ALBERT

Beschreibung: The file includes both field measured and satellite derived high resolution LAI data obtained over the Honghe farm and Hailun site in northeastern China. The Honghe farm (centered at 47°39′N, 133°31′E) is located in the east of the Heilongjiang province, northeast China. Five plots in 400 m × 600 m were selected in the Honghe farm in 2012 and 2013. Within each plot, about 50 - 60 elementary sampling units (ESUs) about 20 m ×20 m in size were selected in different weeks with a moving sampling strategy to avoid the sampling disturbance. Field LAI measurements were performed weekly from June 11 to September 17, 2012, and from June 22 to August 27, 2013. All ESU measurements made with LAI-2200 within a plot were averaged to represent the plot LAI. The Hailun site (47°24′- 47°26′N, 126°47′- 126°51′E) is located in the western part of the Heilongjiang province. The main crop types are maize, soybean, and sorghum. Five crop plots in 100 m × 500 m were chosen for continuous LAI measurements. The plots cover an areas of about 30 km2 and an elevation of approximately 200-240 m above sea level with quite homogeneous surroundings. Three representative ESUs of approximately 20 m × 20 m were selected in each plot. Field LAI measurements were continuously carried out with LAI-2200 weekly at each plot from June 20 to September 22, 2016. The high-resolution LAI data were estimated with a look-up table (LUT) method from the HJ-1, Landsat 7 ETM+, and Sentinel-2A MSI reflectance data. The high resolution LAI data are consistent with the field measured LAI characterized by a slope close to the 1:1 line. The statistical results show R2 of 0.81 and 0.86, and RMSE of 0.62 and 0.70 for paddy rice and broadleaf crops, respectively. The scale factor is 0.01.

Beschreibung: 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).