To provide high-quality data for time-series change detection of lake water level, an automatic and robust algorithm for lake water footprint (LWF) identification is developed. Based on the Ice, Cloud, and Land Elevation Satellite GLA14 data file, six parameters were taken as features of an algorithm for LWF identification, and they are elevation difference between adjacent footprints, waveform width, number of peaks, reflectivity, kurtosis, and skewness of laser echoes. The sensitivity of each parameter was discussed, and elevation difference between adjacent footprints was proved to be most effective. The algorithm was described as a combination of these six parameters, and the thresholds of each parameter were set through statistics of LWF covering Peiku Co in Tibet, China, from 2003 to 2009. The performance of this classification algorithm was evaluated by the user's accuracy and producer's accuracy. Greater than 94% is achieved for all four tested lakes with 97% being the best result of producer's accuracy, and the user's accuracy ranges from 97.9% to 90% for these four lakes.

Circadian clocks exist in all types of organisms and coordinate key biological processes, e.g. photosynthesis in phytoplankton (microalgae) and land plants. We asked whether a circadian rhythm sustains in phytoplankton when living under constant illumination without environmental cues. Here, we report the first transcriptomic architecture of persistent oscillatory gene expression in the model marine diatom, Phaeodactylum tricornutum living under constant illumination and temperature without environmental cues. We show that cyclic expression of a considerable number of genes involved in light harvesting and carbon fixation sustained after 24 hours of constant illumination (free-running), which could pose additional constraints on cell growth under constant light conditions. Over long-term adaptation to constant illumination, the majority of the rhythmic genes identified under diel light conditions lose their oscillatory expression in the absence of external entrainers, and the genes potentially controlled by persistent circadian clocks are primarily involved in transcriptional regulation and cell division. We find constant illumination leads to an increased average expression of transcription factors and cell division genes, while genes involved in the Calvin-Benson cycle and pigment biosynthesis are kept at low expression levels, which plays a role in the down-regulation of photosynthetic efficiency. By manipulation of the dark rest period, we confirm a fine-tuned light/dark cycle could dramatically improve photosynthetic efficiency in microalgae. Our results unveil a novel persistent circadian rhythm on photosynthetic regulation in marine phytoplankton and provide critical insights into the interaction between environmental signals and inheritable internal circadian clocks in diatoms.

Radiometric normalization attempts to normalize the radiomimetic distortion caused by non-land surface-related factors, for example, different atmospheric conditions at image acquisition time and sensor factors, and to improve the radiometric consistency between remote sensing images. Using a remote sensing image and a reference image as a pair is a traditional method of performing radiometric normalization. However, when applied to the radiometric normalization of long time-series of images, this method has two deficiencies: first, different pseudo-invariant features (PIFs)—radiometric characteristics of which do not change with time—are extracted in different pairs of images; and second, when processing an image based on a reference, we can minimize the residual between them, but the residual between temporally adjacent images may induce steep increases and decreases, which may conceal the information contained in the time-series indicators, such as vegetative index. To overcome these two problems, we propose an optimization strategy for radiometric normalization of long time-series of remote sensing images. First, the time-series gray-scale values for a pixel in the near-infrared band are sorted in ascending order and segmented into different parts. Second, the outliers and inliers of the time-series observation are determined using a modified Inflexion Based Cloud Detection (IBCD) method. Third, the variation amplitudes of the PIFs are smaller than for vegetation but larger than for water, and accordingly the PIFs are identified. Last, a novel optimization strategy aimed at minimizing the correction residual between the image to be processed and the images processed previously is adopted to determine the radiometric normalization sequence. Time-series images from the Thematic Mapper onboard Landsat 5 for Hangzhou City are selected for the experiments, and the results suggest that our method can effectively eliminate the radiometric distortion and preserve the variation of vegetation in the time-series of images. Smoother time-series profiles of gray-scale values and uniform root mean square error distributions can be obtained compared with those of the traditional method, which indicates that our method can obtain better radiometric consistency and normalization performance.