： Land surface temperature (LST) is an important parameter at the 23 land-atmosphere interface. The Collection 6 (C6) MODIS LST products are publicly 24 available. Three refinements were performed over bare soil surfaces in the C6 MODIS 25 LST products when compared with the Collection 5 (C5) MODIS LST products. To 26 facilitate the use of the LST products in a wide range of applications, it is necessary to 27 comprehensively evaluate the accuracies of the C6 MODIS LST products. In this 28 study, we validated the C6 MODIS LST products using the temperature-based method 29 2 over various land cover types, including grassland, cropland, cropland/natural 30 vegetation mosaic, Gobi, sandy dune, and desert steppe. In situ measurements were 31 collected from sites under different atmospheric and surface conditions, including six 32 SURFRAD sites in the United States, two KIT sites in Portugal and Namibia, and four 33 HiWATER sites in China. In general, the accuracies of the C6 MODIS LST products 34 at night are better than those during daytime. The daytime RMSE varies from 35 approximately 1.5 K to 5.6 K, whereas the night-time RMSE is less than 2 K at all 36 sites except for the HiWATER SSW site. Furthermore, the accuracies of the C6 37 MODIS LST products were compared with those of the C5 MODIS LST products 38 over bare soil surfaces. The C6 MODIS LST products are in excellent agreement with 39 the in situ LST measurements at the KIT Gobabeb site, with biases of 0.36 K during 40 the day and 0.24 K at night, and RMSEs of 1.5 K during daytime and 0.74 K during 41 night-time. However, there are no improvements in the accuracies of the C6 MODIS 42 LST products when compared with the C5 MODIS LST products due to further 43 overestimation of emissivities at the four HiWATER sites. 44 45 Key words: Land surface temperature, MODIS, temperature-based validation method, 46 split-window algorithm, in situ measurements. 47 48 1. Introduction 49 Land surface temperature (LST) is an important climate variable, which is related 50 to surface energy and water balance. It is also a key parameter for various studies 51 including hydrology, climatology, environment, and ecology (Anderson et al., 2008; 52

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Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infrared, and mid-infrared reflectance spectroscopy, combined with the partial least squares regression (PLSR) method is considered to be an effective way of determining soil properties. In this study, we used 54 different spectral pretreatments to preprocess soil spectral data. These spectral pretreatments were composed of three denoising methods, six data transformations, and three dimensionality reduction methods. The three denoising methods included no denoising (ND), Savitzky–Golay denoising (SGD), and wavelet packet denoising (WPD). The six data transformations included original spectral data, R; reciprocal, 1/R; logarithmic, log(R); reciprocal logarithmic, log(1/R); first derivative, R’; and first derivative of reciprocal, (1/R)’. The three dimensionality reduction methods included no dimensionality reduction (NDR), sensitive waveband dimensionality reduction (SWDR), and principal component analysis (PCA) dimensionality reduction (PCADR). The processed spectra were then employed to construct PLSR models for predicting the SOM content. The main results were as follows—(1) the wavelet packet denoising (WPD)-R’ and WPD-(1/R)’ data showed stronger correlations with the SOM content. Furthermore, these methods could effectively limit the correlation between the adjacent bands and, thus, prevent “overfitting”. (2) Of the 54 pretreatments investigated, WPD-(1/R)’-PCADR yielded the model with the highest accuracy and stability. (3) For the same denoising method and spectral transformation data, the accuracy of the SOM content estimation model based on SWDR was higher than that of the model based on NDR. Furthermore, the accuracy in the case of PCADR was higher than that for SWDR. (4) Dimensionality reduction was effective in preventing data overfitting. (5) The quality of the spectral data could be improved and the accuracy of the SOM content estimation model could be enhanced effectively, by using some appropriate preprocessing methods (one combining WPD and PCADR in this study).

Lilium is a world famous fragrant bulb flower with high ornamental and economic values, and significant differences in fragrance are found among different Lilium genotypes. In order to explore the mechanism underlying the different fragrances, the floral scents of Lilium ‘Sibeia’, with a strong fragrance, and Lilium ‘Novano’, with a very faint fragrance, were collected in vivo using a dynamic headspace technique. These scents were identified using automated thermal desorption—gas chromatography/mass spectrometry (ATD-GC/MS) at different flowering stages. We used RNA-Seq technique to determine the petal transcriptome at the full-bloom stage and analyzed differentially expressed genes (DEGs) to investigate the molecular mechanism of floral scent biosynthesis. The results showed that a significantly higher amount of Lilium ‘Siberia’ floral scent was released compared with Lilium ‘Novano’. Moreover, monoterpenes played a dominant role in the floral scent of Lilium ‘Siberia’; therefore, it is believed that the different emissions of monoterpenes mainly contributed to the difference in the floral scent between the two Lilium genotypes. Transcriptome sequencing analysis indicated that ~29.24 Gb of raw data were generated and assembled into 124,233 unigenes, of which 35,749 unigenes were annotated. Through a comparison of gene expression between these two Lilium genotypes, 6,496 DEGs were identified. The genes in the terpenoid backbone biosynthesis pathway showed significantly different expression levels. The gene expressions of 1-deoxy-D-xylulose 5-phosphate synthase (DXS), 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR), 4-hydroxy-3-methylbut-2-enyl diphosphate synthase (HDS), 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR), isopentenyl diphosphate isomerase (IDI), and geranyl diphosphate synthase (GPS/GGPS), were upregulated in Lilium ‘Siberia’ compared to Lilium ‘Novano’, and two monoterpene synthase genes, ocimene synthase gene (OCS) and myrcene synthase gene (MYS), were also expressed at higher levels in the tepals of Lilium ‘Siberia’, which was consistent with the monoterpene release amounts. We demonstrated that the high activation levels of the pathways contributed to monoterpene biosynthesis in Lilium ‘Siberia’ resulting in high accumulations and emissions of monoterpenes, which led to the difference in fragrance between these two Lilium genotypes.

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