Search

Highlights • A new drought index which integrated multi-sensor remote sensing data was developed. • The new drought index performances better than most existing integrated drought indices and single drought indices. • The new drought index does not require site information and local expert knowledge for good performance. Abstract Drought is one of the most expensive but least understood natural disasters. Remote sensing based integrated drought indices have the potential to describe drought conditions comprehensively, and multi-criteria combination analysis is increasingly used to support drought assessment. However, conventional multi-criteria combination methods and most existing integrated drought indices fail to adequately represent spatial variability. An index that can be widely used for drought monitoring across all climate regions would be of great value for ecosystem management. To this end, we proposed a framework for generating a new integrated drought index applicable across diverse climate regions. In this new framework, a local ordered weighted averaging (OWA) model was used to combine the Temperature Condition Index (TCI) from the Moderate-resolution Imaging Spectroradiometer (MODIS), the Vegetation Condition Index (VCI) developed using the Vegetation Index based on Universal Pattern Decomposition method (VIUPD), the Soil Moisture Condition Index (SMCI) derived from the Advanced Microwave Scanning Radiometer–Earth Observation System (AMSR-E), and the Precipitation Condition Index (PCI) derived from the Tropical Rainfall Measuring Mission (TRMM). This new index, which we call the "Geographically Independent Integrated Drought Index (GIIDI)," was validated in diverse climate divisions across the continental United States. Results showed that GIIDI was better correlated with in-situ PDSI, Z-index, SPI-1, SPI-3 and SPEI-6 (overall r-value = 0.701, 0.794, 0.811, 0.733, 0.628; RMSE = 1.979, 0.810, 0.729, 1.049 and 1.071, respectively) when compared to the Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Scaled Drought Condition Index (SDCI), PCI, TCI, SMCI, and VCI. GIIDI also performed well in most climate divisions for both short-term and long-term drought monitoring. Because of the superior performance of GIIDI across diverse temporal and spatial scales, GIIDI has considerable potential for improving our ability to monitor drought across a range of biomes and climates. [ABSTRACT FROM AUTHOR]

• A comprehensive assessment of the three major droughts' impacts on Amazon forests. • The 2015/16 drought is the most severe meteorological drought since 2000. • Drought in 2015/16 showed lagging impacts on photosynthesis, greenness, and leaf area. • Photosynthesis, greenness, and leaf area had similar suppression in 2010 and 2015/16. • Canopy water content exhibited more widespread and severe drought impacts in 2015/16. The Amazon forests play an integral role in the global carbon cycle and have a substantial impact on Earth's climate. However, it is increasingly susceptible to the effects of prolonged droughts, exacerbated by climate change and human activities. This vulnerability underscores the importance of understanding the forests' reaction to such environmental stressors. Despite their significance, comprehensive cross-comparisons of the climate and vegetation responses during the 2005, 2010, and 2015/2016 drought episodes are not well-established. Here we utilize a range of gridded vegetation and climate datasets—including leaf area index (LAI), solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), vegetation optical depth (VOD), self-calibrating Palmer drought severity index (scPDSI), precipitation (P), land surface temperature (LST), and photosynthetically active radiation (PAR)—to thoroughly assess the climate and vegetation response to these three drought events. Our findings reveal that the extent of drought inhibition in the Amazon forests was 74.7 % in 2015, increasing to 81.3 % in 2016, a significant escalation from 49.6 % in 2005 and 57.7 % in 2010. The effects of these three droughts on vegetation varied in both physiological and structural aspects. The Amazon forests' photosynthetic activity, greenness, and leaf area experienced comparable suppression in 2010 and 2015/2016 droughts. However, canopy water content exhibited more extensive and severe impacts during the 2015/2016 drought. Our findings indicate that varying sensitivities to water deficit and solar radiation lead to diverse spatial patterns and intensities of vegetation response, highlighting the complex dynamics of the Amazon forests under drought stress. [ABSTRACT FROM AUTHOR]

• SOS NDVI dates occurred much earlier than SOS dates from EVI, LSWI, GPP, and SIF. • SOS LSWI dates were more linearly consistent with SOS GPP and SOS SIF than EVI and NDVI. • Temperature analysis showed that early SOS NDVI dates were more likely affected by snowmelt. Accurate phenology characterization is of great importance for measuring ecosystem dynamics, especially for carbon and water exchange between land and the atmosphere. Vegetation indices (VIs), calculated from land surface reflectance, are widely used to estimate phenology from the leaf and canopy structure perspective. Gross Primary Production (GPP) and solar-induced chlorophyll fluorescence (SIF) are used to estimate phenology from the canopy functional (physiological) perspective. To what degree are the spring phenology estimated from these different perspectives consistent with each other? In this study, we evaluated the consistency of the start of the growing season (SOS) in spring for snow-covered evergreen needleleaf forests (ENF) and deciduous broadleaf forests (DBF) using three vegetation indices, in-situ GPP data from the eddy covariance flux towers (GPP EC), GPP data from the Vegetation Photosynthesis Model (GPP VPM), and SIF data from the GOME-2. Results showed that SOS NDVI dates were much earlier than SOS dates from EVI (SOS EVI), land surface water index (LSWI) (SOS LSWI), GPP (SOS GPP ; SOS GPP-EC , SOS GPP_VPM) and SIF (SOS SIF) for both snow-covered evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF). SOS LSWI dates were more linearly correlated with SOS GPP and SOS SIF than SOS dates from NDVI and EVI. At ENF sites, SOS LSWI dates were 17 (± 27) days later and SOS EVI were 25 (± 34) days later than SOS GPP_EC dates. At DBF sites, SOS LSWI and SOS EVI dates were 1-week (± 13 days) later than SOS GPP_EC dates. In the snow-covered regions at mid- to high-latitude in the Northern Hemisphere, SOS LSWI dates were 2˜3 weeks (± 5 days) later than those of SOS GPP_VPM and SOS SIF for both ENF and DBF. Our results clearly highlight the need for further investigation of NDVI-based SOS dates, which were likely affected by snowmelt in snow-covered forests, and the potential of LSWI for tracking the effects of snow on SOS dates. Estimations of SOS dates in snow-covered forests should consider the effects of both snow cover and temperature on leaf emergence (green-up) and gross primary production. [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]

• Optimum air temperature of photosynthesis from GPP EC agree well with those from MODIS EVI. • Optimum air temperature of photosynthesis from GPP EC and MODIS EVI differed substantially from those that are currently used in satellite-based models. • Use of optimum air temperature of photosynthesis from site-year specific or biome-specific approaches can affects global GPP estimates substantially. Gross primary productivity (GPP) of vegetation is affected by air temperature. Biogeochemical models use the optimum air temperature (T opt) parameter, which comes from biome-specific look-up tables (T opt − b − LT). Many studies have shown that plants have the capacity to adapt to changes in environmental conditions over time, which suggests that the static T opt − b − LT parameters in the biogeochemical models may poorly represent actual T opt and induce uncertainty in GPP estimates. Here, we estimated biome-specific, site-year-specific, and site-specific optimum air temperature using GPP data from eddy covariance (EC) flux tower sites (GPP EC) (T opt − b − EC , T opt − sy − EC , T opt − s − EC), the Enhanced Vegetation Index (EVI) from MODIS images (T opt − b − EVI , T opt − sy − EVI , T opt − s − EVI), and mean daytime air temperature (T DT). We evaluated the consistency among the four T opt parameters (T opt − b , T opt − sy , T opt − s and T opt − b − LT), and assessed how they affect satellite-based GPP estimates. We find that T opt parameters from MODIS EVI agree well with those from GPP EC , which indicates that EVI can be used as a variable to estimate T opt at individual pixels over large spatial domains. T opt − b , T opt − sy , and T opt − s differed significantly from T opt − b − LT. GPP estimates using T opt − b and T opt − sy were more consistent with GPP EC than when using T opt − b − LT for all the land cover types. Our use of T opt − sy substantially improved 8-day and annual GPP estimates across biomess (from 1% to 34%), especially for cropland, grassland, and open shrubland. Our simple calculation shows that global GPP estimates differ by up to 10 Pg C/yr when using our suggested T opt − sy − EVI instead of using the static T opt − b − LT. Our new approach on estimating T opt has the potential to improve estimates of GPP from satellite-based models, which could lead to better understanding of carbon-climate interactions. [ABSTRACT FROM AUTHOR]