ALBERT

Description: The dataset includes global specific vegetation cover (SVC), base clumping index (BCI), full clumping index (FCI), and leaf projection function (G) derived from clumping index (CI), leaf area index (LAI), and fractional vegetation cover (FVC) remote sensing products. The SVC, defined as the ratio of FVC to LAI, was proposed to characterize the ability of vegetation to cover the ground and has great potential for vegetation characterization and phenology studies. In this dataset, the global monthly SVC was generated with FVC and LAI products from 2003–2017. Theoretically, SVC varies from 0 to 1. SVC 〉1.0 reveals inconsistent retrievals for FVC and LAI. Therefore, we also map the spatial distribution and frequency of SVC outying pixels based on above monthly SVC product. The BCI refers to the hypothetical minimum CI during leaf emergence when both the FVC and LAI are close to zero. The FCI represents the CI when the ground is completely covered by vegetation (FVC=1.0) or the pixel LAI reaches its maximum (assumed to be 7.0). The BCI and FCI values indicate the seasonal CI variations and would greatly facilitate canopy modeling and parameter retrieval studies. The global BCI and FCI with a spatial resolution of 0.05° were both estimated using the exponential relationships between CI and FVC or between CI and LAI, respectively. The nadir leaf projection function (G(0)) is defined as the average projection of the unit leaf area in the nadir direction. The global monthly G(0) maps at 0.05° spatial resolution were generated for the first time from the global CI, FVC, and LAI products based on the Beer-Lambert equation under the assumption that the whole CI can be approximated as nadir CI. It can be used as a benchmark for biophysical parameter retrieval and land surface modeling studies. The remote sensing products used for generating this dataset include the CAS-CI V1.1 (Wei et al., 2019), the GEOV2 FVC (Verger, A., 2019; https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_ATBD_LAI1km-V2_I1.41.pdf), and the MODIS LAI C6 (Myneni et al., 2015). In order to facilitate further analysis by users, the global monthly average CI, FVC, and LAI data at 0.05° are also provided in this dataset. Moreover, we share the statistical results about the variations of CI, FVC, LAI, and SVC with seasonal, latitude, and altitude. For more details about this dataset, please refer to (Fang et al. (2021) do:10.1016/j.srs.2021.100027).

Description: Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.