Leaf chlorophyll content (LCC) is an important physiological indicator of the actual health status of individual plants. An accurate estimation of LCC can therefore provide valuable information for precision field management. Red-edge information from hyperspectral data has been widely used to estimate crop LCC. However, after the advent of red-edge bands in satellite imagery, no systematic evaluation of the performance of satellite data has been conducted. Toward this end, we analyze herein the performance of winter wheat LCC retrieval of currant and forthcoming satellites (RapidEye, Sentinel-2 and EnMAP) and their new red-edge bands by using partial least squares regression (PLSR) and a vegetation-indexbased approach. These satellite spectral data were obtained by resampling ground-measured hyperspectral data under various field conditions and according to specific spectral response functions and spectral resolution. The results showed: 1) This study confirmed that RapidEye, Sentinel-2 and EnMAP data are suitable for winter wheat LCC retrieval. For the PLSR approach, Sentinel-2 data provided more accurate estimates of LCC (R²=0.755, 0.844, 0.805 for 2002, 2010, and 2002+2010) than do RapidEye data (R²=0.689, 0.710, 0.707 for 2002, 2010, and 2002+2010) and EnMAP data (R²=0.735, 0.867, 0.771 for 2002, 2010, and 2002+2010). For index-based approaches, the MERIS terrestrial chlorophyll index, which is a vegetation index with two red-edge bands, was the most sensitive and robust index for LCC for both the Sentinel-2 and EnMAP data (R²≥0.628), and the indices (NDRE1, SRRE1 and CIRE1) with a single red-edge band were the most sensitive and robust indices for the RapidEye data (R²≥0.420); 2) According to the analysis of the effect of the wavelength and number of used red-edge spectral bands on LCC retrieval, the short-wavelength red-edge bands (from 699 to 734 nm) provided more accurate predictions when using the PLSR approach, whereas the long-wavelength red-edge bands (740 to 783 nm) gave more accurate predictions when using the vegetation indice (VI) approach. In addition, the prediction accuracy of RapidEye, Sentinel-2 and EnMAP data was improved gradually because of more number of red-edge bands and higher spectral resolution; VI regression models that contain a single or multiple red-edge bands provided more accurate predictions of LCC than those without red-edge bands, but for normalized difference vegetation index (NDVI)-, simple ratio (SR)- and chlorophyll index (CI)-like index, two red-edge bands index didn’t significantly improve the predictive accuracy of LCC than those indices with a single red-edge band. Although satellite data with higher spectral resolution and a greater number of red-edge bands marginally improve the accuracy of estimates of crop LCC, the level of this improvement remains insufficient because of higher spectral resolution, which results in a worse signal-to-noise ratio. The results of this study are helpful to accurately monitor LCC of winter wheat in large-area and provide some valuable advice for design of red-edge spectral bands of satellite sensor in future.

Received  24 October, 2017    Accepted  29 January, 2018

Endothelial PAS domain protein 1 (EPAS1), also called hypoxia-inducible factor-2, is a key regulatory factor of hypoxic responses and plays an essential role in high-altitude adaptation in mammalian species. In this study, polymorphisms of EPAS1 were detected in 217 individuals from 2 Tibetan pig populations and 3 low-altitude pig breeds by DNA pooling, PCR-SSCP, PCR-RFLP and DNA sequencing methods. A total of 14 synonymous polymorphisms were identified in the coding region. The analysis suggested that SNP1 (G963A), SNP7 (C1632T), SNP10 (G1929A) and SNP11 (G1947A) showed potential association with high-altitude environment because of their particular variation patterns in Tibetan pigs. Linkage disequilibrium (LD) of these SNPs was analyzed. One common LD block including 5 SNPs clustering in exon 12 was identified in all studied pig populations. Haplotype H1 (AGGTC) in LD block was dominant in Tibetan pigs (76.6 and 74.2% in Linzhi (LZ) and Chayu (CY) pigs, respectively) and segregated at higher frequency than that in low-altitude pig breeds (52.3, 58.7 and 56.2% in Wuzhishan (WZS), Min (M) and Laiwu (LW) pigs, respectively), indicating that H1 may relate to adaptation to high altitude in Tibetan pigs. These findings raise hope that EPAS1 gene can be a candidate gene that involved in adaptation of high altitude in Tibetan pigs.