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Hyperspectral remote sensing technology is an advanced and powerful tool that enables fine identification of the numerous soil reflectance spectrum characteristics. Heavy metal(loid)s (HMs) are the primary pollutants affecting the soil biodiversity and ecosystem services. Estimating HMs' concentrations in soils using hyperspectral data is an effective method but is challenging due to the effects of varied soil properties and measurement-related errors inflicted by atmospheric effects. This study focused on typical mining areas in the Fenwei Plain (FWP), China. Soil-related data were collected by leveraging airborne- and ground-based integrated remote sensing observations. The concentrations of eight HMs, namely copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), chromium (Cr), cadmium (Cd), arsenic (As), and mercury (Hg), were measured by laboratory analysis from 100 in situ soil samples. Soil reflectance spectra were processed based on resampling and envelope methods. The combination datasets of the concentrations and optimal soil reflectance spectra were used to build the soil-related parameter retrieval models using three machine learning (ML) methods, and the feasibility of applying the high-performance retrieval model to estimate the HM concentrations in mining areas was evaluated and explored. Spectral analysis results show that four hundred and twenty-eight bands of five wavelength ranges are of high quality and obviously demonstrate the spectral characteristics selected to build the soil-related parameter models. The evaluation results of eight combination data subsets and three methods show that the preprocessing of spectral data (ground- and airborne-based reflectance) and soil samples with the random forest (RF) method can obtain higher accuracy than support vector machine (SVM) and partial least squares (PLS) methods, denoted as the AER-ACS-RF and GER-GCS-RF models (the average RMSE values of eight HMs were 2.61 and 2.53 mg/kg, respectively). The highest R2 values were observed in Cd and As, with an equal value of 0.98, followed by that of Pb (R2 = 0.97). The relative prediction deviation (RPD) values of Cu and AS were greater than 1.9. Moreover, the airborne-based AER-ACS-RF model presents a good mapping effect about the concentrations (mg/kg) of eight HMs in mining areas, ranging from 21.65 to 31.25 (Cu), 16.38 to 30.45 (Pb), 62.02 to 109.48 (Zn), 23.33 to 32.47 (Ni), 49.81 to 66.56 (Cr), 0.09 to 0.23 (Cd), 7.31 to 12.24 (As), and 0.03 to 0.17 (Hg), respectively. [ABSTRACT FROM AUTHOR]

On the basis of the spectral shape of thermal infrared (TIR) emissivity for sandy land, a remote sensing sand index called the sand differential emissivity index (SDEI) is proposed in this article to simply and conveniently detect sandy land over large areas. The SDEI is evaluated on ground, airborne, and spaceborne thermal emissivity data, and it shows good characterization of sandy land and performs better in sandy land identification than two previous indices. The SDEI was also evaluated in the transition zones of China’s four mega-sandy lands and was applied to long-term land surface emissivity to obtain the spatial distribution and variation in China’s sandy land from 2000 to 2016. The findings showed that a mean accuracy of 96% and a mean kappa coefficient of 0.83 were obtained in the transition zones, and the sandy land in the transition zone exhibited a decreasing trend over the past 17 years and a significant decline in the Mu Us sandy land. Meanwhile, the sandy land area in China decreased by 3.6 × 104 km2 (1.53%) by the end of 2016 compared with that in early 2000. [ABSTRACT FROM AUTHOR]

The lunar surface temperature (LST) derived from thermal infrared (TIR) measurements can aid in understanding the physical properties of the lunar surface. The Diviner Lunar Radiometer Experiment (herein, Diviner) sensor provides global lunar surface observation in seven TIR channels. However, its retrieval of LST constantly uses a single emissivity value (i.e., 0.95) by ignoring the spatial variation of lunar surface, thereby reducing the accuracy of temperature and day–night temperature difference. To overcome this problem, this study developed a physical method called temperature–emissivity separation (TES) algorithm to retrieve LST and lunar surface emissivity from the daytime observation in three Christiansen Feature (CF) channels (7.55–8.05, 8.10–8.40, and 8.38–8.60 μm) of the Diviner, and then used the emissivity from daytime observation to inverse LST at nighttime observation. Findings showed that the TES algorithm could retrieve LST and emissivity with an error of less than 0.8 K and 0.008, respectively. However, observation noise significantly affected the retrieval accuracy, particularly for the low‐temperature pixels; moreover, high retrieval accuracy requires a surface temperature higher than 240 K. The new algorithm was applied to obtain the daytime and nighttime LST and emissivity from the Diviner images. Results showed that the LST retrieved from the algorithm differed approximately 3.9 K from that calculated from a single emissivity 0.95. Finally, an example of global surface temperature and emissivity were obtained. Consequently, the CF pixels were found to distribute in the latitude range from −60° to 60°; however, they did not have a large distribution in high‐latitude and near‐polar regions. Key Points: A physical algorithm was proposed to estimate lunar surface temperature and emissivity from Diviner daytime and nighttime observationsLunar surface temperature retrieved from the new algorithm differed 3.9 K from that calculated from a fixed emissivity 0.95Global lunar Christiansen Feature pixels were identified and found to mainly distribute in the latitude range from −60° to 60° [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing image requires atmospheric and land surface emissivity (LSE) data that are sometimes unattainable. To overcome this problem, a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input, by combining the split-window (SW) and temperature–emissivity separation (TES) algorithms. The SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands, and such radiance is applied to the TES algorithm to retrieve LST and LSE. The hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020, respectively. Moreover, the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm, involving 1%–2% uncertainty in atmospheric correction. The hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products, indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground measurement. Finally, the hybrid algorithm is applied to at two places. [ABSTRACT FROM AUTHOR]

This study analysed the changing pattern of the spectral features of copper-stressed leaves for several vegetation types, and explored the mechanism of the Copper Stress Vegetation Index (CSVI). First, the change of seven key spectral features (Green Peak, Red Valley, Red Shoulder, NIR (Near Infrared) Reflectance Platform, Blue-Edge, Yellow-Edge, and Red-Edge) with copper stress level from low to high, were presented and analysed. Second, the chlorophyll contents in leaves were investigated to explain the spectral characteristics at the visible band. Third, the leaf structure and absorption related to copper were analysed to explore the reason of changing pattern at NIR band. The results showed that there are significant changing trends at Blue-Edge, Green Peak, and Red-Edge while the changing pattern at NIR band depends on the vegetation type. The analysis on chlorophyll content, leaf structure, and absorption related to copper, gave an overall mechanism explanation for the spectral characteristics of copper-stressed vegetation and the wavelengths used in CSVI. The results and conclusions in this paper, contribute new knowledge of copper-stressed vegetation reflectance and the CSVI, and provide mechanism basement for the remote sensing of copper-stressed vegetation. [ABSTRACT FROM AUTHOR]

The sky view factor (SVF) is a crucial variable widely used to quantify the characteristics of surface structures and estimate surface radiation budget. Many SVF models based on raster data have been developed but not yet evaluated in a more quantitative and uniform manner. In this paper, four typical SVF models (Dozier‐Frew (D‐F), Manners, Lindberg‐Grimmond (L‐G), and Helbig_h) are evaluated using the SVF derived from simulated fisheye images based on the digital surface model (DSM) and digital elevation model data. The SVF calculated by D‐F method using DSM data has the best accuracy, with a mean bias error of −0.007, root‐mean‐square error of 0.069, and coefficient of determination (R2) of 0.914. For the SVF value derived from digital elevation model data, L‐G method shows good performance, with an mean bias error of 0.013, root‐mean‐square error of 0.032, and R2 of 0.897. The pixels near the edges of buildings, within the valley or along ridgelines, have higher SVF deviations. In addition, the slope angle calculated using DSM data has some artificial defects that make the significant impact on the SVF biases due to their calculation method and the discontinuous surface in urban areas. Thus, L‐G and Helbig_h methods are more applicable for the DSM data due to the difficulty in defining slope and aspect angles. Moreover, the high accordance of SVFs between Helbig_h and L‐G methods implies that the Helbig_h method is an alternative in virtue of its simpler form and lower computation cost than L‐G method. Plain Language Summary: Four typical sky view factor (SVF) models are evaluated and compared using simulated SVF data based on two kinds of raster terrain data to estimate models' accuracies and their different spatial characteristics. They show that different calculation accuracies and large SVF bias are mainly near the edges of buildings, along the valley and ridgeline. The slope angle can cause larger SVF biases than the aspect angle, and the influence on the SVF derived from DSM data is more significant than that derived from digital elevation model data. This paper evaluates these methods in a more quantitative and uniform manner, which offers accuracy levels that are important for SVF's application in various fields. The new findings and implications for possible improvements can benefit the estimation of the urban heat island, surface solar radiation, and melting of the polar icecap. Key Points: Four different SVF models are evaluated and compared using urban DSM and montane DEM data in a quantitative and uniform mannerThe slope angle calculated using DSM data has some artificial defects that cause large SVF biases due to the discontinuous surface in urban areasThe L‐G and Helbig_h methods are more applicable for urban DSM data because defining slope and aspect angles is difficult for the DSM data [ABSTRACT FROM AUTHOR]

Fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter in ecosystem productivity and carbon balance estimation. FPAR can be estimated from various satellite images but its product might have significant differences due to the usage of various algorithms. This work proposes a generalized FPAR retrieval method for Landsat 5/ Thematic Mapper (TM), Landsat 7/Enhanced Thematic Mapper Plus (ETM+), Landsat 8/ Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer(MODIS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SPOT/VEGETATION, and HJ-1/CCD in the form of two linear models, namely, the BOA (bottom of atmosphere) model and the TOA (top of atmosphere) model, to reduce FPAR discrepancy among sensors. The BOA model estimates canopy FPAR from land surface multiband reflectance after atmospheric correction, whereas the TOA model estimates FPAR from apparent multiband reflectance at the TOA. Analysis results found that the FPAR errors from the BOA and TOA models were approximately 0.03 and 0.06, respectively, and the difference among FPAR estimated from different sensors was turned out to be less than 0.015 in theory. In addition, the FPAR difference between the two models was generally small, especially under low aerosol optical depth (AOD) and densely vegetated conditions. Ground validation using the datasets from the HiWATER and Validation of Land European Remote Sensing Instruments (VALERI) programs showed that the FPAR errors were 0.16 and 0.18 for the BOA and TOA models, respectively, which might be affected by the time interval of ground and satellite observation, spatial scale effect, and atmospheric correction errors. Moreover, this paper applied the new methods to estimate FPAR in different dates at the Heihe River basin and conduct a cross-comparison of FPAR from various sensors, and consequently obtained acceptable results. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) retrieved from thermal infrared (TIR) remote sensing data displays strong directional anisotropy. This characteristic makes the LST at different angles incomparable and limits its application. Multi-angle data are usually required to model or retrieve the angular anisotropy, but most of the TIR images are observed from one single angle. This study investigates the relationship between fractional vegetation coverage (FVC) and thermal radiance, and proposes a practical method for angular normalization of LST using an FVC-radiance space LST angular normalization (called the FRAN method) to perform LST angular normalization for two-component (leaf and soil) vegetated canopy using only single-angle, single-temporal TIR data. In this method, the single-angle data are used to construct the space and build a linear relationship between FVC and radiance. Nadir radiance and LST are obtained by using FVC data at nadir. The validity and accuracy of the FRAN method are tested by using simulated dataset. Results show that 60%–70% of the angular effect of LST can be corrected, and the maximum LST correction reaches 2.0 K. The LST biased error distribution changes to unbiased distribution after angular normalization. The method sensitivity to the vertex of FVC-radiance space and the parameter in FVC calculation is analyzed. The FRAN method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data, and obtains the maximum correction of LST as 1.2 K. Viewing zenith angle and land cover affect the corrected LST. The FRAN method has the potential for practical angular normalization of LST with single-angle data. • A novel method for LST angular normalization from single observation data • 60%–70% of LST angle effect can be corrected by the method • Pixels with large VZA and FVC have large LST correction. [ABSTRACT FROM AUTHOR]

The Gaofen-5 (GF-5) satellite, the only satellite that provides the thermal infrared (TIR) sensor in the national high-resolution earth observation project of China, will observe earth surface at a spatial resolution of 40 m in four TIR channels. This paper aims at developing a new nonlinear, four-channel split-window (SW) algorithm to retrieve land surface temperature (LST) from GF-5 image. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapors (CWV) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the new algorithm can obtain LST with root-mean-square errors of less than 1 K. Compared with previous two- and three-channel SW algorithms, the four-channel SW algorithm obtained better results in estimating LST, especially under moist atmospheres. Methods of estimating CWV and pixel emissivity were also conducted. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapor demonstrated the good performance of the proposed algorithm. At last, the new SW algorithm was validated using ground-measured data at six sites, and some simulated images from airborne hyperspectral TIR data. [ABSTRACT FROM PUBLISHER]

This study proposed a new narrow band index to characterize the Cu (Copper) stress degree on vegetation (Copper Stress Vegetation Index, CSVI). Firstly, the spectral reflectance and biochemical data of wheat, pea, locust and ash were analysed using Pearson correlation coefficient (r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the reflectance near 550 nm and 700 nm correlated positively with Cu contents in leaves and solutions, and negative correlation was present in the range of 800–900 nm. Secondly, the selected wavelengths of 550 nm, 700 nm, and 850 nm were used to establish CSVI, and it was compared with existing popular vegetation indices (VIs) related to heavy metal stress (Normalized Difference Vegetation Index (NDVI), Red-Edge Position (REP), Difference Vegetation Index (DVI), Photochemical Reflectance Index (PRI)) by calculating Pearson correlation coefficient between VIs and Cu contents in leaves and solutions. Thirdly, verifications of CSVI on other vegetations were conducted, and the performance of CSVI was also compared with that of NDVI, REP, DVI, and PRI. The results suggested that CSVI showed significant correlation with Cu stress degree, and the correlation of CSVI was much stronger than that of other VIs for all the tested vegetations. The proposed CSVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness, straightforward calculation, and robustness for different vegetations. This study focused on the spectral reflectance at the leaf scale, so it is expected that future work extends it to canopy scale and mixed-pixel scale. [ABSTRACT FROM PUBLISHER]

The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) has been identified as one of the key parameters in calculating ecosystem productivity. The objective of this paper is to model the vertical profile of FPAR in the canopy using a radiative transfer model, the Modified Simultaneous Heat and Water (MSHAW) radiation model. Model analysis indicated that the vertical distribution of the canopy FPAR was dependent on the leaf area index (LAI), average leaf orientation angle (ALA), solar position, and sky conditions. In the validation of the MSHAW model with three varieties of wheat leaf profile at different growth stages, two parabolic functions were developed to approximately reconstruct the shape of the wheat leaf for the first time and, consequently, the vertical profiles of LAI and ALA used to drive the MSHAW model were estimated. The validation results indicated that the estimated FPAR was close to the measurements made with the SunScan canopy analysis system with an RMSE of approximately 0.15 for the continuous canopy. Finally, this paper also discusses a promising method to perform time normalisation on canopy FPAR data using multiple temporal remotely sensed data observations and to retrieve FPAR from remotely sensed data based on the analysis of the MSHAW model. [ABSTRACT FROM AUTHOR]

Rhizome cutting is prevalent in propagation for Hemerocallis citrina Baroni. This study aimed to reveal the influence of rhizome weight on plantlet growth, and evaluate a new cutting protocol. Three levels of rhizome weight (big (BR), medium (MR) and small (SR)) were compared by measuring plantlet growth four times during cutting propagation. Sectioning rhizomes longitudinally along shaft axis of crown into 2, 3 or 4 parts (S2, S3 and S4), was tested on rhizomes with two bud sizes (Large bud (LB), Small bud (SB)). Propagation coefficient, the number of plantlets obtained per rhizome, kept similar regardless of rhizome weight, while S3 and S4 increased propagation coefficient. Rhizome weight changed the relative growth rates on different dates. SR resulted in lower aboveground dry biomass, leaf area, total fibrous root length, relative total fibrous root length growth rate and N deficiency compared to BR. Sucrose concentration in rhizome decreased with time and fructose concentration was lowest in MR at the end of experiment. Aboveground dry biomass and total fibrous root length were lowest in S4, followed by S3 and S2. Rhizome weight and number of sectioning per rhizome influence cutting propagation, and sectioning rhizomes into three pieces has higher propagation coefficient and less retardation on growth. According to N deficiency in plantlets during the propagation, N fertiliser is probably needed during cutting. [ABSTRACT FROM AUTHOR]

Accurate spectral calibration of the in-flight sensors is crucial for processing and exploration of remotely sensed data. This paper developed a strategy to make spectral recalibration (i.e., spectral response function, central wavelength, and bandwidth) for in-flight broadband sensor using a device-responsivity-decomposition model with a priori knowledge and an optimization algorithm. Sensitivity analysis indicates that an accurate result requires the targets to be observed under a dry and clear atmospheric condition (column water vapor <2\ \g/cm^2 and visibility > 23 km) and no more than 5 \% error is included in the measured data. The new strategy was used to retrieve the spectral parameters along with radiometric calibration coefficients for a multichannel camera onboard an unmanned aerial vehicle from simultaneously remotely sensed and ground measured data sets over 19 (15 color-scaled and four gray-scaled) man-made surface targets, and the retrieved results were validated with a similar data set over another four man-made targets. It demonstrated that the camera's spectral parameters were accurately retrieved and an error less than 3.5 \W/m^2/\mu\m/sr was brought to the channel radiance. [ABSTRACT FROM AUTHOR]

Abstract: A sensor's footprint determines the target that is observed by the sensor, and influences the angular features of the target's directional brightness temperature (DBT) at the field site. This paper describes a new radiative transfer model (FovMod) to simulate the DBT of the row crop canopy by considering the sensor's footprint in the ground measurements. The FovMod firstly divides the sensor's circular or elliptical footprint into a few small cells, and then estimates the components' fractions (e.g., leaves, sunlit soil and shaded soil) in each cell based on the gap probability theory. The canopy's DBT is finally obtained by weighting the components' brightness temperatures and their fractions using a Gaussian point spreading function (PSF) of the sensor's response. Simulation results indicate that a small footprint causes the distribution of the DBT to be strongly dominated by the row direction and a single component's temperature but little influenced by the solar position. On the contrary, a large footprint smoothes the row-space effect and causes the DBT to distribute as a uniform, continuous canopy. Comparison with a previous parallel model shows that if the diameter of the sensor's circular footprint extends to 1.5–2.0 times as large as the total width of the row crop canopy, the footprint effect is minimized, and the ground measured DBT can, theoretically, be used to evaluate the parallel model with negligible error. Finally, validations with a maize canopy demonstrated that the new model performed more accurately than the parallel model to simulate the DBT. Moreover, the FovMod also provides an opportunity to assess the measurement uncertainty caused by some unexpected changes in the sensor's footprint. [Copyright &y& Elsevier]

Abstract: The angular effects of emissivity are ignored in current land surface temperature (LST) products. As a result, the directionality of these LST products limits their further application in many fields. Accurate correction of the angular problem of LST products requires explicit understanding of the angular effects of emissivity at the pixel scale. Currently, nearly ten years of global emissivity products of MODIS are available. However, the pixel-scale directionality of emissivity has never been analyzed. By performing a statistical analysis of 5-year MODIS emissivity products over most of East Asia, we generated the empirical relationships between the directional emissivity, land cover, and seasonal variations. Two look-up tables (LUTs) of directional emissivity were created for typical land cover types and applied to the generalized split-window algorithm to modify the MODIS LST. The results showed that the angular effect of emissivity could introduce a significant bias of −1–3 K to the 1 km resolution LST. Finally, the spatial scale effects of emissivity were analyzed, and it was found that the temperature differences caused by scale effects fell within +/−0.5 K for most pixels if 5 km emissivity was used in 1 km LST retrieval. Therefore, wide use of the LUTs can be expected. [Copyright &y& Elsevier]

Urban land surface temperature (ULST) is a key surface feature parameter in urban heat island studies. However, the geometry and adjacency effects are usually neglected in conventional land surface temperature (LST) retrieval methods. In this study, considering the geometry and adjacency effects, a new urban canopy multiple-scattering thermal radiative transfer (UCM-RT) model and an urban temperature-emissivity separation (UTES) algorithm were developed for ULST retrieval from Chinese GaoFen-5 (GF-5) satellite thermal infrared (TIR) images. The UCM-RT model and the UTES algorithm incorporate multiple scattering within the urban building canopy and consider the thermal radiance contribution from adjacent pixels and the atmosphere. Two schemes were designed to evaluate the geometry and adjacency effects quantitatively in this study: (1) evaluating their impact on ground temperature and top of atmosphere (TOA) brightness temperature for GF-5 four TIR bands from the simulation dataset, and (2) comparing the retrieval temperature difference between the UTES algorithm and conventional temperature-emissivity separation (TES) algorithm (without considering geometry and adjacency effects). For a specific simulation situation, the magnitude of the geometric effects on urban ground temperatures were found to be approximately 3.2 K, 4.2 K, 4.0 K, and 3.9 K for the four GF-5 TIR bands, while the effects on the TOA brightness temperature reached 2.4 K, 3.3 K, 3.3 K, and 3.0 K for those bands, respectively. The results show that the geometry effects have a significant influence on the thermal radiative transfer process. The temperature error of the UTES algorithm was generally up to 0.8 K, much lower than that of the conventional TES algorithm (~2.2 K) in urban surface. The results indicate that the UTES algorithm is suitable for ULST retrieval, and the retrieval temperature difference between the UTES algorithm and conventional TES algorithm ranges from 0.1 K to 2.1 K, especially for dense building pixels. Additionally, the thermal radiation from adjacent pixels in high-spatial-resolution TIR images has significant influence on ULST retrieval. The comparison between the ULST algorithm for urban land surface and the conventional TES algorithm for the flat surface indicated that the three-dimensional structure inside the pixel has a significant influence on the ULST retrieval. The findings of this study indicate that the geometry and adjacency effects must be considered in ULST retrieval for highly accurate results. [Display omitted] • An UCM-RT model taking into account geometry and adjacency effects was deduced. • Accuracy of Urban TES algorithm based on urban effective emissivity is about 0.8 K. • The magnitude of geometry and adjacency effects on urban LST is up to 2.1 K. • Application of high spatial resolution TIR image in urban LST retrieval was analyzed. [ABSTRACT FROM AUTHOR]

Cucumber (Cucumis sativus L.) is a globally prevalent and extensively cultivated vegetable whose yield is significantly influenced by various abiotic stresses, including drought, heat, and salinity. Transcription factors, such as zinc finger-homeodomain proteins (ZHDs), a plant-specific subgroup of Homeobox, play a crucial regulatory role in stress resistance. In this study, we identified 13 CsZHDs distributed across all six cucumber chromosomes except chromosome 7. Phylogenetic analysis classified these genes into five clades (ZHDI-IV and MIF) with different gene structures but similar conserved motifs. Collinearity analysis revealed that members of clades ZHD III, IV, and MIF experienced amplification through segmental duplication events. Additionally, a closer evolutionary relationship was observed between the ZHDs in Cucumis sativus (C. sativus) and Arabidopsis thaliana (A. thaliana) compared to Oryza sativa (O. sativa). Quantitative real-time PCR (qRT-PCR) analysis demonstrated the general expression of CsZHD genes across all tissues, with notable expression in leaf and flower buds. Moreover, most of the CsZHDs, particularly CsZHD9-11, exhibited varying responses to drought, heat, and salt stresses. Virus-induced gene silencing (VIGS) experiments highlighted the potential functions of CsZHD9 and CsZHD10, suggesting their positive regulation of stomatal movement and responsiveness to drought stress. In summary, these findings provide a valuable resource for future analysis of potential mechanisms underlying CsZHD genes in response to stresses. [ABSTRACT FROM AUTHOR]

Mutation of DOMAIN OF UNKNOWN FUNCTION 679 MEMBRANE PROTEIN in cucumber induces in vivo maternal haploids and suggests prospects for cucurbit breeding. [ABSTRACT FROM AUTHOR]

Landsat 8/thermal infrared sensor (TIRS) is suffering from the problem of stray light that makes an inaccurate radiance for two thermal infrared (TIR) bands and the latest correction was conducted in 2017. This paper focused on evaluation of land surface temperature (LST) retrieval from Landsat 8 before and after the correction using ground-measured LST from five surface radiation budget network (SURFRAD) sites. Results indicated that the correction increased the band radiance at the top of the atmosphere for low temperature but decreased such radiance for high temperature. The root-mean-square error (RMSE) of LST retrieval decreased by 0.27 K for Band 10 and 0.78 K for Band 11 using the single-channel algorithm. For the site with high temperature, the LST retrieval RMSE of the single-channel algorithm for Band 11 even reduced by 1.4 K. However, the accuracy of two of three split-window algorithms adopted in this paper decreased. After correction, the single-channel algorithm for Band 10 and the linear split-window algorithm had the least RMSE (approximately 2.5 K) among five adopted algorithms. Moreover, besides SURFRAD sites, it is necessary to validate using more robust and homogeneous ground-measured datasets to help solely clarify the effect of the correction on LST retrieval. [ABSTRACT FROM AUTHOR]

• The efficiency of Sentinel-3A SLSTR LST and NDVI was proved in monitoring agricultural drought. • VTCI can only be used in warm seasons (late spring and summer) to conduct agricultural drought monitoring. • VTCI was encouraging as a near-real-time drought monitoring indicator. Agricultural drought is one of most damaging agricultural hazards worldwide that can bring significant agricultural losses and water scarcity. The use of satellite images for monitoring agricultural drought has received increasing research attention and has also been applied at both the regional and global scales. In this paper, the land surface temperature (LST) and radiance products of the new Sentinel-3A SLSTR (sea and land surface temperature radiometer) launched by European Space Agency (ESA) are used for the first time for estimating the vegetation temperature condition index (VTCI), which in turn is used for monitoring agricultural drought in the Hetao Plain of Inner Mongolia, China. This paper initially analyzes the correlation between LST and normalized difference vegetation index (NDVI) by using time series time MODIS LST and NDVI products under different vegetation growth conditions. The findings reveal that VTCI can only be used in warm seasons (late spring and summer periods) when negative correlations between LST and NDVI are observed. Therefore, VTCI images are captured in the study area between July and August 2017 by using Sentinel-3A SLSTR LST and NDVI and are utilized for drought investigation. These images reveal that the average VTCI of the cultivated land pixels in the study area has increased from 0.4511 on July 28 to 0.5229 on August 12 before declining to 0.4710 on August 18 due to the rainfall in the first period, thereby indicating that VTCI has a timely response to rainfall. Meanwhile, cross-comparison of VTCI values from Sentinel-3A SLSTR shows high consistency in terms of spatial distribution with that estimated from EOS MODIS products. The difference between these indices ranged from −0.1 to 0.1 for most points, especially in the cultivated land cover. Overall, the findings support the use of the LST and NDVI products of Sentinel-3A SLSTR in monitoring agricultural drought. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm. [ABSTRACT FROM AUTHOR]

Snow cover is an essential climate variable of the Global Climate Observing System. Gaofen-4 (GF-4) is the first Chinese geostationary satellite to obtain optical imagery with high spatial and temporal resolution, which presents unique advantages in snow cover monitoring. However, the panchromatic and multispectral sensor (PMS) onboard GF-4 lacks the shortwave infrared (SWIR) band, which is crucial for snow cover detection. To reach the potential of GF-4 PMS in snow cover monitoring, this study developed a novel method termed the restored snow index (RSI). The SWIR reflectance of snow cover is restored firstly, and then the RSI is calculated with the restored reflectance. The distribution of snow cover can be mapped with a threshold, which should be adjusted according to actual situations. The RSI was validated using two pairs of GF-4 PMS and Landsat-8 Operational Land Imager images. The validation results show that the RSI can effectively map the distribution of snow cover in these cases, and all of the classification accuracies are above 95%. Signal saturation slightly affects PMS images, but cloud contamination is an important limiting factor. Therefore, we propose that the RSI is an efficient method for monitoring snow cover from GF-4 PMS imagery without requiring the SWIR reflectance. [ABSTRACT FROM AUTHOR]

Pre-harvest sprouting (PHS), the germination of seeds on the plant prior to harvest, poses significant challenges to agriculture. It not only reduces seed and grain yield, but also impairs the commodity quality of the fruit, ultimately affecting the success of the subsequent crop cycle. A deeper understanding of PHS is essential for guiding future breeding strategies, mitigating its impact on seed production rates and the commercial quality of fruits. PHS is a complex phenomenon influenced by genetic, physiological, and environmental factors. Many of these factors exert their influence on PHS through the intricate regulation of plant hormones responsible for seed germination. While numerous genes related to PHS have been identified in food crops, the study of PHS in vegetable crops is still in its early stages. This review delves into the regulatory elements, functional genes, and recent research developments related to PHS in vegetable crops. Meanwhile, this paper presents a novel understanding of PHS, aiming to serve as a reference for the study of this trait in vegetable crops. [ABSTRACT FROM AUTHOR]

The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5–7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images. [ABSTRACT FROM AUTHOR]

• Autumn phenology-climate relationships were analyzed for 15 herbaceous species. • Effects of precipitation and temperature/light on autumn phenology are opposite. • Simulation results show that drought promotes leaf senescence in most species. • The responses of autumn phenology to climate change are location-specific. Autumn phenology is important in determining the growing season length and controlling carbon and energy exchanges in terrestrial ecosystems. However, our knowledge on the interaction processes of vegetation autumn phenology and climate changes remains limited, especially for herbaceous plants. In this study, we comprehensively analyzed the responses of autumn phenology of grassland vegetation to climate changes by using ground-observed brown-down date records of 15 grass species and daily temperature, precipitation, and day length data at six stations. Aside from conducting correlation analysis, we also simulated the brown-down date with a newly developed model by incorporating the effect of drought stress (CDDP) into the traditional chilling-degree-days (CDD) model and compared it with the CDD model. Another revised CDD model included the effect of day length (CDDD) and null model (multiyear average, NM). The statistical results showed a predominant significant negative correlation between the brown-down date and previous temperature/day length in 27.3%/40.9% of site species but a predominant significant positive correlation between the brown-down date and previous precipitation in 54.6% of site species. The opposite effects of previous precipitation and previous temperature/day length on the brown-down date were induced by local thermal–moisture conditions. With regard to the modeling results, the CDDP model was selected as the optimal model for 73% of site species with insufficient water supply in preseason, while the CDD model was selected as the optimal model for 18% of site species with a relatively wet but cold preseason. The CDDD model was selected as the optimal model for only two cases. The average estimation error based on the CDDP model (7.4 days) was lower by 2.0/1.5/1.7 days than that based on the CDD/CDDD/NM model. Overall, this study comprehensively demonstrated the important role of water availability in controlling the autumn phenology process of herbaceous plants. [ABSTRACT FROM AUTHOR]

Abstract Comprehensive analysis of how wheat phenology responds to environmental factors in global scale is helpful for tackling the possible adverse effects of ongoing climate change on wheat production. In this study, six phenological parameters of global wheat, i.e., the growing season start (SGS), peak (PGS), end (EGS), length (LGS), as well as the vegetative period length (LVP) and reproductive period length (LRP), were retrieved from remote sensing data (1981–2014) by threshold-, logistic-, and shape-based methods. And then, we analyzed the effects of temperature, precipitation, short-wave (SW) radiation, and frost on spatiotemporal patterns of wheat phenology. In addition, haze impacts on wheat phenology were investigated in China and India where haze weather appears frequently in winter-spring seasons. Results showed that the occurrence time of SGS/PGS/EGS is gradually advanced from the pole to the equator and annual mean air temperature can explain >70% of their spatial variations. A dominant advanced SGS/PGS/EGS and a shortened LGS/LVP/LRP were detected in the study region due to the significant increase in temperature and SW radiation, as well as the decrease in frost days. Interannual fluctuations of SGS/PGS/EGS are primarily controlled by air temperature, while precipitation and frost only exerted some obvious impacts in some locations. Higher preseason temperature would induce an earlier wheat phenology and a shorter growing season, while adequate precipitation and frequent frost in preseason could delay the occurrence timing of wheat phenology and lead to a longer growing season. Besides, the decreased temperature resulted from severe haze weather may have partly counteracted the global-warming-induced advancing trend of wheat phenology in China, but further advanced the occurrence timing of wheat phenology through prompting vernalization in India. Overall, though wheat growth is largely constrained by human management, we still highlight the strong impacts of global climate change on wheat phenology. Graphical abstract Unlabelled Image Highlights • Three wheat phenology estimation methods were compared in this study. • Air temperature mainly determines the spatiotemporal patterns of wheat phenology. • Precipitation only exhibited a strong impact on growing season peak and end. • Growing season start and peak were also closely connected with frost frequency. • An opposite impact of haze weather on wheat phenology was found in China and India. [ABSTRACT FROM AUTHOR]

SUMMARY: Fruit size and shape are controlled by genes expressed during the early developmental stages of fruit. Although the function of ASYMMETRIC LEAVES 2 (AS2) in promoting leaf adaxial cell fates has been well characterized in Arabidopsis thaliana, the molecular mechanisms conferring freshy fruit development as a spatial–temporal expression gene in tomato pericarp remain unclear. In the present study, we verified the transcription of SlAS2 and SlAS2L, two homologs of AS2, in the pericarp during early fruit development. Disruption of SlAS2 or SlAS2L caused a significant decrease in pericarp thickness as a result of a reduction in the number of pericarp cell layers and cell area, leading to smaller tomato fruit size, which revealed their critical roles in tomato fruit development. In addition, leaves and stamens exhibited severe morphological defects in slas2 and slas2l single mutants, as well as in the double mutants. These results demonstrated the redundant and pleiotropic functions of SlAS2 and SlAS2L in tomato fruit development. Yeast two‐hybrid and split‐luciferase complementation assays showed that both SlAS2 and SlAS2L physically interact with SlAS1. Molecular analyses further indicated that SlAS2 and SlAS2L regulate various downstream genes in leaf and fruit development, and that some genes participating in the regulation of cell division and cell differentiation in the tomato pericarp are affected by these genes. Our findings demonstrate that SlAS2 and SlAS2L are vital transcription factors required for tomato fruit development. Significance Statement: Understanding early fruit development in tomato is critical since the size and shape of a fruit is largely decided in the early stages. Our previous work identified hundreds of transcription factors specifically expressed in the pericarp of 0–5 days post anthesis fruits in tomato that are likely involved in this complex process. ASYMMETRIC LEAVES 2 (AS2) belongs to the LATERAL ORGAN BOUNDARIES family, and its role in promoting leaf adaxial cell fates in Arabidopsis has been well characterized. SlAS2 and SlAS2L, two homologs of AS2 in tomato, are specifically expressed in the ovary wall/pericarp at early fruit developmental stage, but their functions in fleshy fruit development are still unclear. In the present study, we found that knockout mutations in SlAS2 or SlAS2L caused significantly reduced pericarp thickness, leading to smaller mature fruit size. Histocytological observation of the pericarp revealed that disruption of SlAS2 or SlAS2L inhibited both cell division and expansion, leading to the observed thinner pericarp. Additionally, leaf and stamen defects in single and double mutants suggested that SlAS2 and SlAS2L have redundant functions in plant development. Both SlAS2 and SlAS2L interact with SlAS1 in vitro, which is consistent with Arabidopsis AS2. Molecular analyses revealed that genes regulated by SlAS2 and SlAS2L in leaf and fruit development are different, and were related to cell division and differentiation processes during tomato pericarp development. Our results confirm the vital roles of SlAS2 and SlAS2L in regulating tomato fruit development. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982-2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas. [ABSTRACT FROM AUTHOR]

Glandular trichomes (GTs), specialized structures formed by the differentiation of plant epidermal cells, are known to play important roles in the resistance of plants to external biotic and abiotic stresses. These structures are capable of storing and secreting secondary metabolites, which often have important agricultural and medicinal values. In order to better understand the molecular developmental mechanisms of GTs, studies have been conducted in a variety of crops, including tomato (Solanum lycopersicum), sweetworm (Artemisia annua), and cotton (Gossypium hirsutum). The MYC transcription factor of the basic helix-loop-helix (bHLH) transcription factor family has been found to play an important role in GT development. In this study, a total of 13 cucumber MYC transcription factors were identified in the cucumber (Cucumis sativus L.) genome. After performing phylogenetic analyses and conserved motifs on the 13 CsMYCs in comparison to previously reported MYC transcription factors that regulate trichome development, seven candidate MYC transcription factors were selected. Through virus-induced gene silencing (VIGS), CsMYC2 is found to negatively regulate GT formation while CsMYC4, CsMYC5, CsMYC6, CsMYC7, and CsMYC8 are found to positively regulate GT formation. Furthermore, the two master effector genes, CsMYC2 and CsMYC7, are observed to have similar expression patterns indicating that they co-regulate the balance of GT development in an antagonistic way. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a crucial parameter that reflects land–atmosphere interaction and has thus attracted wide interest from geoscientists. Owing to the rapid development of Earth observation technologies, remotely sensed LST is playing an increasingly essential role in various fields. This review aims to summarize the progress in LST estimation algorithms and accelerate its further applications. Thus, we briefly review the most‐used thermal infrared (TIR) LST estimation algorithms. More importantly, this review provides a comprehensive collection of the widely used TIR‐based LST products and offers important insights into the uncertainties in these products with respect to different land cover conditions via a systematic intercomparison analysis of several representative products. In addition to the discussion on product accuracy, we address problems related to the spatial discontinuity, spatiotemporal incomparability, and short time span of current LST products by introducing the most effective methods. With the aim of overcoming these challenges in available LST products, much progress has been made in developing spatiotemporal seamless LST data, which significantly promotes the successful applications of these products in the field of surface evapotranspiration and soil moisture estimation, agriculture drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Overall, this review encompasses the most recent advances in TIR‐based LST and the state‐of‐the‐art of applications of LST products at various spatial and temporal scales, identifies critical further research needs and directions to advance and optimize retrieval methods, and promotes the application of LST to improve the understanding of surface thermal dynamics and exchanges. Plain Language Summary: Land surface temperature (LST) is a crucial geophysical parameter related to surface energy and water balance of the land‐atmosphere system. Satellite remote sensing provides the best way to measure LST and generate various LST products at regional and global scales. In this review, to facilitate the application of LST products in different fields, we first present the physical meaning of satellite‐derived LST. Subsequently, we summarize recent advances in LST retrieval and validation methods, with a special focus on the state‐of‐the‐art product collections, product accuracies and intercomparisons, and main problems in current LST products as well as their possible solutions. Additionally, we also review the major applications of LST products in agricultural drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Finally, we offer recommendations or perspectives to promote LST retrieval methods and their applications. This review will aid the user in gaining a thorough comprehensive understanding of satellite‐derived LST products and promoting their appropriate applications. Key Points: State‐of‐the‐art satellite‐derived land surface temperature (LST) product levels, sources, uncertainties, and differences are providedTypical applications of LST products in various fields are summarizedFuture directions for the generation and applications of LST products are recommended [ABSTRACT FROM AUTHOR]

Fruit glossiness is an important external fruit quality trait that greatly affects the marketability of fresh cucumber (Cucumis sativus) fruits. A few reports have suggested that the extent of cuticular wax loading influences the glossiness of the fruit surface. In the present study, we tested the wax contents of two inbred cucumber lines, comparing a line with waxy fruit (3401) and a line with glossy fruit (3413). Wax content analysis and dewaxing analysis demonstrate that fruit cuticular wax loads negatively correlate with fruit glossiness in cucumber. Identifying genes that were differentially expressed in fruit pericarps between 3401 and 3413 and genes induced by abscisic acid suggested that the wax biosynthesis gene CsCER6 (Cucumis sativus ECERIFERUM 6) and the regulatory gene CsCER7 may affect wax accumulation on cucumber fruit. Expression analysis via RT-qPCR, GUS-staining, and in situ hybridization revealed that CsCER6 and CsCER7 are abundantly expressed in the epidermis cells in cucumber fruits. Furthermore, the overexpression and RNAi lines of CsCER6 and CsCER7 showed dramatic effects on fruit cuticular wax contents, fruit glossiness, and cuticle permeability. Our results suggest that CsCER6 and CsCER7 positively regulate fruit cuticular wax accumulation and negatively influence fruit glossiness. [ABSTRACT FROM AUTHOR]

Summary: Fusarium wilt disease, caused by Fusarium oxysporum f. sp. cucumerinum (Foc), leads to widespread yield loss and quality decline in cucumber. However, the molecular mechanisms underlying Foc resistance remain poorly understood.We report the mapping and functional characterisation of CsChi23, encoding a cucumber class I chitinase with antifungal properties. We assessed sequence variations at CsChi23 and the associated defence response against Foc. We functionally characterised CsChi23 using transgenic assay and expression analysis. The mechanism regulating CsChi23 expression was assessed by genetic and molecular approaches.CsChi23 was induced by Foc infection, which led to rapid upregulation in resistant cucumber lines. Overexpressing CsChi23 enhanced fusarium wilt resistance and reduced fungal biomass accumulation, whereas silencing CsChi23 causes loss of resistance. CsHB15, a homeodomain leucine zipper (HD‐Zip) III transcription factor, was found to bind to the CsChi23 promoter region and activate its expression. Furthermore, silencing of CsHB15 reduces CsChi23 expression. A single‐nucleotide polymorphism variation −400 bp upstream of CsChi23 abolished the HD‐Zip III binding site in a susceptible cucumber line.Collectively, our study indicates that CsChi23 is sufficient to enhance fusarium wilt resistance and reveals a novel function of an HD‐Zip III transcription factor in regulating chitinase expression in cucumber defence against fusarium wilt. [ABSTRACT FROM AUTHOR]

To explore the effect of the introduction of the amino and substituted amino groups on the antitumor activity of harmine, twenty-five novel 6-amino substituted harmine derivatives (3a–3j and 5a–5o) were synthesized and evaluated for anti-proliferative activity on a panel of cancer cell lines. Compounds 3i and 5n exhibited the most potent antiproliferative activity with IC 50 values lower than 2.2 μM. Especially, compound 5n possessed extremely potent antitumor activity with IC 50 values of 0.34 μM and 0.65 μM against HL-60 and A549 cell lines, respectively. Further, the preliminary studies of mechanisms showed that compound 5n could significantly induce cell apoptosis in a dose-dependent manner, cause cell cycle arrest at the G2/M phase and intercalate into ct-DNA via the competition with EB, while displaying very weak topoisomerase I (Topo I) inhibition activity. More importantly, 5n showed mild cytotoxicity against human normal lung epithelial cells BEAS-2B. Based on these considerations, 5n may be a good antitumor candidate compound for further exploration. Twenty-five novel 6-amino substituted harmine derivatives were synthesized and evaluated for anti-proliferative activities on a panel of cancer cell lines, apoptosis induction, cell cycle arrest and DNA intercalation effects. [Display omitted] • Twenty-five novel 6-amino substituted harmine derivatives were synthesized and evaluated for antitumor activity. • All synthesized compounds further supplemented the SARs. • Compound 5n possessed the potent antitumor activity with IC 50 values of 0.34 μM and 0.65 μM against HL-60 and A549. • Compound 5n could significantly induce cell apoptosis, cause cell cycle arrest, and intercalate into ct-DNA. [ABSTRACT FROM AUTHOR]

YABBY transcription factors play important roles in plant growth and development. However, little is known about YABBY genes in Cucurbitaceae. Here, we identified 59 YABBY genes from eight cucurbit species, including cucumber (C. sativus L.), melon (C. melon L.), watermelon (C. lanatus), wax gourd (B. hispida), pumpkin (C. maxima), zucchini (C. pepo L.), silver-seed gourd (C. argyrosperma), and bottle gourd (L. siceraria). The 59 YABBY genes were clustered into five subfamilies wherein the gene structures and motifs are conserved, suggesting similar functions within each subfamily. Different YABBY gene numbers in eight cucurbit species indicated that gene loss or duplication events exist in an evolutionary process across Cucurbitaceae. The cis-acting elements analysis implied that the YABBYs may be involved in plant development, and phytohormone, stress, and light responses. Importantly, YABBY genes exhibited organ-specific patterns in expression in cucumber. Furthermore, a gene CsaV3_6G038650 was constitutively expressed at higher levels at different fruit development stages and might play a crucial role in cucumber fruit development. Collectively, our work will provide a better understanding for further function identifications of YABBY genes in Cucurbitaceae. [ABSTRACT FROM AUTHOR]

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas using in situ LST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K. [ABSTRACT FROM AUTHOR]

Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features around 760, 940, 1140, and 2060 nm without making use of external atmospheric or surface parameters. A sensitivity analysis based on synthetic data demonstrated that, for each of the two methods, the root-mean-square errors to be expected were less than 0.18 nm for the retrieval of channel wavelength center and less than 0.8 nm for channel full-width at half-maximum. The application of the proposed methods to a real Hyperion data set showed quite-similar cross-track variations in the spectral calibration for the two methods, although relatively large differences in magnitude were found near the 940- and 1140-nm \H2\O absorption features. The significant improvement of the reflectance spectra derived after the refinement of the instrument spectral calibration confirms the good performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Summary: The fruit trichomes of Cucurbitaceae are widely desired in many Asian countries and have been a key determinant of cucumber (Cucumis sativus L.) cultivar selection for commercial production and breeding. However, our understanding of the initiation and development of cucumber trichomes is still limited. Here, we found that the cucumber TINY BRANCHED HAIR (TBH) gene is preferentially expressed in multicellular trichomes. Overexpression of CsTBH in tbh mutants restored the trichome phenotype and increased the percentage of female flowers, whereas silencing of CsTBH in wild‐type plants resulted in stunted trichomes with a lower rate of female flowers. Furthermore, we provide evidence that CsTBH can directly bind to the promoters of cucumber 1‐Aminocyclopropane‐1‐Carboxylate Synthase (CsACS) genes and regulate their expression, which affects multicellular trichome development, ethylene accumulation, and sex expression. Two cucumber acs mutants with different trichome morphology and sex morphs compared with their near‐isogenic line further support our findings. Collectively, our study provides new information on the molecular mechanism of CsTBH in regulating multicellular trichome development and sex expression through an ethylene pathway. Significance Statement: We explored the role of CsTBH in cucumber (Cucumis sativus L.) trichome development by directly activating CsACS genes, providing new information on the molecular mechanism of CsTBH functioning in multicellular trichome development and sex expression through an ethylene pathway. [ABSTRACT FROM AUTHOR]

Cucumber is dioecious by nature, having both male and female flowers, and is a model system for unisexual flower development. Knowledge related to male flowering is limited, but it is reported to be regulated by transcription factors and hormone signals. Here, we report functional characterization of the cucumber (Cucumis sativus) GL2-LIKE gene, which encodes a homeodomain leucine zipper (HD-ZIP) IV transcription factor that plays an important role in regulating male flower development. Spatial–temporal expression analyses revealed high-level expression of CsGL2-LIKE in the male flower buds and anthers. CsGL2-LIKE is closely related to AtGL2, which is known to play a key role in trichome development. However, ectopic expression of CsGL2-LIKE in Arabidopsis gl2-8 mutant was unable to rescue the gl2-8 phenotype. Interestingly, the silencing of CsGL2-LIKE delayed male flowering by inhibiting the expression of the florigen gene FT and reduced pollen vigor and seed viability. Protein–protein interaction assays showed that CsGL2-LIKE interacts with the jasmonate ZIM domain protein CsJAZ1 to form a HD-ZIP IV–CsJAZ1 complex. Collectively, our study indicates that CsGL2-LIKE regulates male flowering in cucumber, and reveals a novel function of a HD-ZIP IV transcription factor in regulating male flower development of cucumber. [ABSTRACT FROM AUTHOR]

The protection of water resources is of paramount importance to human beings' practical lives. Monitoring and improving water quality nowadays has become an important topic. In this study, a novel Bayesian probabilistic neural network (BPNN) improved from ordinary Bayesian probability methods has been developed to quantitatively predict water quality parameters including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a. The proposed method, based on conventional Bayesian probability methods, involves feature engineering and deep neural networks. Additionally, it extracts significant information for each endmember from combinations of spectra by feature extraction, with spectral unmixing based on mathematical and statistical analysis, and calculates each of the water quality parameters. The experimental results show the great performance of the proposed model with all coefficient of determination R 2 over 0.9 greater than the values (0.6–0.8) from conventional methods, which are greater than ordinary Bayesian probability analysis. The mean percent of absolute error (MPAE) is taken into account as an important statistical criterion to evaluate model performance, and our results show that MPAE ranges from 4% (nitrogen) to 10% (COD). The root mean squared errors (RMSEs) of phosphorus, nitrogen, COD, BOD, and chlorophyll-a (Chla) are 0.03 mg/L, 0.28 mg/L, 3.28 mg/L, 0.49 mg/L, and 0.75 μg/L, respectively. In comparison with other deep learning methods, this study takes a relatively small amount of data as training data to train the proposed model and the proposed model is then tested on the same amount of testing data, achieving a greater performance. Thus, the proposed method is time-saving and more effective. This study proposes a more compatible and effective method to assist with decomposing combinations of hyperspectral signatures in order to calculate the content level of each water quality parameter. Moreover, the proposed method is practically applied to hyperspectral image data on board an unmanned aerial vehicle in order to monitor the water quality on a large scale and trace the location of pollution sources in the Maozhou River, Guangdong Province of China, obtaining well-explained and significant results. [ABSTRACT FROM AUTHOR]

Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neural networks (ANNs) using hyperspectral remote sensing and ground-measured water quality data is proposed to quantitatively predict water quality parameters, including phosphorus, nitrogen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and chlorophyll a. Seventy-nine ground measured data samples are used as training data in the establishment of the proposed model, and 30 samples are used as testing data. The proposed method based on traditional ANNs of numerical prediction involves feature selection of bands, self-adapting selection based on multiple selection criteria, stepwise backtracking, and combined weighted correlation. Water quality parameters are estimated with coefficient of determination R 2 ranging from 0.93 (phosphorus) to 0.98 (nitrogen), which is higher than the value (0.7 to 0.8) obtained by traditional ANNs. MPAE (mean percent of absolute error) values ranging from 5% to 11% are used rather than root mean square error to evaluate the predicting precision of the proposed model because the magnitude of each water quality parameter considerably differs, thereby providing reasonable and interpretable results. Compared with other ANNs with backpropagation, this study proposes an auto-adapting method assisted by the above-mentioned methods to select the best model with all settings, such as the number of hidden layers, number of neurons in each hidden layer, choice of optimizer, and activation function. Different settings for ANNS with backpropagation are important to improve precision and compatibility for different data. Furthermore, the proposed method is applied to hyperspectral remote sensing images collected using an unmanned aerial vehicle for monitoring the water quality in the Shiqi River, Zhongshan City, Guangdong Province, China. Obtained results indicate the locations of pollution sources. [ABSTRACT FROM AUTHOR]

One focus of remote-sensing studies is obtaining highly accurate land-cover maps, which is essential for quantifying and monitoring changes in the environment. However, thermal data, which can provide auxiliary information, is often ignored in land-cover classification. In this study we compare the performance of different remote-sensing feature combinations with and without the Landsat 8 thermal band (band 10). The results show that overall the thermal feature had a positive effect on mapping land cover. A combination of spectral features, indices and the thermal feature maximized the improvement in accuracy. The proposed classifier was applied to map land cover in an area in Egypt. The thermal feature significantly reduced the confusion between cropland and wetland. The improvement in accuracy obtained by adding the thermal feature was analysed in a time series spanning 1 year. We found that the thermal feature improved the classification accuracy when temperature variations occurred among the different land-cover types. The effect of the thermal feature was also influenced by the land cover; in cloudless conditions, warmer weather can enhance the accuracy improvement of the thermal feature. [ABSTRACT FROM AUTHOR]

Summary: The fruit epidermal features such as the size of tubercules are important fruit quality traits for cucumber production. But the mechanisms underlying tubercule formation remain elusive. Here, tubercule size locus CsTS1 was identified by map‐based cloning and was found to encode an oleosin protein. Allelic variation was identified in the promoter region of CsTS1, resulting in low expression of CsTS1 in all 22 different small‐warty or nonwarty cucumber lines. High CsTS1 expression levels were closely correlated with increased fruit tubercule size among 44 different cucumber lines. Transgenic complementation and RNAi‐mediated gene silencing of CsTS1 in transgenic cucumber plants demonstrated that CsTS1 positively regulates the development of tubercules. CsTS1 is highly expressed in the peel at fruit tubercule forming and enlargement stage. Auxin content and expression of three auxin signalling pathway genes were altered in the 35S:CsTS1 and CsTS1‐RNAi fruit tubercules, a result that was supported by comparing the cell size of the control and transgenic fruit tubercules. CsTu, a C2H2 zinc finger domain transcription factor that regulates tubercule initiation, binds directly to the CsTS1 promoter and promotes its expression. Taken together, our results reveal a novel mechanism in which the CsTu‐TS1 complex promotes fruit tubercule formation in cucumber. [ABSTRACT FROM AUTHOR]

A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors. [ABSTRACT FROM AUTHOR]

Studying wheat phenology can greatly enhance our understanding of how wheat growth responds to climate change, and guide us to reasonably confront its influence. However, comprehensive global-scale wheat phenology–climate analysis is still lacking. In this study, we extracted the wheat harvest date (WHD) from 1981–2014 from satellite data using threshold-, logistic-, and shape-based methods. Then, we analyzed the effects of heat and drought stress on WHD based on gridded daily temperature and monthly drought data (the Palmer drought severity index (PDSI) and the standardized precipitation evapotranspiration index (SPEI)) over global wheat-growing areas. The results show that WHD was generally delayed from the low to mid latitudes. With respect to variation trends, we detected a significant advancement of WHD in 32.1% of the world's wheat-growing areas since 1981, with an average changing rate of −0.25 days/yr. A significant negative correlation was identified between WHD and the prior three months' normal-growing-degree-days across 50.4% of the study region, which implies that greater preseason effective temperature accumulation may cause WHD to occur earlier. Meanwhile, WHD was also found to be significantly and negatively correlated with the prior three months' extreme-growing-degree-days across only 9.6% of the study region (mainly located in northern South Asia and north Central-West Asia). The effects of extreme heat stress were weaker than those of normal thermal conditions. When extreme drought (measured by PDSI/SPEI) occurred in the current month, in the month prior to WHD, and in the second month prior to WHD, it forced WHD to advance by about 9.0/8.1 days, 13.8/12.2 days, and 10.8/5.3 days compared to normal conditions, respectively. In conclusion, we highlight the effects that heat and drought stress have on advancing wheat harvest timing, which should be a research focus under future climate change. [ABSTRACT FROM AUTHOR]

Crop growth in different periods influences the final yield. This study started from the agronomic mechanism of yield formation and aimed to extract useful spectral characteristics in different phenological phases, which could directly describe the final yield and dynamic contributions of different phases to the yield formation. Hyperspectral information of the winter wheat canopy was acquired during three important phases (jointing stage, heading stage, and grain-filling stage). An enhanced 2D correlation spectral analysis method modified by mutual information was proposed to identify the sensitive wavebands. The selected wavebands performed well with good mechanism interpretation and close correlation with important crop growth parameters and main physiological activities related to yield formation. The quantitative contribution proportions of plant growth in three phases to the final yield were estimated by determining the coefficients of partial least square models based on full spectral information. They were then used as single-phase weight factors to merge the selected wavebands. The support vector machine model based on the weighted spectral dataset performed well in yield prediction with satisfactory accuracy and robustness. This result would provide rapid and accurate guidance for agricultural production and would be valuable for the processing of hyperspectral remote sensing data. [ABSTRACT FROM AUTHOR]

Accurate and efficient in situ measurement methods of leaf area index (LAI) and leaf angle distribution (LAD) are needed to estimate the fluxes of water and energy in agricultural settings. However, available methods: to estimate these two parameters, especially LAD, are limited. In this study, we propose a field measurement method using multi-angular digital images to estimate LAI and LAD simultaneously from the area proportions of: (i) sunlit soil; (ii) sunlit leaves; (iii) shaded soil; and (iv) shaded leaves. A new expression of the fraction of sunlit leaves is developed based on the radiative transfer theory. Coupling the measured and modeled fractions with an optimization scheme, LAI and the LAD parameters are derived from inverting a fractional model of sunlit and shaded leaves and soil. Through four tests using simulated scenes and in situ measurements for row crops, it is determined that our method performs well. The absolute error of LAI estimation is less than 0.1 when LAI is low (i.e., <1.2), and the absolute deviations of LAI estimates are approximately 0.5 when the reference LAI is 3.5. The estimation errors of LAI and the G function (a representative of LAD which quantifies the projection of unit foliage area) for in situ measurements are respectively less than 0.2 and 0.06 in general. In addition, the accuracy of estimation is even higher when leaves are simulated as randomly distributed disks or observations from multiple azimuth planes are used. One of the most interesting features of this method is its ability to estimate reasonable LAD directly from the fractions of sunlit and shaded leaves, even when LAI is high (i.e., >3), so little background soil is seen. The sensitivity and uncertainty analysis is consistent with the estimation errors. Theoretically, the application of this method is not limited to row crops or to field measurement, as the derived formulae of sunlit and shaded components can be used for other types of vegetation by introducing the clumping index and can be used in the modeling of canopy vegetation parameters (e.g., canopy reflectance). [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of wheat production at a regional scale is crucial for national food security and sustainable agricultural development. This study developed a new gross primary productivity (GPP) estimation model (denoted as the [ACPM]), based on the effects of light, heat, soil moisture, and nitrogen content (N) on the light-use efficiency of winter wheat. The ACPM model used the quantic additivity of the environmental factors to improve the minimum form or multiple multiplication form in the previous model and thus characterized the joint effects of heat, soil moisture, and N on crop photosynthesis performance. The key parameters (i.e., light) were determined from the photosynthetically active radiation product of the Himawari-8 sensor and the fraction of photosynthetically active radiation product of Moderate Resolution Imaging Spectroradiometer (MODIS). The heat was determined from the land temperature products of MODIS. The soil moisture was obtained from the inversion using a visible and shortwave infrared drought index (VSDI), whereas the N stress of winter wheat was detected using the newly developed modified ratio vegetation index (MRVI), which could accurately obtain the spatiotemporal distribution of the leaf chlorophyll content of winter wheat. The ACPM and two other previous models (named the GPP1 and GPP2 models) were applied on the Himawari-8 and MODIS images in Hengshui City. The evaluation results, based on the ground measurement, indicated that the ACPM models exhibited the best estimate of dry aboveground biomass (DAM) and the wheat yield in Hengshui City, with errors of <10% and <12% for the DAM and yield, respectively. Considering the easy operation of the ACPM model and the accessibility of the corresponding satellite images, the Agriculture Crop Photosynthesis Model (ACPM) can be expected to provide information on the winter wheat shortfalls and surplus ahead of the availability of official statistical data. [ABSTRACT FROM AUTHOR]

• A chiral approach has been accomplished for the total synthesis of Tasimelteon. • Tasimelteon is effective for treatment of non-24-h sleep-wake disorder. • The synthesis was accomplished in four steps in overall 48% yield. • This synthetic method is beneficial to industrial production. A simple and efficient approach has been accomplished for the total synthesis of active pharmaceutical ingredient (API) of Tasimelteon which is effective for treatment of non-24-h sleep-wake disorder. Starting from 4-vinyl-2, 3-dihydrobenzofuran, the synthesis involves asymmetric epoxidation, Wittig reaction, reduction of the nitrile group and propionation of the amine. The synthesis was accomplished in four steps in overall 48% yield. [ABSTRACT FROM AUTHOR]

Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one. [ABSTRACT FROM AUTHOR]

Gibberellins are phytohormones with many roles, including the regulation of fruit development. However, little is known about the relationship between GA perception and fleshy fruit ontogeny, and particularly locule formation., We characterized the expression of cucumber ( Cucumis sativus) GA receptor gene ( CsGID1a) using quantitative real-time PCR, in situ hybridization and a promoter::β-glucuronidase (GUS) assay. CsGID1a-RNAi cucumber fruits were observed by dissecting microscope, scanning electron microscopy and transmission electron microscopy. Finally, genome-wide gene expression in young fruits from a control and the RNAi line was compared using a digital gene expression (DGE) analysis approach., The expression pattern of CsGID1a was found to be closely correlated with fruit locule formation, and silencing CsGID1a in cucumber resulted in fruits with abnormal carpels and locules. Overexpression of CsGID1a in the Arabidopsis thaliana double mutant ( gid1a gid1c) resulted in 'cucumber locule-like' fruits. The DGE analysis suggested that expression of genes related to auxin synthesis and transport, as well as the cell cycle, was altered in CsGID1a-RNAi fruits, a result that was supported by comparing the auxin content and cellular structures of the control and transgenic fruits., This study demonstrates a previously uncharacterized GA signaling pathway that is essential for cucumber fruit locule formation. [ABSTRACT FROM AUTHOR]