Haijiang Liu

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 (FAPAR_FVC) 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 FAPAR_FVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPAR_LAI) and MODIS FPAR products (FAPAR_MOD). The mean absolute percentage error of FAPAR_FVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPAR_LAI and FAPAR_MOD, respectively. The mean percentage error of FAPAR_FVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPAR_LAI and FAPAR_MOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPAR_FVC became more pronounced with increasing FVC compared to FAPAR_LAI. The findings suggested that the FAPAR_FVC 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.

In this study, soil and rice samples from 85 sites in six cities in Hunan Province were analyzed for Cu, Zn, Pb, Cd, Hg, Mn, and Co (total and bioavailable concentrations for soil) in July 2014. The results indicated that the total concentrations of Cu, Zn, Pb, Cd, and Hg in soil had increased significantly compared with the 1980s, and were correlated with their bioavailable concentrations in soil positively. The total concentrations of Cd and Co in soil were correlated with those in rice. Bioavailable concentrations of Cd, Mn, Co, Pb, and Cu represented 64.4%, 33.2%, 12.0%, 11.6%, and 6.1% of the total soil concentrations, respectively. The bioavailable concentrations of Cd and Co in soil had a extremely significant (p < 0.01) positive correlation with those in rice, suggesting that bioavailable concentrations was a better indicator for soil potentially toxic elements contamination. The pH values had a significant influence on the bioavailability of Cd and Cu and the amounts taken up by rice. The Cd contamination in 27.0% rice samples exceeded World Health Organization recommended thresholds. The results added basic pollution distribution data, further revealing the relationships of metals in soil and crops and would offer great help to the metallic pollution control in these areas.

Spatio-temporally continuous and high-quality soil moisture (SM) is very important for assessing changes in the water cycle and climate, especially over the Tibetan Plateau (TP). Data fusion is an important method to improve the quality of SM product. However, limited observation overlaps between different satellite SM products, caused by inherent gaps, make it difficult to fuse them to create a continuous and high-quality product. In this study, a SM spatio-temporal continuity and quality simultaneously improving algorithm is proposed. The first step of the approach is obtaining spatio-temporally continuous reference data, including land surface temperature (LST), normalized difference vegetation index (NDVI), Albedo, and digital elevation model (DEM). The second step is training the general regression neural network (GRNN) model with all available Essential Climate Variables (ECV) and Fengyun (FY) SM. The last step is predicting the spatio-temporally continuous and high-quality SM using the trained GRNN derived by the spatio-temporal continuity reference data. An implementation of the algorithm on the TP showed that, compared with the original ECV and FY SM, both the continuity and quality of the fused SM product were largely improved in terms of coverage (72.5%), correlation (R=0.809), root mean square error (0.081 cm3 cm-3) and bias (0.050 cm3 cm-3). The algorithm showed a good performance in obtaining spatio-temporal variation fusion weights over the TP. This spatio-temporally continuous and high-quality SM of the TP will help advance our understanding of global and regional changes in water cycle and climate.