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The leaf inclination angle (LIA), defined as the leaf or needle inclination angle to the horizontal plane, is vital in radiative transfer, precipitation interception, evapotranspiration, photosynthesis, and hydrological processes. This paper reviews the field and remote sensing methods to determine LIA. In the field, LIA is determined using direct and indirect methods. The direct methods include direct contact, photographic, and light detection and ranging (LiDAR) methods, while the indirect methods are composed of the gap fraction, four-component, and polarization measurement methods. The direct methods can obtain LIA accurately at individual leaves, crown, and plot scales, whereas the indirect methods work well for crops at the plot level. The remote sensing methods to estimate LIA are mainly based on the empirical, radiative transfer model, and gap fraction methods. More advanced inversion strategies and validation studies are necessary to improve the robustness of LIA remote sensing estimation. In future studies, automated observation systems can be developed and the LIA measurement can be incorporated into existing ground observation networks to enhance spatial coverage. [ABSTRACT FROM AUTHOR]

Canopy clumping index (CI) is a key structural parameter related to vegetation phenology and the absorption of radiation, and it is usually retrieved from remote sensing data based on an empirical relationship with the Normalized Difference between Hotspot and Darkspot (NDHD) index. A rapid production software was developed to implement the CI algorithm based on the Google Earth Engine (GEE) to update current CI products and promote the application of CI in different fields. Daily, monthly, and yearly global CI products are continuously generated and updated in real-time by the software. Users can directly download the product or work with CI without paying attention to data generation. For the application case study, a change detection algorithm, LandTrendr, was implemented on the GEE to examine the global CI trend from 2000 to 2020. The results indicate that the area of increase trend (28.7%, ΔCI > 0.02) is greater than that of the decrease trend (17.1%, ΔCI < −0.02). Our work contributes toward the retrieval, application, and validation of CI. [ABSTRACT FROM AUTHOR]

The photon recollision probability (p) and spectral invariant theory (p -theory) have gained much attention in the vegetation remote sensing community over the past decades. A broad range of studies have been performed involving the derivation of theoretical principles, determination of the p value in field measurements and remote sensing studies, and application of the theory in various areas. This review synthesizes current developments in this theory and discusses future directions. The review begins with a brief introduction of the historical background of the p -theory, followed by a description of the general principles of the theory. Subsequently, various methods for estimating the p value in field measurements and remote sensing studies are reviewed. Next, applications of the p -theory in canopy reflectance modeling and vegetation parameter retrieval are illustrated. Finally, the general features of the theory and future research directions are discussed. This study aims to enhance our understanding of the p -theory, its development and applications in various disciplines. • The historical context of the spectral invariant theory (p -theory) is introduced. • The general principles of the p -theory are described. • The methods to determine the spectral invariant values are reviewed. • Applications of the p -theory in modeling and parameter retrieval are presented. [ABSTRACT FROM AUTHOR]

Forest leaf area index (LAI) and clumping index (CI) are critical structural parameters to study the biomass, biogeochemical cycles, ecological simulation, and radiation transfer in a forest system. The ground-based measurements by traditional methods are costly, time-consuming, and are sensitive to variations in solar illumination conditions. Global Positioning System (GPS) satellites transfer L-band signals, and the presence of the forest canopy leads to an increase in noise. The GPS satellite's signal strength is generally described by the carrier-to-noise-density ratio (C/N0). This study explores the potential of using the GPS satellite C/N0 to estimate forest canopy structural parameters. We performed experiments at 16 forest plots in northeastern China. LAI, CI, and canopy closure were estimated by digital hemispherical photography (DHP) using the CAN_EYE software. The GPS satellite C/N0 was recorded with a modified smartphone application, GPSTest. The relationship between the forest canopy structural parameters and satellite C/N0 was analyzed for those observations with satellite elevation angle (SEA) > 30°. The results show that both the true LAI and the effective LAI have strong linear and polynomial correlation with C/N0 and the coefficient of determination (R2) is larger than 0.7. CI is linearly related to C/N0(R2 = 0.56). The linear relationship between the canopy closure and C/N0 is much lower (R2= 0.28). Accounting for the SEA effect, LAI can be estimated with R2 > 0.8, and CI with R2 = 0.6. LAI and CI can be well estimated from C/N0 when the SEA effect is accounted for. In general, the GPS C/N0 provides an alternative method to estimate forest canopy structural parameters. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI. Key Points: LAI, one half the total leaf area per unit surface area, is a fundamental vegetation attribute and an essential climate variableThe paper gives an overview of LAI field and remote sensing estimation methods, and LAI product validation, uncertainties, and applicationsGaps in current studies and new frontiers are analyzed; recommendations for future LAI estimations and validations are given [ABSTRACT FROM AUTHOR]

In the past few decades, SiC and Si have been widely researched via different methods due to their unique electrical conductivity, stable physical-chemical performance and good wear-resisting performance. In this paper, we firstly used diatomite as silica source and carbon powders as carbon source to synthesize mesoporous Si/SiC via magnesiothermic reduction reaction at relative low temperature (650 °C). All samples were characterized by XRD, SEM, TEM and N 2 adsorption-desorption. The XRD patterns and TEM images confirmed that diatomite was fully transformed into Si and SiC. N 2 adsorption-desorption isotherms shown that Si/SiC exhibited mesoporous structure. Brunauer-Emmett-Teller (BET) gas adsorption data showed that Si/SiC had a specific surface area of 69.09 m2/g and an average pore size about 10.82 nm. The results confirmed that diatomite can fully transformed into Si/SiC. The possible chemical reactions of magnesiothermic synthesis of mesoporous Si/SiC is also discussed. The schematic illustrating the synthesis process of Si/SiC powders. Image 1 [ABSTRACT FROM AUTHOR]

Consumer characteristics can help energy utilities implement efficient demand response programs and personalized services. However, there are two problems in obtaining consumer characteristics from smart meter data: label scarcity and class imbalance. The two problems greatly reduce the effectiveness of the existing supervised learning classifiers. Therefore, this paper proposes a self-training convolutional autoencoder (STCAE) framework for consumer characteristics identification with imbalance datasets. STCAE has three modules including encoder, decoder, and classifier. For the label scarcity problem, a seasonal pretext is designed to enable learning unlabeled samples through encoder–decoder structure of STCAE. For the class imbalance problem, a piecewise synthetic minority over-sampling technique (PSMOTE) method is designed for class rebalancing. Case studies and comparison studies demonstrate the validity of each major component and the superiority of STCAE over existing methods. In comparison studies of twelve consumer characteristics identification, the average improvements of accuracy and Matthews correlation coefficient are higher than 6.32% and 35.18%, respectively. Furthermore, in the ablation studies, STCAE performs better than the framework with different components in most consumer characteristics. [ABSTRACT FROM AUTHOR]

• A new intermittent electricity theft attack is presented by repeating normal and fraudulent behaviors alternately. • Electricity feature engineering is conducted on the automatically divided time intervals. • A machine learning-based detection framework is proposed to detecting the intermittent electricity theft attack. • VBGMM is used to overcome the difficulty of parameter selection of detection threshold. The widespread installation of advanced metering infrastructure (AMI) brings convenience to applications including but not limited to load management and demand response. However, AMI is also at risk of electricity theft and non-technical loss. Using the smart meter data provided by AMI to dig out user electricity consumption behavior is an effective way to construct electricity theft detectors. In this paper, a new intermittent electricity theft attack behavior is presented which switches between committing electricity theft and honestly consuming electricity alternately to skillfully evade the existing detectors. Based on the assumption that the labels of intermittent adversaries are unavailable, a new machine learning-based detection framework is proposed to detect this attack. Initially electricity features are constructed based on time intervals divided by a numerical iteration method. Then light gradient boosting method (LightGBM) is used to classify the normal users and adversaries. Further, the disperse degree of users is designed for capturing the differences between the intermittent adversaries and others. A new 2D label set is then constructed by combining the predicted labels of LightGBM and the disperse degree. Finally, a variational Bayesian Gaussian mixture model is employed based on the 2D label set to sort the users into the normal users and adversaries visually. Results of the case studies show that the presented attack can evade state-of-the-art detectors but still gain high profits. In addition, the proposed machine learning-based detector outperforms state-of-the-art detectors on both persistent attacks and intermittent attacks. [ABSTRACT FROM AUTHOR]

Graphical abstract A sample downward DHP image (a) and the gap fraction (b) over plot C on July 12, 2012 (DOY 194). Panels (c), (d), and (e) represent the Ω A (θ , ϕ) values for each 10°×10° cell, Ω A (θ) for different rings, and Ω A (ϕ) for different azimuth sectors, respectively. Panel (f) indicates the Ω A (ν) values calculated over different solid angles. Panels (c)–(f) are calculated over an elementary sampling unit (ESU) of 20 images. Highlights • Proposed a directional apparent clumping index (ACI) for different geometric units. • Estimated seasonal directional and whole ACIs from LAI-2200, DHP, and AccuPAR. • Compared the integration, averaging, non-linear correction, and VMR methods to estimate the whole ACI. • ACI broadens the current CI concept and can be estimated from optical instruments. Abstract Canopy clumping index (CI) indicates the non-random distribution of foliage components in space, and is an important structural parameter for better understanding the radiative transfer process in a canopy. The apparent clumping index (ACI), calculated using the logarithmic gap fraction averaging method, is reported by the LAI-2200 Plant Canopy Analyzer. While LAI-2200 calculates the gap fraction and ACI from different conical rings, calculation of ACI for other geometric units (e.g., an angular cell or an azimuth sector) and instruments has been lacked. Building upon the LAI-2200 ACI, this study compares the ACIs calculated for different geometric spaces from different optical instruments. The field data obtained from seasonal continuous measurements with LAI-2200, digital hemispheric photography (DHP), and AccuPAR at a paddy rice field in northeast China were used to calculate the directional ACIs at different levels—a directional cell (Ω A (θ , ϕ)), a concentric ring (Ω A (θ)), an azimuth sector (Ω A (ϕ)), and over the horizontal landscape (Ω A (ν)). The whole ACIs were calculated from the directional ACIs with an angular integration method, a simple averaging method, a non-linear correction method, and a variance-to-mean ratio method. The directional ACIs for paddy rice generally follow the order of Ω A (θ , ϕ) < Ω A (θ) and Ω A (ϕ) < Ω A (ν), displaying an increase of foliage randomness with the segment size. The Ω A (θ , ϕ) estimated from DHP indicates canopy clumping at the finest level and is consistent with the CIs estimated from the logarithmic averaging method (Ω LX) and the ratio method (the effective leaf area index (LAI e) divided by the LAI). The ACI metrics expand the current CI metrics and can be obtained with different optical instruments. The expanded metrics can be applied in the canopy radiative transfer modeling and in the estimation of canopy biophysical parameters for other vegetation ecosystems. [ABSTRACT FROM AUTHOR]

Professor Hongliang Fang supported the experiment design and data collection in 2015 and 2016. The corrected author contributions as follow: B **Conceptualization, T.S. and H.F.; methodology, T.S.; writing - original draft preparation, T.S.; writing - review and editing, L.C., H.F. and R.S.; funding acquisition, T.S.** b The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. After careful discussion and with the consensus of all existing authors, Professor Hongliang Fang should be added as a co-author. [Extracted from the article]

Automatic leaf area index (LAI) measurements are important for obtaining sufficient amounts of field data over an extended period of time. A seasonal field campaign was carried out to obtain continuous LAI measurements over maize, soybean, and sorghum fields in northeast China in 2016. Field LAI measurements were acquired with the automatic PASTIS-57 (PAI Autonomous System from Transmittance Instantaneous Sensed from 57°) instrument and two smartphone applications, PocketLAI and LAISmart. These measurements were compared with data obtained using the LAI-2200 Plant Canopy Analyzer, digital hemispherical photography (DHP), and destructive sampling measurements. The effective plant area index (PAI eff ) estimates from LAI-2200 and DHP are consistent over the season, with the overall relative errors (RE) of less than 5%. The PASTIS-57 data exhibit a small underestimation of the LAI-2200 and DHP values (RE < 20%). The relative errors for the LAISmart data are between −20% and −30%. PocketLAI significantly underestimates the LAI-2200 values (RE > 40%) and saturates at around PAI eff  = 3.5. The canopy clumping index (CI) exhibits an S-shaped seasonal variation that decreases with the increase of PAI eff during the vegetative growth stage but increases after this stage. PASTIS-57 shows great potential for obtaining continuous LAI measurements in agricultural crop fields, but the smartphone applications should be further examined before they can be used for research purposes. The data collected in this study are valuable for the validation of remote sensing products. [ABSTRACT FROM AUTHOR]

Water background significantly affects the reflectance of partially flooded vegetation. However, the flooded background effects on the reflectance anisotropy have not been fully understood. This paper explores the water background effects on the anisotropic reflectance of rice canopy through multi-angle measurements. Seasonal multi-angle reflectance was measured for different view zenith angles (VZA) (−60°–60° with 15° interval) in a flooded paddy rice site. Wavelengths of 550 nm, 660 nm, 850 nm and 1640 nm were extracted to show the canopy anisotropic characteristics. Two canopy anisotropic indices, the anisotropic factor (ANIF) and the anisotropy index (ANIX), were examined for both non-flooded and flooded canopies in key phenology phases. Results show that the reflectance anisotropy of rice canopy is significantly weakened by a shallow layer (≈10 cm) of water. For VZA > 15°, background water decreases the canopy reflectance by up to 28% and weakens the anisotropic properties when LAI < 4.0 and the fraction of vegetation cover (fCover) <80%. The background water shows barely no effect on canopy reflectance when LAI > 5.0. 1640 nm shows the strongest anisotropy when LAI < 2.0, whereas 660 nm shows more significant anisotropy when LAI > 3.0. The backward observations show stronger scattering than the forward observations for the same VZA. The data from this study can be used as input for biophysical models and as validation data for remote sensing products. [ABSTRACT FROM AUTHOR]

Clumping index (CI) describes the spatial distribution pattern of foliage, and is a critical parameter to characterize the terrestrial ecosystem and model land-surface processes. Global and regional scale CI maps have been generated from POLDER, MODIS, and MISR sensors based on an empirical relationship with the normalized difference between hotspot and darkspot (NDHD) index by previous studies. However, the hotspot, darkspot, and CI values can be considerably different from different bidirectional reflectance distribution function (BRDF) models and solar zenith angles (SZA). In this study, we evaluated the effects of different configurations of BRDF models and SZA values on CI estimation using the NDHD method. CI maps derived from MISR and MODIS were compared with reference data at the VALERI sites. Results show that for moderate to least clumped vegetation (CI > 0.5), CI values retrieved with the observational SZA agree well with field measurements, while SZA = 0° underestimates, and SZA = 60° overestimates. For highly clumped (CI < 0.5) and sparsely vegetated areas (FCOVER < 25%), the Ross-Li model with 60° SZA is recommended for CI estimation. The best NDHD configuration was used to estimate a 15-year time series CI from MODIS BRDF data. The time series CI shows a reasonable seasonal trajectory, and varies consistently with the MODIS leaf area index (LAI). This study enables better usage of the NDHD method for CI estimation, and can be a useful reference for research on CI validation. [ABSTRACT FROM AUTHOR]

Continuous field leaf area index (LAI) measurement has become increasingly important for the validation of remote sensing LAI products. A seasonal field campaign was carried out to take continuous LAI measurements over paddy rice fields in NE China in 2012. Three indirect optical methods, LAI-2200, digital hemispherical photography (DHP), and AccuPAR, were compared with a destructive sampling method conducted concurrently. Corrections for the clumping effect were applied to the effective plant area indices (PAI eff ) estimated from the indirect optical measurements. Both LAI-2200 and DHP produce consistent PAI eff estimates over the season ( R 2 = 0.76, RMSE = 0.97). The clumping index (CI) values obtained from DHP generally decrease with plant growth and range between 0.63 and 0.74 during the peak growing period from day of year (DOY) 191−230. The CI values retrieved from DHP photos generally decrease with increasing view angles. The optical PAI and LAI values estimated from LAI-2200 and DHP correspond very well with the destructive values before DOY 230 ( R 2 = 0.75, RMSE = 1.15 for PAI and R 2 = 0.78, RMSE = 0.74 for LAI), and the relative errors are less than 10% and 5%, respectively, for the two instruments. Omitting ring 5 for LAI-2200 generates very accurate PAI and LAI estimations during the peak season. Nevertheless, AccuPAR underestimates the PAI eff , PAI, and LAI values obtained from other methods (up to 30%). After DOY 231, the capability to detect PAI decreases significantly for both destructive and optical methods due to the leaf senescence and the DHP classification difficulty. In general, rice PAI could be accurately estimated with LAI-2200 and DHP before senescence if the clumping effect could be properly taken into account. The seasonal continuous LAI measurements obtained from this study are valuable for the validation of remote sensing LAI products. [ABSTRACT FROM AUTHOR]

Abstract: Graphene oxide (GO) immunotoxicity is not clarified well up to date. Herein we reported the effects of GOs with and without polyvinylpyrrolidone (PVP) coating on human immune cells such as dendritic cells (DCs), T lymphocytes and macrophages. Human immune cells such as dendritic cells (DCs), T lymphocytes and macrophages were isolated from health donated bloods, PVP-coating GO (PVP-GO) exhibited lower immunogenicity compared with pure GO on the aspect of inducing differentiation and maturation of dendritic cells (DCs), the levels of secreted TNF-α and IL-1β had no obvious difference between two groups, yet the secretion of IL-6 remained in PVP-coating GO group. In addition, PVP-coating GO delayed significantly the apoptotic process of T lymphocytes, at the same time, and exhibited anti-phagocytosis ability against macrophages and markedly enhanced the physiological activity of macrophages. In conclusion, PVP-coating GO possesses good immunological biocompatibility and immunoenhancement effects in vitro, and is likely to be an available candidate of immunoadjuvant in the future. [Copyright &y& Elsevier]

Abstract: Uncertainty information for global leaf area index (LAI) products is important for global modeling studies but usually difficult to systematically obtain at a global scale. Here, we present a new method that cross-validates existing global LAI products and produces consistent uncertainty information. The method is based on a triple collocation error model (TCEM) that assumes errors among LAI products are not correlated. Global monthly absolute and relative uncertainties, in 0.05° spatial resolutions, were generated for MODIS, CYCLOPES, and GLOBCARBON LAI products, with reasonable agreement in terms of spatial patterns and biome types. CYCLOPES shows the lowest absolute and relative uncertainties, followed by GLOBCARBON and MODIS. Grasses, crops, shrubs, and savannas usually have lower uncertainties than forests in association with the relatively larger forest LAI. With their densely vegetated canopies, tropical regions exhibit the highest absolute uncertainties but the lowest relative uncertainties, the latter of which tend to increase with higher latitudes. The estimated uncertainties of CYCLOPES generally meet the quality requirements (±0.5) proposed by the Global Climate Observing System (GCOS), whereas for MODIS and GLOBCARBON only non-forest biome types have met the requirement. Nevertheless, none of the products seems to be within a relative uncertainty requirements of 20%. Further independent validation and comparative studies are expected to provide a fair assessment of uncertainties derived from TCEM. Overall, the proposed TCEM is straightforward and could be automated for the systematic processing of real time remote sensing observations to provide theoretical uncertainty information for a wider range of land products. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to quantitatively validate the global MODIS and CYCLOPES leaf area index (LAI) products using a global LAI field measurement database created on the basis of a literature review and major validation campaigns. The MODIS LAI product suite, containing the Terra Collection 4 (C4), Terra Collection 5 (C5) and Terra+Aqua combined C5, was analyzed, with considerable attention paid to the quality control (QC) information. The CYCLOPES V3.1 LAI product was similarly analyzed with regard to the status map (SM) layer. In general, the MODIS LAI has improved consistently over all releases. MODIS C5 data retrieved with the main algorithm (QC<64) and CYCLOPES data showed a similar range of uncertainties (1.0–1.2). Uncertainties for the best MODIS C5 (QC=0) and CYCLOPES (SM=0) estimates were around 0.9–1.1. The overall mean differences between the best MODIS C5 and CYCLOPES were within ±0.10. The highest correspondence was obtained for woody biomes from the best MCD15 C5 data (RMSE=0.80). Results indicate that the uncertainties in current LAI products (around ±1.0) are still unable to meet the accuracy requirement of GCOS (±0.5). Although there are limitations, we recommend MODIS C5 retrieved with the main algorithm (QC<64) and CYCLOPES for the user community. This study demonstrates the necessity of exploring uncertainties related to the true and effective LAIs separately, and reveals the importance of referring to the quality assessment information. More field measurements are required for further studies, which should focus on under-sampled biome types and areas. [Copyright &y& Elsevier]

Advanced information on crop yield is important for crop management and food policy making. A data assimilation approach was developed to integrate remotely sensed data with a crop growth model for crop yield estimation. The objective was to model the crop yield when the input data for the crop growth model are inadequate, and to make the yield forecast in the middle of the growing season. The Cropping System Model (CSM)-Crop Environment Resource Synthesis (CERES)-Maize and the Markov Chain canopy Reflectance Model (MCRM) were coupled in the data assimilation process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and vegetation index products were assimilated into the coupled model to estimate corn yield in Indiana, USA. Five different assimilation schemes were tested to study the effect of using different control variables: independent usage of LAI, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), and synergic usage of LAI and EVI or NDVI. Parameters of the CSM-CERES-Maize model were initiated with the remotely sensed data to estimate corn yield for each county of Indiana. Our results showed that the estimated corn yield agreed very well with the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) data. Among different scenarios, the best results were obtained when both MODIS vegetation index and LAI products were assimilated and the relative deviations from the NASS data were less than 3.5%. Including only LAI in the model performed moderately well with a relative difference of 8.6%. The results from using only EVI or NDVI were unacceptable, as the deviations were as high as 21% and -13% for the EVI and NDVI schemes, respectively. Our study showed that corn yield at harvest could be successfully predicted using only a partial year of remotely sensed data. [ABSTRACT FROM AUTHOR]

Shaping residential electricity consumption behaviors can achieve the effect of load shifting to emit less carbon. Electricity consumption pattern is a good way to describe electricity consumption behavior, and is basically determined by household characteristics (HCs). Effective mining of important factors in HCs can help to shape residential electricity consumption behaviors. This paper proposes a new mining framework for discovering the association between HCs and residential electricity consumption patterns. At first, HCs are classified into six categories by the theory of planned behavior based factor classification. Then, typical electricity consumption patterns (TECPs) of each individual resident in different periods divided by seasons and weekday are extracted by a proposed piecewise symbolic spatial clustering with noise algorithm which combines symbolic aggregate approximation and density-based spatial clustering of applications with noise algorithm. Afterward, TECPs are clustered by hierarchical clustering method. To determine the clustering number, six clustering validity indexes are applied to evaluate the clustering results. Finally, a feature-selection-based multifactor analysis method is designed to evaluate 95 factors in HCs impacts on TECPs. Results of the case study using 3117 records including smart meter data and survey data show that most factors of attitudes toward energy category and socio-demographic category are effective factors for predicting TECPs, while the factors in appliance and heating category are non-significant. The proposed mining framework and the findings can provide guidance of shaping grid-friendly electricity consumption behavior. • A mining framework is proposed for factor analysis of electricity consumption. • A symbolic clustering algorithm is proposed for electricity consumption patterns. • A numerical iterative method is designed for time interval division. • Feature selection is used in multifactor analysis for household characteristics. [ABSTRACT FROM AUTHOR]

• Instantaneous direct, diffuse, and total FAPARs estimated from the Himawari-8 AHI. • The total and direct FAPARs show a "bowl-shaped" intra-daily variation pattern. • The remote sensed total and direct FAPARs at 10:00–10:30 LST underestimate the daily FAPARs. • The total and direct FAPARs at 9:30 and 14:30 LST best approximate the daily values. The fraction of absorbed photosynthetically active radiation (FAPAR) quantifies the efficiency of light absorption by vegetation. The intra-daily variation in FAPAR is important for monitoring vegetation growth but current studies to estimate the intra-daily FAPAR from remote sensing are limited. This study proposes to estimate the intra-daily variations of the direct, diffuse, and total FAPARs on clear days from the geostationary Himawari-8 AHI data. A look-up table inversion method was developed based on simulations of a radiative transfer model that couples a soil reflectance (GSV, General Spectral Vectors), canopy (PROSAIL), and atmospheric models (6SV, Second Simulation of a Satellite Signal in the Solar Spectrum). Field measurements were conducted over paddy rice fields in Northeast China from June 22 to August 25, 2019, to obtain continuous FAPAR measurements using an AccuPAR ceptometer and digital hemispherical photography. The instantaneous FAPAR values were analyzed and evaluated with field measurements and MODIS and GEOV2 FAPAR products. The results show that the retrieved AHI FAPAR values are consistent with the field measurements (R2 > 0.75). The AHI, MODIS, and GEOV2 FAPAR values display similar spatial and temporal patterns over the growing season (R2 > 0.66). The intra-daily variations in the total and direct FAPAR values agree with the field measurements, and both show a "bowl-shaped" pattern with the smallest value found at noon. The diffuse FAPAR remains stable before 16:00 local solar time (LST). The instantaneous total and direct FAPAR values at 10:00 – 10:30 LST usually assumed to be daily values underestimate the daily values, while the values at 9:30 and 14:30 LST can better represent the daily FAPAR. As a future study, global intra-daily instantaneous and daily FAPAR products could be generated by combining all geostationary satellite data. [ABSTRACT FROM AUTHOR]

One of the applications of crop simulation models is to estimate crop yield during the current growing season. Several studies have tried to integrate crop simulation models with remotely sensed data through data-assimilation methods. This approach has the advantage of allowing reinitialization of model parameters with remotely sensed observations to improve model performance. In this study, the Cropping System Model-CERES-Maize was integrated with the Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products for estimating corn yield in the state of Indiana, USA. This procedure, inversion of crop simulation model, facilitates several different user input modes and outputs a series of agronomic and biophysical parameters, including crop yield. The estimated corn yield in 2000 compared reasonably well with the US Department of Agriculture National Agricultural Statistics Service statistics for most counties. Using the seasonal LAI in the optimization procedure produced the best results compared with only the green-up LAIs or the highest LAI values. Planting, emergence and maturation dates, and N fertilizer application rates were also estimated at a regional level. Further studies will include investigating model uncertainties and using other MODIS products, such as the enhanced vegetation index. [ABSTRACT FROM AUTHOR]

Leaf Area Index (LAI) is an important biophysical variable for characterizing the land surface vegetation. Global LAI product has been routinely produced from the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellite platforms. However, the MODIS standard LAI product is not continuous both spatially and temporally. To fill the gaps and improve the quality, we have developed a data filtering algorithm. This filter, called the temporal spatial filter (TSF), integrates both spatial and temporal characteristics for different plant functional types. The spatial gaps are first filled with the multi-year averages of the same day. If the values are missing over all years, the pixel is filled with a new estimate using the vegetation continuous field–ecosystem curve fitting method. The TSF integrates both the multi-seasonal average trend (background) and the seasonal observation. We implement this algorithm using the MODIS Collection 4 LAI product over North America. Comparison of the TSF results with the Savitzky–Golay filter indicates that the TSF performs much better in restoring the spatial and temporal distribution of seasonal LAI trends. The new LAI product has been validated by comparing with field measurements and the derived LAI maps from ETM+ data at a broadleaf forest site and an agricultural site. The validation results indicate that the new LAI product agrees better with both the field measurements and LAI values obtained from the ETM+ than does the MODIS LAI standard product, which usually shows higher LAI values. [Copyright &y& Elsevier]

Abstract: Leaf area index (LAI) is an important variable needed by various land surface process models. It has been produced operationally from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a look-up table (LUT) method, but the inversion accuracy still needs significant improvements. We propose an alternative method in this study that integrates both the radiative transfer (RT) simulation and nonparametric regression methods. Two nonparametric regression methods (i.e., the neural network [NN] and the projection pursuit regression [PPR]) were examined. An integrated database was constructed from radiative transfer simulations tuned for two broad biome categories (broadleaf and needleleaf vegetations). A new soil reflectance index (SRI) and analytically simulated leaf optical properties were used in the parameterization process. This algorithm was tested in two sites, one at Maryland, USA, a middle latitude temperate agricultural area, and the other at Canada, a boreal forest site, and LAI was accurately estimated. The derived LAI maps were also compared with those from MODIS science team and ETM+ data. The MODIS standard LAI products were found consistent with our results for broadleaf crops, needleleaf forest, and other cover types, but overestimated broadleaf forest by 2.0–3.0 due to the complex biome types. [Copyright &y& Elsevier]

Semiarid rangelands are very sensitive to global climatic change; studies of their biophysical attributes are crucial to understanding the dynamics of rangeland ecosystems under human disturbance. In the Santa Rita Experimental Range, AZ, the vegetation has changed considerably, and there have been many management activities applied. This study calculates seven surface variables: the enhanced vegetation index, the normalized difference vegetation index (NDVI), surface albedos (total shortwave, visible, and near-infrared), leaf area index (LAI), and the fraction of photosynthetically active radiation (FPAR) absorbed by green vegetation from the Enhanced Thematic Mapper (ETM+) data. Comparison with the Moderate Resolution Imaging Spectroradiometer vegetation index and albedo products indicates they agree well with our estimates from ETM+, while their LAI and FPAR are larger than from ETM+. Human disturbance has significantly changed the cover types and biophysical conditions. Statistical tests indicate that surface albedos increased and FPAR decreased following tree-cutting disturbances. The recovery will require more than 67 years and is about 50% complete within 40 years at the higher elevation. Grass cover, vegetation indexes, albedos, and LAI recovered from cutting faster at the higher elevation. Woody plants, vegetation indexes, and LA! have recovered to their original characteristics after 65 years at the lower elevation. More studies are needed to examine the spectral characteristics of different ground components. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is an important structural property of vegetation canopy and is also one of the basic quantities driving the algorithms used in regional and global biogeochemical, ecological and meteorological applications. LAI can be estimated from remotely sensed data through the vegetation indices (VI) and the inversion of a canopy radiative transfer (RT) model. In recent years, applications of the genetic algorithms (GA) to a variety of optimization problems in remote sensing have been successfully demonstrated. In this study, we estimated LAI by integrating a canopy RT model and the GA optimization technique. This method was used to retrieve LAI from field measured reflectance as well as from atmospherically corrected Landsat ETM+ data. Four different ETM+ band combinations were tested to evaluate their effectiveness. The impacts of using the number of the genes were also examined. The results were very promising compared with field measured LAI data, and the best results were obtained with three genes in which the R2 is 0.776 and the root-mean-square error (RMSE) 1.064. [Copyright &y& Elsevier]

This paper presents the general methods and some preliminary results of validating Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface reflectance and albedo products using ground measurements and Enhanced Thematic Mapper Plus (ETM+) imagery. Since ground “point” measurements are not suitable for direct comparisons with MODIS pixels of about 1 km over heterogeneous landscapes, upscaling based on high-resolution remotely sensed imagery is critical. In this study, ground measurements at Beltsville, MD were used to calibrate land surface reflectance and albedo products derived from ETM+ imagery at 30 m, which were then aggregated to the MODIS resolution for determining the accuracy of the following land surface products: (1) bidirectional reflectance from atmospheric correction, (2) bidirectional reflectance distribution function (BRDF), (3) broadband albedos, and (4) nadir BRDF-adjusted reflectance. The initial validation results from ground measurements and two ETM+ images acquired on October 2 and November 3, 2000 showed that these products are reasonably accurate, with typically less than 5% absolute error. Final conclusions on their accuracy depend on more validation results. [Copyright &y& Elsevier]

• A review of CI field and remote sensing methods, characteristics, and applications. • The LX and CC methods are two standard methods in field CI estimation. • Remote sensing CI is mainly derived from the NDHD method and needs more validation. • CI shows vertical, scale, directional, and temporal variations. • CI is critical in the LAI estimation, canopy reflectance and land surface models. Canopy clumping index (CI) characterizes the spatial distribution of leaves or needles within a vegetation canopy. CI is critical in determining the canopy radiation transfer, photosynthesis, and hydrological processes. This paper reviews the field measurement and remote sensing estimation methods, characteristics, and applications of CI. CI is generally estimated in the field using direct, indirect optical, and allometric methods. The basis for the indirect optical approach is the gap-fraction based method. Remote sensing of CI is carried out using passive optical and active LiDAR technology. Current CI products are mainly derived from an empirical relationship with the normalized difference hotspot and darkspot (NDHD) index. Further CI product validation studies should be conducted using enhanced field measurements. CI typically shows vertical, scale, directional, and temporal variations. The overall CI is calculated as the integration of the directional CI values. CI is a key parameter in leaf area index (LAI) estimation, and canopy reflectance and land surface modeling studies. Future studies should focus on the adoption of automated and wireless measurement methods, development of new remote sensing estimation methods, improving our understanding of the CI characteristics, and accounting for CI in land surface models. [ABSTRACT FROM AUTHOR]

• fIPAR is a good proxy of fAPAR when the background is dark as in most rice crops. • Impact of illumination conditions and non-green components was analyzed. • Green fAPAR can be estimated from canopy fAPAR and the GAI/PAI ratio. • Downward DHP is recommended when estimating green fIPAR. Continuous and accurate ground measurements of the fraction of absorbed (fAPAR) or intercepted (fIPAR) photosynthetically active radiation by green canopy components is important to monitor canopy functioning. fAPAR and fIPAR are sensitive to illumination conditions and non-green components during the senescence stage. While several methods have been developed to estimate fAPAR or fIPAR in the field from different methods including AccuPAR, LAI-2200 and Digital Hemispheric Photograph Photography (DHP), the differences among these methods still need more investigations. The principles on which they are based are first reviewed with due attention to the assumptions used and approximations made. Two field campaigns conducted in 2012 and 2013 in northeastern China over paddy rice fields were then used to compare fAPAR and fIPAR measured using AccuPAR, DHP and LAI-2200. Results demonstrated that considering only canopy light transmittance (fIPAR), measured with AccuPAR, DHP or LAI-2200, is a good proxy of fAPAR which is computed from AccuPAR measurements of the four fluxes of the radiation balance. However, when canopy is senescing, downward looking DHP method is recommended since it is the only method that directly measures the light intercepted by green elements. Methods based on upward looking (DHP upward, AccuPAR, LAI-2200) cannot distinguish between the green and senescent vegetation elements. Corrections based on independent measurements of the ratio of the green area index (GAI) to the plant area index (PAI) (GAI/PAI) need to be used in this case, while assuming that green and senescent elements are well mixed in the canopy volume. Downward looking DHP appears to be the preferred method for relatively short and dense canopies such as rice since it does not disturb the canopy, it is sensitive to the green elements only and allows to simulate fIPAR for any illumination conditions. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is an important vegetation parameter. Active light detection and ranging (LiDAR) technology has been widely used to estimate vegetation LAI. In this study, LiDAR technology, LAI retrieval and validation methods, and impact factors are reviewed. First, the paper introduces types of LiDAR systems and LiDAR data preprocessing methods. After introducing the application of different LiDAR systems, LAI retrieval methods are described. Subsequently, the review discusses various LiDAR LAI validation schemes and limitations in LiDAR LAI validation. Finally, factors affecting LAI estimation are analyzed. The review presents that LAI is mainly estimated from LiDAR data by means of the correlation with the gap fraction and contact frequency, and also from the regression of forest biophysical parameters derived from LiDAR. Terrestrial laser scanning (TLS) can be used to effectively estimate the LAI and vertical foliage profile (VFP) within plots, but this method is affected by clumping, occlusion, voxel size, and woody material. Airborne laser scanning (ALS) covers relatively large areas in a spatially contiguous manner. However, the capability of describing the within-canopy structure is limited, and the accuracy of LAI estimation with ALS is affected by the height threshold and sampling size, and types of return. Spaceborne laser scanning (SLS) provides the global LAI and VFP, and the accuracy of estimation is affected by the footprint size and topography. The use of LiDAR instruments for the retrieval of the LAI and VFP has increased; however, current LiDAR LAI validation studies are mostly performed at local scales. Future research should explore new methods to invert LAI and VFP from LiDAR and enhance the quantitative analysis and large-scale validation of the parameters. [ABSTRACT FROM AUTHOR]

• Investigates a range of strategies in hyperspectral soil reflectance modeling. • Proposes a general spectral vectors (GSV) model using global soil data. • GSV simulates soil spectra regardless of soil types and moisture conditions. • Coupling with GSV improves canopy radiative transfer model. Soil background reflectance is a critical component in canopy radiative transfer (RT) models. However, few efforts have been devoted to the development of soil reflectance models compared to other components in canopy RT models. In the spectral domain of soil reflectance, spectral vector models are more flexible than typical spectra models, but its characteristics and performance are poorly understood and validated. To improve the understanding of hyperspectral soil reflectance modeling, this study conducted a comprehensive diagnostic analysis on different spectral vectors derivation algorithms, the impact of training datasets on model performance, and the soil moisture effect in modeling. With improved understanding, a general spectral vectors (GSV) model was developed. The model employs three dry spectral vectors and one humid spectral vector derived from global dry and humid soil reflectance databases including 23,871 soil spectra (400–2500 nm), using a matrix decomposition algorithm. A comprehensive evaluation shows that separate modeling of dry and humid soils and the usage of global training data significantly improved the performance of spectral vectors model, while the choice of spectral vectors derivation algorithm has little influence on model performance. Overall, the GSV model accurately simulates global soil reflectance with an R2 of 0.99 and RMSE of 0.01, superior to the widely-used Price model. In particular, the performance of GSV was robust over various soil types and under different moisture conditions. Coupling with the GSV model substantially reduced errors of 3D and 1D canopy RT modeling. The proposed GSV model has great potentials for vegetation remote sensing studies. [ABSTRACT FROM AUTHOR]

Over the last decade, a series of global moderate resolution leaf area index (LAI) products have become available and been widely applied in many disciplines. At the same time, there is an increasing demand for the uncertainties associated with these products, which has to be determined through rigorous validation studies. This study validated seven global LAI products — EPS, GEOV2, GLASS, GLOBMAP, MODIS, PROBA-V, and VIIRS — over typical agricultural croplands in northeastern China. Seasonal continuous LAI measurements were obtained from field campaigns in paddy rice fields in 2012 and 2013, and in maize, soybean, and sorghum fields in 2016. High resolution reference LAI maps were first derived from HJ-1, Landsat 7, and Sentinel-2A images with the look-up table (LUT) inversion method and were evaluated with the field measured LAI (R 2 = 0.85 and RMSE = 0.66). Subsequently, the moderate resolution LAI products were validated with the upscaled high resolution reference LAI. All LAI products show typical seasonal variation patterns of agricultural crops, but distinct differences exist among the products. The product quality indicators show large deviations during the peak growing season, whereas the relative uncertainties are higher during the green-up and senescent phases. Both EPS and GLASS show some saturation effects at LAI ~ 4.0 and underestimate the reference LAI (>0.5), whereas GLOBMAP shows the largest overestimation (bias = 0.96). GEOV2 and PROBA-V significantly overestimate the LAI for all crops. In contrast, MODIS and VIIRS underestimate and show high variations (RMSE >1.50, RRMSE >47%) compared with the reference LAI. In general, the global moderate resolution LAI products show moderate agreement with the reference LAI (RMSE: 0.80–2.0 and RRMSE: 25–60%). The product uncertainties are higher over paddy rice fields than those over the other crop fields. The uncertainties are mainly attributed to the lack of regional tuning of the global algorithms for agricultural crops at different growth stages. Further algorithm improvement and validation studies are necessary to improve the global LAI products for regional applications. • Estimated reference LAI from HJ-1, Landsat 7, and Sentinel-2A with an LUT method • Validated EPS, GEOV2, GLASS, GLOBMAP, MODIS, PROBA-V, and VIIRS LAI products • Revealed significant differences between LAI products in different crop growth stages • Satellite and reference LAI moderately agree (RMSE: 0.80–2.0, RRMSE: 25–60%) • Crop-specific and time series validation necessary in further studies [ABSTRACT FROM AUTHOR]

The vegetation clumping index (CI) quantifies the degree to which foliage deviates from a random distribution, and therefore, is an important canopy structural parameter that governs the photosynthesis and evapotranspiration processes in terrestrial ecosystems. While the temporal variation of CI has been investigated in many field studies, there has not been a global CI product that captured its seasonal and inter-annual variations. In this study, a look-up table (LUT) method was developed based on an improved Normalized Difference between Hotspot and Darkspot (NDHD) method to estimate the daily CI from the MODIS V006 MCD43 BRDF (bidirectional reflectance distribution function) product. This new CI product is consistent with field measurements at 33 sites distributed globally (R2 = 0.38, RMSE = 0.04, and bias = −0.01). CI shows stronger seasonal variations for deciduous broadleaf forest (DBF) and mixed forests than other vegetation types. The large seasonal differences between evergreen and deciduous vegetation, especially for DBF and deciduous shrubs, allow the CI to be used to detect seasonal structural changes and classify vegetation. The global CI shows a negative correlation to the leaf area index (LAI) for all land cover types, especially forests. The global CI demonstrates an inter-annual variation that correlated with global precipitation (R2 = 0.34). In addition, the global CI shows a global decreasing trend from 2001 to 2017 (−0.007/decade) that was consistent with the increasing LAI (0.049/decade). The time series of CI provides a new perspective for understanding the structural characteristics of vegetation in global ecosystem studies. • A global 500 m clumping index (CI) product (2001–2017) was generated and evaluated. • CI shows strong seasonal variation for deciduous vegetation types. • CI and LAI show stronger negative correlation for forests than other vegetation. • CI shows strong inter-annual variation that may be related to precipitation. • The global CI has decreased since 2001 (−0.007/decade), opposite to the increasing LAI. [ABSTRACT FROM AUTHOR]

• A wrapper method of feature selection considering both relevance and redundancy is proposed for similar-day choosing. • A novel similar-day choosing method is used to excavate the uncertainties in residential loads. • An efficient and accurate probability density forecasting framework is proposed for residential loads. • Superior performances in both point forecasting and probabilistic forecasting are obtained. Probabilistic load forecasting can provide much richer information about uncertainties in residential loads than point load forecasting, and is gaining increasing attention in building efficient and economical demand response programs. However, obtaining satisfactory forecasting results for residential loads is challenging due to their intrinsic nonlinearity and volatility. Furthermore, existing probabilistic forecasting methods are limited in their ability to directly generate probability density forecasting in an efficient and accurate way. To address these issues, a new probability density forecasting framework for residential loads is proposed in this paper. Firstly, a similar-day choosing method is proposed by combining weighted external features and internal load curves to build a similar-day dataset. Then, point forecasting is achieved by training an exclusive predictor on the corresponding similar-day dataset for each forecasting target. Finally, probability density forecasting is realized by conducting kernel density estimation on the similar-day dataset and calibrating it according to the point forecasting result. Comparisons with other state-of-the-art methods carried out on the HUE dataset demonstrate the efficiency and superiority of the proposed framework in both point and probability density forecasting. [ABSTRACT FROM AUTHOR]

Short-term load forecasting for residential buildings is of great significance in ensuring the safe and economic operation of the power grid. However, most existing prediction methods focus solely on the temporal characteristics of individual buildings and ignore the spatial correlation between different buildings in a neighborhood. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with a selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto-correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy is proposed to build input feature set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, a novel selected-shared-private mechanism is designed for multi-task learning to improve the prediction accuracy, which can selectively utilize information from related tasks while learning private features. The proposed model is compared with other methods on the public dataset, and the results demonstrate that the proposed model can make satisfactory joint predictions for multiple buildings with more than 35% accuracy improvement over the support vector machine. • A correlation-oriented combination strategy is proposed to build the optimal input set. • A novel selected-shared-private mechanism is designed for multi-task learning. • The proposed model realizes superior joint load prediction for multiple residential buildings. [ABSTRACT FROM AUTHOR]

• A new deep learning-based clustering method is proposed to deal with the large number of daily load curves. • A novel customer selection framework is proposed to identify the potential good candidates for demand response programs. • The proposed clustering method outperforms the classical K-means and two state-of-the-art deep clustering methods. • The customers selected by the proposed framework contribute to 59 % of total peak reduction with only accounting for 23 % of population. Demand response (DR) is regarded as a promising solution to the problem of renewable energy integration, while it remains one of the key barriers for DR to target the right customers, i.e. the customers with high potential in peak reduction. Exploring the electricity consumption behaviors of customers can help DR program practitioners evaluate customers' peak reduction potential. Clustering has been the most common approach to describe customers' consumption behaviors in the literature. However, the existing clustering methods suffer from either unscalability or poor performance when handling a large number of daily load curves. To overcome these issues, a new deep learning-based clustering method is proposed in this paper, which integrates both intra-cluster compactness and inter-cluster separation into the objective function of clustering for consideration. Based on the clustering results, a new customer selection framework is developed to identify the potential good candidates for DR programs, which takes the stability of consumption behaviors and the characteristics at peak hours into account. Case studies on a London dataset demonstrate the superiority of the proposed clustering method over classical K-means and two state-of-the-art deep clustering methods in terms of clustering validity indexes. Furthermore, the simulations performed on an actual price-based pilot show that the customers selected by the proposed customer selection framework contribute to 59 % of total peak reduction with only accounting for 23 % of population. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been widely used as a prospective proxy of plant photosynthesis to accurately detect the response of vegetation to climate change and study terrestrial ecosystem carbon cycle. However, the challenge of estimating vegetation gross primary product (GPP) based on SIF observations remains unresolved due to the confounding effects of leaf physiology and canopy structure, especially the vertical heterogeneity of plant attributes. Radiative transfer models are ideally suited to investigate the variability in radiance signals including fluorescence, but previous studies lack vertically layered spectra to validate the model. In this study, we developed a vertically layered hyperspectral system and proposed an innovative framework for assessing vertical characteristics of SIF and improving the estimation of GPP based on SIF in a subtropical evergreen forest. The global sensitivity analysis on Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model with vertical profiles showed that chlorophyll content (Cab), leaf inclination distribution (LIDFa), leaf area index (LAI), and senescent material (Cs) dominate the vertical variations in reflectance (Ref) and SIF. We found that the vertical characteristics of SIF were mainly impacted by these parameters of the adjacent vertical layer, except for the effects of LAI across the vertical layers on the observed understory SIF U , which were approximately equivalent. 7.8 ± 1.7% of the SIF U was transferred to the top of canopy, contributing up to 17% of the SIF observed on the top of canopy (SIF TOC) during the year. Furthermore, our findings suggest that substituting the observed SIF TOC with the total emitted SIF, particularly from the simulated overstory and understory SIF, could enhance the correlation between GPP and SIF with an increased R2 (ΔR2 = 0.09). This study highlights the importance of accounting for the contribution of the layered SIF in the quantification of the total SIF emission and can benefit the GPP estimation based on SIF signals in subtropical evergreen forests with high canopy density. [ABSTRACT FROM AUTHOR]

Understanding the long-term performance of global satellite leaf area index ( LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products ( GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products ( GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre- MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS> LAI3g> TCDR> GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS> LAI3g> TCDR> GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products. [ABSTRACT FROM AUTHOR]

• A framework was developed and evaluated to estimate daily FAPAR from instantaneous FAPAR. • Daily FAPAR can be estimated from MODIS instantaneous FAPARs. • MODIS instantaneous FAPAR underestimates daily value. • Daily FAPAR is potential to improve GPP estimation. The daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is crucial for vegetation dynamics monitoring and terrestrial carbon modeling. However, current FAPAR products only provide instantaneous values at the satellite transit times and a large data gap exists between instantaneous and daily FAPAR. To fill this gap, this study aimed to estimate daily FAPAR from instantaneous values retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) data. A framework combining method to estimate instantaneous FAPAR (instantaneous FAPAR method) and method to calculate daily FAPAR (instantaneous-to-daily FAPAR method) was proposed. Instantaneous FAPAR method for estimating intra-daily FAPAR from reduced observations was evaluated. Three instantaneous-to-daily FAPAR methods (ratio, simple average and solar zenith angle weighted average methods) were compared to estimate the daily FAPAR from continuous instantaneous values. The daily FAPAR was finally estimated from MODIS instantaneous values. The estimation and validation were performed at seven forest sites distributed in North America and Western Europe. The results show that the instantaneous FAPAR method can accurately simulate the intra-daily instantaneous FAPAR (RMSE ∼ 0.01, bias ∼ 0). The three instantaneous-to-daily FAPAR estimation methods showed similar performances (R2 ∼1, RMSE ∼ 0.01, and bias ∼ 0). The in situ instantaneous FAPAR values at the MODIS acquisition times underestimated the daily value during the non-peak growing season (FAPAR < 0.9) and the underestimation is also affected by the diffuse PAR radiation. MODIS instantaneous FAPAR underestimates in situ daily FAPAR over the year. The daily FAPAR estimates from in situ instantaneous values at MODIS acquisition times or the MODIS instantaneous FAPAR data reduce the gap between instantaneous and daily values. Validated by in situ data, the MODIS instantaneous FAPAR products should be further optimized to improve the accuracy of daily FAPAR estimation. This study lays the foundation for estimating the daily FAPAR from instantaneous values to improve GPP estimation. [ABSTRACT FROM AUTHOR]

Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates. [ABSTRACT FROM AUTHOR]

A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show spatio-temporal discontinuities and are sometimes inconsistent with vegetation growth patterns. A deep-learning model was proposed to retrieve time-series LAIs from multiple satellite data in this paper. The fusion of three global LAI products (i.e., VIIRS, GLASS, and MODIS LAI) was first carried out through a double logistic function (DLF). Then, the DLF LAI, together with MODIS reflectance (MOD09A1) data, served as the training samples of the deep-learning long short-term memory (LSTM) model for the sequential LAI estimations. In addition, the LSTM models trained by a single LAI product were considered as indirect references for the further evaluation of our proposed approach. The validation results showed that our proposed LSTMfusion LAI provided the best performance (R2 = 0.83, RMSE = 0.82) when compared to LSTMGLASS (R2 = 0.79, RMSE = 0.93), LSTMMODIS (R2 = 0.78, RMSE = 1.25), LSTMVIIRS (R2 = 0.70, RMSE = 0.94), GLASS (R2 = 0.68, RMSE = 1.05), MODIS (R2 = 0.26, RMSE = 1.75), VIIRS (R2 = 0.44, RMSE = 1.37) and DLF LAI (R2 = 0.67, RMSE = 0.98). A temporal comparison among LSTMfusion and three LAI products demonstrated that the LSTMfusion model efficiently generated a time-series LAI that was smoother and more continuous than the VIIRS and MODIS LAIs. At the crop peak growth stage, the LSTMfusion LAI values were closer to the reference maps than the GLASS LAI. Furthermore, our proposed method was proved to be effective and robust in maintaining the spatio-temporal continuity of the LAI when noisy reflectance data were used as the LSTM input. These findings highlighted that the DLF method helped to enhance the quality of the original satellite products, and the LSTM model trained by the coupled satellite products can provide reliable and robust estimations of the time-series LAI. [ABSTRACT FROM AUTHOR]

• Vegetation coverage of desert ecosystems was investigated in the Qinghai-Tibet Plateau (QTP). • A boosted regression tree (BRT) model was employed for desert vegetation identification. • A robust empirical model based on in-situ FVC and satellite-derived MSAVI was proposed. • Currently available vegetation coverage estimates for QTP's desert ecosystems underestimate FVC by at least 29%. Accurate fractional vegetation coverage (FVC) detection is beneficial for evaluating the dynamics and development potential of desert ecosystem. However, at present the performance of the related published products in the Qinghai-Tibet Plateau (QTP)'s desert ecosystem has not been verified, and the method for extracting FVC for this region from unmanned aerial vehicle (UAV) RGB images has also not been tested. UAV RGB images were processed via a boosted regression tree model (BRT). 57 ground measurements were collected, and were further paired with vegetation indexes (VIs) from satellite sensors to develop an empirical model that was used to evaluate FVC of QTP's desert ecosystem. The results showed that: (1) BRT effectively enhanced the vegetation information in UAV RGB images and improved the accuracy of FVC ground measurements (the area under the receiver operating characteristic curve (AUC) = 0.95, kappa > 0.95); (2) The relationship between FVC and modified soil-adjusted vegetation index (MSAVI) was most robust in QTP's desert ecosystem, and a power model (FVC = 13.85 × MSAVI2.07, R2 = 0.89, RMSE < 0.01) was proposed to derive local FVC within an area of 1.05 × 106 km2; (3) Although the current published FVC products show spatial consistency in QTP's desert ecosystem, FVC was underestimated by these products by at least 29% compared with the FVC values derived from the power model determined in this study. [ABSTRACT FROM AUTHOR]

Abstract: Estimation of aerosol loadings is of great importance to the studies on global climate changes. The current Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol estimation algorithm over land is based on the “dark-object” approach, which works only over densely vegetated (“dark”) surfaces. In this study, we develop a new aerosol estimation algorithm that uses the temporal signatures from a sequence of MODIS imagery over land surfaces, particularly “bright” surfaces. The estimated aerosol optical depth is validated by Aerosol Robotic Network (AERONET) measurements. Case studies indicate that this algorithm can retrieve aerosol optical depths reasonably well from the winter MODIS imagery at seven sites: four sites in the greater Washington, DC area, USA; Beijing City, China; Banizoumbou, Niger, Africa; and Bratts Lake, Canada. The MODIS aerosol estimation algorithm over land (MOD04), however, does not perform well over these non-vegetated surfaces. This new algorithm has the potential to be used for other satellite images that have similar temporal resolutions. [Copyright &y& Elsevier]

The Sanjiang Plain is the largest agricultural reclamation area and the biggest marsh area in China. The regional vegetation coverage in this area is vital to local ecological systems, and vegetation growth is affected by natural and anthropogenic factors. The clumping index (CI) is of great significance for land surface models and obtaining information on other vegetation structures. However, most existing ecological models and the retrieval of other vegetation structures do not consider the spatial and temporal variations of CI, and few studies have focused on detecting factors that influence the spatial differentiation of CI. To address these issues, this study investigated the spatial and temporal characteristics of foliage CI in the Sanjiang Plain, analysing the correlation between CI and leaf area index (LAI) through multiple methods (such as Theil−Sen trend analysis, the Mann−Kendall test, and the correlation coefficient) based on the 2001−2015 Chinese Academy of Sciences Clumping Index (CAS CI) and Global LAnd Surface Satellite Leaf Area Index (GLASS LAI). The driving factors of the spatial differentiation of CI were also investigated based on the geographical detector model (GDM) with natural data (including the average annual temperature, annual precipitation, elevation, slope, aspect, vegetation type, soil type, and geomorphic type) and anthropogenic data (the land use type). The results showed that (1) the interannual variation of foliage CI was not obvious, but the seasonal variation was obvious in the Sanjiang Plain from 2001 to 2015; (2) the spatial distribution of the multiyear mean CI of each season in the Sanjiang Plain was similar to the spatial distribution of the land use type, and the CI decreased slightly with increases in elevation; (3) the correlation between the growing season mean CI (CIGS) and the growing season mean LAI (LAIGS) time series was not significant, but their spatial distributions were negatively correlated; (4) topographic factors (elevation and slope) and geomorphic type dominated the spatial differentiation of foliage CI in the Sanjiang Plain, and the interactions between driving factors enhanced their explanatory power in terms of the spatial distribution of foliage CI. This study can help improve the accuracy of the retrieval of other vegetation structures and the simulation of land surface models in the Sanjiang Plain, providing invaluable insight for the analysis of the spatial and temporal variations of vegetation based on CI. Moreover, the results of this study support a theoretical basis for understanding the explanatory power of natural and anthropogenic factors in the spatial distribution of CI, along with its driving mechanism. [ABSTRACT FROM AUTHOR]

The U.S. National Park Service uses W126 to assess critical levels of ozone (O 3) exposure to sensitive plant species, whereas the scientific community considers the stomatal flux-based metric, phytotoxic O 3 dose (POD), more relevant in foliar risk evaluation. This study found a decreasing trend of 0.15 ppm-hrs/yr (p = 0.049) in W126 at a Yellowstone National Park monitoring site during 1997–2021 and no trend at a Grand Teton National Park site over 2012–2021 but no trends at both sites in POD calculated following the United Nations Economic Commission for Europe Convention on Long-range Transboundary Air Pollution (CLRTAP) (2017). To evaluate the CLRTAP (2017) method, 2019 summer POD was calculated for comparison using the Big-leaf model and was found 20%–100% less than the CLRTAP (2017)-calculated leaf-level one already, let alone compared to the latter scaled up to canopy-level, owing to the much smaller stomatal O 3 flux (SOF) estimates under most conditions. Further analysis revealed exceptions where larger SOF values (>0.02 mmol/m2/hr), obtained from using the Big-leaf model with measured sensible heat and water vapor flux included in input, were not captured by the CLRTAP (2017) method and the Big-leaf model with input of regular meteorological variables only. Those large SOF values resulted under cooler, less windy, more humid, and cloudier conditions, when the approximate water vapor concentration difference between the ambient and leaf saturation level was found <1 mol/m3. A multivariate function was developed, through machine learning, by regressing SOF on air temperature, wind speed, relative humidity, phytosynthetically active radiation, and O 3 concentration. Its testing and evaluation demonstrated improved estimates of the large variability in SOF. This new parameterization showed promising capability of assessing critical levels of O 3 exposure in park management, and it could be further improved with long-term flux measurement data becoming available and evapotranspiration better represented. • W126 at NPS's YELL decreased at 0.15 ppm-hrs/yr (p = 0.049). • No trend was found in CLRTAP (2017) -calculated POD. • Big-leaf Model with measured fluxes yielded large SOF (0.02–0.12 mmol/m2/s). • Those very large SOF values were not captured by other commonly used methods. • A new approach captured the large SOF and could be useful to park management. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor (CAR)-T cell therapy is a novel cellular immunotherapy for relapsed/refractory(R/R) B acute lymphoblastic leukemia (B-ALL). However, the survival duration of CAR-T cells in vivo is noteworthy, and in some cases recurrence occurs following CAR-T cell therapy. There is controversy over the benefits of bridging to allo-HSCT after CAR-T cell therapy. The present study explored the efficacy and safety of CD19 chimeric antigen receptor (CAR) T-bridged allogeneic hematopoietic stem cell transplantation (allo-HSCT) treatment in relapsed/refractory B-cell acute lymphocytic leukemia (R/R B-ALL). A total of 9 patients with B-ALL treated at The First Affiliated Hospital of Wenzhou Medical University between December 2016 and November 2017 were included. The results demonstrated that the total response rate on day 28 after receiving CD19-CAR T-cell therapy was 100% (9/9) and all patients exhibited complete remission. The 1-year overall survival (OS) rate for 5 patients who received CAR-T bridged HSCT was 100%, the 1-year DFS rate was 100%; the 1-year OS rate for the 4 patients who received CAR-T therapy was 75%, and the 1-year DFS rate was 75%. Patients who received CAR-T bridged to HSCT had no significant prolongation of myeloid and platelet engraftment median time compared with patients who received CAR-T alone, and the incidence of acute graft-versus-host disease or extensive chronic graft-versus-host disease did not increase. Overall, the present clinical trial demonstrated that CAR-T therapy bridging to HSCT is a feasible, safe and effective method to treat adult patients with R/R B-ALL. [ABSTRACT FROM AUTHOR]

Clumping index is the measure of foliage grouping relative to a random distribution of leaves in space. It is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. The Normalized Difference between Hotspot and Darkspot (NDHD) index has been previously used to retrieve global clumping index maps from POLarization and Directionality of the Earth’s Reflectances (POLDER) data at ∼6 km resolution and the Bidirectional Reflectance Distribution Function (BRDF) product from Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution. Most recently the algorithm was also applied with Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m resolution over selected areas. In this study for the first time we characterized and compared the three products over a set of sites representing diverse biomes and different canopy structures. The products were also directly validated with both in-situ vertical profiles and available seasonal trajectories of clumping index over several sites. We demonstrated that the vertical distribution of foliage and especially the effect of understory need to be taken into account while validating foliage clumping products from remote sensing products with values measured in the field. Satellite measurements responded to the structural effects near the top of canopies, while ground measurements may be biased by the lower vegetation layers. Additionally, caution should be taken regarding the misclassification in land cover maps as their errors can propagate into the foliage clumping maps. Our results indicate that MODIS data and MISR data, with 275 m in particular, can provide good quality clumping index estimates at spatial scales pertinent for modeling local carbon and energy fluxes. [ABSTRACT FROM AUTHOR]

Abstract: The leaf area index (LAI) is one of the most critical structural parameters of the vegetation canopy in regional and global biogeochemical, ecological, and meteorological applications. Data gaps and spatial and temporal inconsistencies exist in most of the existing global LAI products derived from single-satellite data because of their limited information content. Furthermore, the accuracy of current LAI products may not meet the requirements of certain applications. Therefore, LAI retrieval from multiple satellite data is becoming popular. An existing LAI inversion scheme using the ensemble Kalman filter (EnKF) technique is further extended in this study to integrate temporal, spectral, and angular information from Moderate Resolution Imaging Spectroradiometer (MODIS), SPOT/VEGETATION, and Multi-angle Imaging Spectroradiometer (MISR) data. The recursive update of LAI climatology with the retrieved LAI and the coupling of a canopy radiative-transfer model and a dynamic process model using the EnKF technique can fill in missing data and produce a consistent accurate time-series LAI product. During each iteration, we defined a 5∗1 sliding window and compared the RMSEs in the selected window to determine the minimum. Validation results at six sites demonstrate that the combination of temporal information from multiple sensors, spectral information provided by red and near-infrared (NIR) bands, and angular information from MISR bidirectional reflectance factor (BRF) data can provide a more accurate estimate of LAI than previously available. [Copyright &y& Elsevier]

The purpose of this paper is to develop Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) Normalised Difference Vegetation Index (NDVI; AVHRR GIMMS NDVI for short) based fraction of absorbed photosynthetically active radiation (FPAR) from 1982 to 2006 and focus on their seasonal and spatial patterns analysis. The available relationship between FPAR and NDVI was used to calculate FPAR values from 1982 to 2006 and validated by Moderate-resolution Imaging Spectroradiometer (MODIS) FPAR product. Then, the seasonal dynamic patterns were analysed, as well as the driving force of climatic factors. Results showed that there was an agreement between FPAR values from this study and those of the MODIS product in seasonal dynamic, and the spatial patterns of FPAR vary with vegetation type distribution and seasonal cycles. The time series of average FPAR revealed a strong seasonal variation, regular periodic variations from January 1982 to December 2006, and opposite patterns between the Northern and Southern Hemispheres. Evergreen vegetation FPAR values were close to 0.7. A clear single-peak curve was observed between 30°N and 80°N – an area covered by deciduous vegetation. In the Southern Hemisphere, the time series fluctuations of FPAR averaged by 0.7° latitude zones were not clear compared to those in the Northern Hemisphere. A significant positive correlation (P<0.01) was observed between the seasonal variation of temperature and precipitation and FPAR over most other global meteorological sites. [ABSTRACT FROM AUTHOR]

In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr−1 for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research. [ABSTRACT FROM AUTHOR]