiotemporal heterogeneity in vegetation change over the LP and QL. Overall, PMDI was shown here to be a very suitable index for monitoring meteorological drought (short-term) and has effective action in capturing the response of vegetation signals to agriculture drought events (long-term). Given the excellent performance of PMDI in diverse climate-hydrology conditions of our study regions, we concluded that the PMDI has considerable potential as a drought monitoring methodology on a larger spatial scale. • A new drought index was developed (PMDI), which integrated multiple sources of information describing different aspects. • PMDI performed well in different climate-hydrology regions for short- and long-term drought monitoring. • PMDI has reasonably good potential applicability in drought monitoring on a larger spatial scale. [ABSTRACT FROM AUTHOR]

• Forest age was mapped at 30 m resolution using GEDI-derived forest height maps. • Climate and topographic data accounted for 23–52% in forest age mapping. • Logistic equation was the best in mapping forest age among stand growth equations. • Random forest was applicable to map forest age without classifying tree species. Forest age is a key parameter for estimating forest growth and carbon uptake and for forest management. Remote sensing provides indirect but useful information for mapping forest age at large scales. However, existing regional and global forest age products were generated at low spatial resolutions (often 1000 m) and are not useful for most forest stands in China that are smaller than 1000 m. This study aims to map forest age at the 30 m resolution based on forest height maps mainly derived from the Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data, and analyze the roles of auxiliary data including temperature, precipitation, slope, and aspect in forest age mapping. Forest age is defined as the average age of dominant tree species within a pixel. Five commonly-used stand growth equations and twelve machine learning methods were tested for their suitability for mapping forest age of different tree species. We found that the Logistic equation performed the best among the tested stand growth equations and the Random Forest (RF) was the best among the tested machine learning methods. According to RF, forest height contributed predominantly to the variance in forest age mapping, while temperature, precipitation, slope, and aspect also had an overall non-negligible and variable contribution among different tree species. By integrating the climate and topographical variables, RF was applicable for forest age mapping without classifying the tree species. These results show that forest height maps derived from space-borne lidar data such as GEDI and ICESat-2 data are highly useful for mapping forest stand age, and the methodology developed in this study highlights a perspective for generating national and global forest age products at a high spatial resolution. [ABSTRACT FROM AUTHOR]