•Chronic mild stress induced depression-like behaviors and impaired spatial memory in adult mice.•Chronic mild stress induced the abnormal expression of TPH2 and TH in mouse hippocampus, cortex and ...midbrain.•Chronic mild stress decreased the quantity of astrocytes and increased the quantity of microglia in mouse hippocampus.•Chronic mild stress increased the density of synapse and small spine in inner and outer molecular layers of dentate gyrus.
Chronic mild stress (CMS) model is most similar to the depression human suffered in daily life. Strong evidence proved the important role of hippocampal synaptic plasticity in the mechanism of depression. This study investigated the effect of CMS on synaptic plasticity in hippocampus. Our results showed that CMS impaired spatial memory and exploring ability, disturbed the release of neurotransmitters including 5-hydroxytryptamine (5-HT) and dopamine (DA), reduced the density of synaptic vesicle in inner molecular layer, increased the number of thin spines in inner and outer molecular layer, whereas did not affect the density of spine apparatus, the above mentioned were probably related to the reduction of astrocytes and activation of microglial cells.
Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the ...atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.
Land surface temperature (LST) is a comprehensive embodiment of surface energy balance and land surface processes. The spatial and temporal variation of LST is of great significance for studying ...surface characteristics and climate change. In this study, the spatiotemporal variations of LST in China from 2003 to 2018 is examined by using the continuous and derivable annual temperature cycle model. The trends of the annual mean and annual amplitude of LST is detected using the Mann-Kendall test and Theil-Sen estimator. In addition, we have further revealed the correlation between normalized difference vegetation index (NDVI) and LST in different land cover types. The results show that the annual mean LST presents a spatial distribution pattern of high values in the southern regions and low values in the northern regions and that the factors of altitude and land cover type also affect the LST's spatial distribution. The annual amplitude of the LST presents a spatial distribution pattern of high values in the northern regions and low values in the southern regions. In the majority of instances, the phase of LST in China was positioned between the 175th and 205th day of each year. Both the annual mean and annual amplitude of the LST have a mean increasing trend in China with a rate of 0.02 K/year, and the areas with large significant changes accounted for 8.6% out of the total area, with a mean rate of approximately 0.05 K/year, in this period. Significant changes in the annual mean LST were correlated with the change in vegetation coverage and the impact of land cover types on the interannual variations of LST is also determined in this study. While the increase in vegetation coverage in barren land exhibits a clearly recognizable upward trend in the annual mean LST, the improved vegetation coverage in the grassland region presents a downward trend in annual mean LST.
Monitoring agricultural drought via ground hyper-spectral remote sensing has always been a hot topic in the fields of agriculture and meteorology. In this study, a greenhouse experiment was conducted ...on wheat subjected to water stress during its different growth stages, namely tillering, jointing, and milk maturity. An instrument (HOBO ware PRO) used to continuously measure soil moisture was employed to measure the soil water content (SWC). An analytical spectral device (ASD) was utilized to obtain the spectral curve of wheat subject to different water treatment methods. The canopy temperature was obtained using thermal infrared sensors (METER SI-400). The relationships between the SWC, wheat drought stage, canopy temperature, and spectral response characteristics were elucidated. The results showed that the significant differences in spectral characteristics were due to water stress during the different growth stages of wheat. Red-edge parameters of red-valley position (RVP) and red-edge position (REP) both changed by 21 nm for the tillering-stage drought and the jointing-stage drought; however, the RVP and REP values for the milk maturity stage drought and the treatment under no water stress changed by 2 nm. Further, it was proved that the red-edge blue-shift phenomenon was affected not only by the different wheat growth processes, but also by the water stress at different growth stages. Red-edge reflectance clearly reflects wheat water stress at different growth stages. From SWC and canopy temperature analysis results, SWC and canopy temperature had a significant difference between wheat drought at different growth stages, and the canopy temperature at the jointing stage drought had the strongest change. The water index (WI) based on eleven vegetation water indexes exhibited a good performance for distinguishing wheat water stress at different growth stages. In conclusion, ground-based hyperspectral remote sensing can provide a large amount of high temporal and spectral resolution data on vegetation and its surrounding environment, making it an important technical tool for wheat drought monitoring, which has a great significance on the monitoring and early warning of wheat drought, reducing drought-related yield losses, and ensuring food security.
Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth's land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products ...in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD (Surface Radiation Budget Network) sites, 6 ARM (Atmospheric Radiation Measurement) sites, and 9 NDBC (National Data Buoy Center) sites. The results indicate that a relatively consistent performance among Landsat 5, 7, and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity (LSE) correction methods for Landsat 5, 7, and 8 sensors. Large bias and root mean square error (RMSE) of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index (NDVI). Except for the sites with incorrect LSE estimation, a mean bias (RMSE) of the differences between Landsat LST and in situ LST is 1.0 K (2.1 K) over snow-free land surfaces, −1.1 K (1.6 K) over snow surfaces, and −0.3 K (1.1 K) over water surfaces.
•The training set must be globally representative.•The single-element training set can achieve high-precision retrieval of SIF.•The length of the fitting window seriously affects SIF retrieval.•The ...number of singular vectors has remarkable effect on SIF retrieval.
The singular value decomposition (SVD) method, a sun-induced chlorophyll fluorescence (SIF) retrieval approach, has been used widely in far-red SIF spaceborne retrievals on a global scale. However, due to its semi-empirical nature, setting different parameter values may affect its retrieval accuracy, and ultimately have a large impact on its application. Hence, in this study, we evaluated the impact of parameter selection on the far-red SIF retrieval of this approach using TanSat satellite data. We first retrieved the far-red SIF within a narrow spectral window of 757.4–759.2 nm using the first four singular vectors (SVs) that were derived from the snow and soil spectra in a globally distributed training set. The retrievals are highly consistent with TanSat mission SIF (R2 = 0.72), indicating the reliability of our retrieval results. Then, the uncertainty was executed based on the above SIF retrievals and evaluated from five metrics: the number of SVs, length of the fitting window, type of training set elements, proportion of training set elements, and spatial distribution of the training set. Results showed that an unwise selection of the number of SVs would result in large retrieval errors (R2 = 0.03). Meanwhile, a fitting window that is too short or does not include a strong Fraunhofer line would cause severe errors and a large number of negative values (R2 = 0.06). Further, failure to include training samples below the equatorial zone in the Southern Hemisphere, and high latitude samples in the Northern Hemisphere, leads to poorer outcomes (R2 = 0.57). In contrast, the other global sampling metrics had little effect on the retrieval results (generally, R2 > 0.97). Accordingly, the relevant suggestions for using this method in the future are listed in this study for reference, with the potential to improve the accuracy of SIF retrievals from ultra-high spectral resolution instruments in the far-red spectrum.
Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate ...Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change.
•Both one-phase and two-phase trapezoids are examined in separating ETsoil and ETveg.•Two typical methods determining four endmembers are integrated into two trapezoids.•Analytical deductions of ...differences of ETsoil and ETveg fraction are made.•ETveg/ET are intercompared with those by five typical partitioning methods.
Partition of land surface evapotranspiration (ET) into soil evaporation (ETsoil) and vegetation transpiration (ETveg) is of great significance for scheduling agricultural irrigation, improving water-use efficiency of crop, and managing water resources. This study made a comprehensive evaluation of one-phase and two-phase surface temperature versus fractional vegetation cover trapezoids in the separation of soil evaporation from vegetation transpiration through analytical deductions and model applications, with surface temperatures at four end-members determined from the layered approach and the patch approach. The two trapezoids were tested on the MODIS data during June to September in 2012 at Daman superstation in Northwest China. The trapezoid-estimated ratios of transpiration to total ET (ETveg/ET) were intercompared with the ETveg/ET from five typical partitioning methods, namely, the stable isotope-method, the underlying water-use efficiency (uWUE) method, the transpiration estimation algorithm (TEA), the Pérez-Priego method, and the Wei method. Results showed that: 1) The two-phase trapezoid integrated with the layered approach and the one-phase trapezoid integrated with the layered approach performed the best and worst with root-mean-square errors of 52.6 W/m2 and 78.6 W/m2, respectively, when the estimated total ET was validated against the Bowen Ratio corrected eddy covariance measurements. 2) The five partitioning methods produced largely different ETveg/ET, with the highest values from the TEA method and the lowest values from the Pérez-Priego method. 3) The estimated vegetation transpiration and ETveg/ET by the two-phase trapezoid were generally higher than those by the one-phase trapezoid. 4) In the intercomparison of ETveg/ET, the layered approach agreed better with the five partitioning methods than the patch approach. 5) The two-phase trapezoid integrated with the layered approach overall produced the most consistent estimates of ETveg/ET with those from the five partitioning methods, with the lowest bias varying between -30.9% and 8.7% and root-mean-square differences varying between 16.8% and 36.8%. In summary, the two-phase trapezoid is theoretically more rational and appears to outperform the one-phase trapezoid. This study is beneficial for a better understanding of the differences, similarities, advantages and weaknesses of the one-phase and two-phase trapezoids in the partition of total ET to its soil and vegetation components.
The monthly mean land surface temperature (MMLST) reflects more stable intra- and interannual temperature variations, and therefore, it has a wider range of applications than instantaneous land ...surface temperature (LST). This study aimed to generate a high-resolution global MMLST product by temporally upscaling the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km instantaneous LST. First, six current methods were comprehensively evaluated using cross-validation technology. These six methods are the cross combinations of two temporal aggregation schemes: the average by observations (ABO) and average by days (ABD), and three conversion models: the diurnal temperature cycle model (DTC), the simple average of two instantaneous LSTs (TSA), and a weighted average model for multiple instantaneous LSTs (MWA). The analysis with measurements from 235 flux stations worldwide revealed that the choice of conversion model considerably affected the overall retrieval accuracy, whereas the influence of the aggregation scheme was minor. From the conversion model standpoint, MWA performed best, followed by DTC, and finally TSA; this order remained the same even if DTC and TSA were improved with mean bias correction. Notably, the errors of ABDMWA decreased as the number of daily mean LST (NOD) increased, whereas the errors of ABOMWA were not related to NOD. Accordingly, we deduced that the optimal strategy for estimating MMLST is using ABOMWA when NOD is < 20 and ABDMWA when NOD is <inline-formula> <tex-math notation="LaTeX">\ge 20 </tex-math></inline-formula>. Subsequently, we adopted this combination method to process MODIS instantaneous LSTs and produced a global 1-km MMLST dataset for the years 2003-2020. The validation showed a satisfactory accuracy with a root mean square error (RMSE) of 1.6 K. The intercomparison with MMLSTs from geostationary (GEO) satellites (containing complete LST daily cycle) presented a good agreement (biases < 0.3 K and STDs < 2 K). Compared with atmospheric infrared sounder (AIRS) L3 monthly standard physical retrieval (AIRS3STM) product which had the same temporal span, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature. Most importantly, it had a prominently better ability to retrieve spatial details of temperature variations due to its higher resolution. Our new method and product show promising prospects for applications in global change studies, where accurate spatially resolved MMLST data are one of the fundamental geophysical variables required.
The active ester-synthesis microorganisms in medium-high temperature Daqu (MHT-Daqu) largely impact the strong-flavor Baijiu quality, while their actual composition and metabolic mechanism remain ...unclear. Here, to explore how the active microbiota contributes to MHT-Daqu ester biosynthesis, metatranscriptomic and metaproteomic analyses coupled with experimental verification were performed. The results showed that the MHT-Daqu microbiota with the higher ester-forming ability exhibited a more active dynamic alteration from transcription to translation. The genera Aspergillus, Bacillus, Leuconostoc, and Pediococcus could transcribe and translate obviously more ester-forming enzymes. In the ester-synthesis metabolic network, the synergetic microbiota confirmed by interaction analysis, containing Eurotiales, Bacillales, and Saccharomycetales, played an essential role, in which the Eurotiales and its representative genus Aspergillus contributed the highest transcript and protein abundance in almost every metabolic process, respectively. The recombined fermentation verified that their corresponding genera could produce the ester and precursor profiles very close to that of the original MHT-Daqu active microbiota, while the microbiota without Aspergillus caused a polar separation. These results indicated that the synergetic microbiota with Aspergillus as the core dominated the metabolic network of ester synthesis in MHT-Daqu. Our study provides a detailed framework of the association between the active synergetic microbiota and ester synthesis in MHT-Daqu.
•MHT-Daqu active microbes were explored using integrative metatranscriptomic and metaproteomic.•The MHT-Daqu microbiota producing more ester exhibited a more dynamic alteration.•Ascomycota overall contributed the highest abundance from transcription to translation.•The synergetic microbiota cored by Aspergillus dominates the ester-synthesis metabolic network.
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