The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPARFVC) based on Beer-Lambert’s law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert’s law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPARFVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPARLAI) and MODIS FPAR products (FAPARMOD). The mean absolute percentage error of FAPARFVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPARLAI and FAPARMOD, respectively. The mean percentage error of FAPARFVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPARLAI and FAPARMOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPARFVC became more pronounced with increasing FVC compared to FAPARLAI. The findings suggested that the FAPARFVC method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers.
Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.
Abstract The absolute crystallization ages of minerals from hydrothermal fluids measured in situ can unravel the timing of key events leading to the formation of, for instance, ore deposits and hydrothermally derived geological terrains. In this study, a skarn iron deposit from northwest (NW) China is shown to have U-Pb garnet and U-Pb zircon ages of 254.2 ± 1.7 Ma and 255.5 ± 1.0 Ma, respectively, which are both significantly younger than magmatism and metamorphism of the region. This skarn age instead correlates with the occurrence of strike-slip and thrust faulting in the region. The water/rock mass ratio of 0.065~0.115 suggests the δ 18 O garnet composition is ~1‰ at temperatures ranging from 250–450 °C. The low oxygen isotopic composition indicates the role of meteoric water in the garnet formation. These measurements can be interpreted as the shear along faults circulating meteoric water ~10 km below the hanging wall of meta-volcanic sedimentary rock. Meteoric water in this hydrothermal system would leach cations from the meta-volcano-sedimentary rocks necessary for mineralization. Silica-rich hydrothermal fluid reacts with calcic-rich materials in the meta-volcano-sedimentary rocks, depositing the garnet and magnetite. Our work suggests that the shear zone is rich in ores, rendering this deposit for NW China a prospective source for future mineral resource exploration.
Radar backscattering response has the potential of retrieving desired snow parameters, such as snow water equivalence, snow depth, liquid water content which are important factors in hydrological investigation. The objective of this study is to develop an algorithm which can decompose the scattering of wet snow and also develop new description of surface scattering. There are two scattering sources - the volume scattering component from snow pack and the air-snow surface scattering component - for radar backscattering while observing wet snow. Depending upon which scattering component is dominant and then controls the response to snow wetness, an algorithm can be developed to quantitatively describe the relationship between this two scattering sources and snow wetness. We have established a model - simulated at C-band a ta-base by using two scattering components. The database covers the most possible wet snow physical properties and surface roughness conditions. Using this data-base, an inversion algorithm can be developed for using C-band multi-polarization measurements. The newly developed algorithm mainly involved two steps: 1) decomposes the surface and volume scattering signals, and 2) then use each scattering component to estimate snow wetness
A quantitative study on macrobenthos was conducted at 78 stations in the North Yellow Sea in January,2007.A total of 322 macrobenthic species was identified in the study,among which 147 were polychaetes,62 mollusks,82 crustaceans,15 echinoderms and 16 species of other groups.In the study area,the mean abundance and biomass were 1 883 ind./m2 and 38.86 g.wwt/m2 respectively.Compared with other studies,the number of macrobenthic species in the North Yellow Sea was higher than in the Bohai Sea,but lower than the East China Sea;the abundance and biomass were higher than the East China Sea and the South Yellow Sea.As a whole,the distribution of number of species,speices abundance and biomass of macrozoobenthos in the North Yellow Sea in winter were higher in along-shore areas than off-shore areas.Data analysis revealed that there were significant positive,significant negative and obviously significant correlation between the macrozoobenthic abundance and median diameter,sand% and silt% respectively,while no significant correlation between macrobenthic biomass and measured environmental factors was observed.
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