Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change ...studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MOD!S) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981-1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000-2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps (RMSE = 0.7848 and R 2 = 0.8095) than the GEOV1 LAI values (RMSE = 0.9084 and R 2 = 0.7939) and the MOD15 LAI values (RMSE = 1.1173 and R 2 = 0.6705).
Given their low cost and intrinsic safety, aqueous Zn metal batteries (AZMBs) are drawing increasing attention in the field of smart grids and large-scale energy storage. However, the Zn metal anode ...in aqueous electrolyte suffers from a critical issue, corrosion, which must be fully addressed before the practical implementation of AZMBs. In this perspective, the mechanisms of aqueous Zn metal anode corrosion in both alkaline and neutral electrolytes are compared and discussed. The methods for studying the corrosion processes and the strategies for Zn corrosion protection in AZMBs are also summarized. Finally, some expectations about potential research directions for making corrosion-resistant AZMBs a commercial reality are provided.
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•The development of aqueous Zn batteries, from the first voltaic pile to very recent sustainable Zn batteries, is summarized.•The corrosion mechanisms of aqueous Zn anodes in both alkaline and neutral electrolytes are discussed.•Potential future research directions are proposed for next-generation aqueous Zn anode materials.
Leaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other ...factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China.
Nickel-iron layered double hydroxide （NiFe-LDH） nanosheets have shown optimal oxygen evolution reaction （OER） performance; however, the role of the intercalated ions in the OER activity remains ...unclear. In this work, we show that the activity of the NiFe-LDHs can be tailored by the intercalated anions with different redox potentials. The intercalation of anions with low redox potential （high reducing ability）, such as hypophosphites, leads to NiFe-LDHs with low OER overpotential of 240 mV and a small Tafel slope of 36.9 mV/dec, whereas NiFe-LDHs intercalated with anions of high redox potential （low reducing ability）, such as fluorion, show a high overpotential of 370 mV and a Tafel slope of 80.8 mV/dec. The OER activity shows a surprising linear correlation with the standard redox potential. Density functional theory calculations and X-ray photoelectron spectroscopy analysis indicate that the intercalated anions alter the electronic structure of metal atoms which exposed at the surface. Anions with low standard redox potential and strong reducing ability transfer more electrons to the hydroxide layers. This increases the electron density of the surface metal sites and stabilizes their high-valence states, whose formation is known as the critical step prior to the OER process.
Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which ...cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies.
•The VFSA-based variational scheme was used to derive spatio-temporally continuous LAI.•The new sensitivity analysis method was presented to identify free parameters.•The quality of estimated LAI ...results using the VFSA-based scheme had been improved.•The assimilated LAI was found to outperform MODIS LAI, ACRM LAI and CERES-Maize LAI.
The spatially and temporally continuous leaf area index (LAI) mapping is very crucial for many agricultural applications, such as crop yield estimation and growth status monitoring. Data assimilation technology provides an innovational way to improve spatio-temporally continuous crop LAI estimation through integration of remotely sensed observations and crop growth models. In this study, a very fast simulated annealing (VFSA)-based variational assimilation scheme was proposed to integrate the crop growth model (CERES-Maize), MODIS reflectance product (MOD09A1) and a two-layer canopy reflectance model (ACRM) to estimate time-series crop LAI at regional scale. Simultaneously, a new sensitivity analysis method (called “histogram comparison”) was developed to identify sensitive parameters of CERES-Maize and ACRM models. The proposed scheme was applied for continuous crop LAI estimation during the maize growing season in the dominating spring maize planting area of Jilin province, China. Results showed that R2 values between LAI estimations from the proposed assimilation scheme (referred to as assimilated LAI) and fine resolution LAI reference maps were 0.24 and 0.63, with RMSE values of 0.21 and 0.54 for Julian day 176, 2010, and Julian day 196, 2010, respectively. The assimilated results were closer to LAI reference maps than the MODIS LAI product and ACRM-based inversion results (referred to as ACRM LAI). Moreover, by introducing the prior information of LAI dynamics depicted by a crop growth model, the assimilated LAI showed better temporal consistency than the MODIS LAI product, LAI profiles simulated by CERES-Maize model (referred to as CERES-Maize LAI), and ACRM LAI. It was found that the accuracies of LAI estimations could be enhanced by assimilating satellite observations into a crop simulation model in the VFSA framework at a regional scale.
Background
Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research. ...The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale. However, this method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. In this paper, we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale. We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.
Results
The semi-variance model can be used to calculate the CD at the regional scale that closely matches (mainly with in a range from − 1 to 1 m) the CD derived from the canopy height model (CHM) data. The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small, with a root mean square error (RMSE) of 1.96 for a 500-m area with high fractional vegetation cover (FVC) (i.e., forest area coverage index values greater than 0.8). Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height. And the error caused by the unmatched spatial resolution is small in dense forest.
Conclusions
The semi-variance model can be used to calculate the CD at the regional scale, together with the canopy structure parameters inverted by the GOMS model, the mean tree height at the regional scale can be obtained. Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.
A ground-based, upward-scanning, near-infrared lidar, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at ...breast height (DBH), stem count density (stems/area), basal area, and above-ground woody biomass with very good accuracy in six New England hardwood and conifer forest stands. Comparing forest structural parameters retrieved using EVI data with extensive ground measurements, we found excellent agreement at the site level using five EVI scans (plots) per site (
R
2
=
0.94–0.99); very good agreement at the plot level for stem count density and biomass (
R
2
=
0.90–0.85); and good agreement at the plot level for mean DBH and basal area (
R
2
=
0.48–0.66). The observed variance at site and plot levels suggest that a sample area of at least 1
ha (10
4
m
2) is required to estimate these parameters accurately at the stand level using either lidar-based or conventional methods. The algorithms and procedures used to retrieve these structural parameters are dependent on the unique ability of the Echidna® lidar to digitize the full waveform of the scattered lidar pulse as it returns to the instrument, which allows consistent separation of scattering by trunks and large branches from scattering by leaves. This successful application of ground-based lidar technology opens the door to rapid and accurate measurement of biomass and timber volume in areal sampling scenarios and as a calibration and validation tool for mapping biomass using airborne or spaceborne remotely sensed data.
► Ground-based, full-waveform, scanning Echidna lidar retrieves mean tree diameter, stem density, basal area, and above-ground biomass with R2=0.94-0.99. ► We scanned six 1-ha sites, five scans per site, in three New England forests including deciduous and coniferous stands. ► Best agreement is obtained when five scans are averaged to represent one hectare. ► Occlusion of far trunks hidden behind near trunks is corrected using geometrical probability theory. ► Biomass is estimated by a pooled allometric equation using the two leading dominant species at the site.
Leaf Area Index (LAI) is one of the most important variables characterizing land surface vegetation and dynamics. Many satellite data, such as the Moderate Resolution Imaging Spectroradiometer ...(MODIS), have been used to generate LAI products. It is important to characterize their spatial and temporal variations by developing mathematical models from these products. In this study, we aim to model MODIS LAI time series and further predict its future values by decomposing the LAI time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary or irregular parts. Three such models that can characterize the non-stationary time series data and predict the future values are explored, including Dynamic Harmonics Regression (DHR), STL (Seasonal-Trend Decomposition Procedure based on Loess), and Seasonal ARIMA (AutoRegressive Intergrated Moving Average) (SARIMA). The preliminary results using six years (2001–2006) of the MODIS LAI product indicate that all these methods are effective to model LAI time series and predict 2007 LAI values reasonably well. The SARIMA model gives the best prediction, DHR produces the smoothest curve, and STL is more sensitive to noise in the data. These methods work best for land cover types with pronounced seasonal variations.
Watershed Allied Telemetry Experimental Research Li, Xin; Li, Xiaowen; Li, Zengyuan ...
Journal of Geophysical Research. B. Solid Earth,
27 November 2009, Letnik:
114, Številka:
D22
Journal Article
Recenzirano
The Watershed Allied Telemetry Experimental Research (WATER) is a simultaneous airborne, satellite‐borne, and ground‐based remote sensing experiment aiming to improve the observability, ...understanding, and predictability of hydrological and related ecological processes at a catchment scale. WATER consists of the cold region, forest, and arid region hydrological experiments as well as a hydrometeorology experiment and took place in the Heihe River Basin, a typical inland river basin in the northwest of China. The field campaigns have been completed, with an intensive observation period lasting from 7 March to 12 April, from 15 May to 22 July, and from 23 August to 5 September 2008: in total, 120 days. Twenty‐five airborne missions were flown. Airborne sensors including microwave radiometers at L, K, and Ka bands, imaging spectrometer, thermal imager, CCD, and lidar were used. Various satellite data were collected. Ground measurements were carried out at four scales, that is, key experimental area, foci experimental area, experiment site, and elementary sampling plot, using ground‐based remote sensing instruments, densified network of automatic meteorological stations, flux towers, and hydrological stations. On the basis of these measurements, the remote sensing retrieval models and algorithms of water cycle variables are to be developed or improved, and a catchment‐scale land/hydrological data assimilation system is being developed. This paper reviews the background, scientific objectives, experiment design, filed campaign implementation, and current status of WATER. The analysis of the data will continue over the next 2 years, and limited revisits to the field are anticipated.
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