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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]

• 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]