Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
The concentration data set of persistent organic pollutants in the atmosphere, lake water and fish bodies in Namco from 2012 to 2014 includes concentration time series of atmospheric gaseous organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs), atmospheric gaseous polycyclic aromatic hydrocarbons (PAHs), atmospheric particulate PAHs, dissolved persistent organic pollutants (POPs) in lake water, POPs in suspended particles of lake water and POPs in bodies of Gymnocypris namensis. The contents of the data set are all measured data. (1) The atmospheric samples were collected from the Integrated Observation and Research Station of Multisphere in Namco by the atmospheric active sampler. The flow rate of the sampler is 60 L min-1, which collects data every other day. One sample is generated every half month, and the sampling volume is approximately 600 m³. Each sample includes a glass fiber filter (GFF, 0.45 μm, Whatman) that adsorbs particulate POPs and a polyurethane foam (PUF, 7.5 x 6 cm) that collects gaseous POPs. (2) Fifteen sampling points were selected along Namco to collect surface lake water samples at a water depth of 0-1 m and with a volume of 200 L. The total suspended particulates are obtained by filtering the water samples with a 0.7 μm GFF membrane, and then the dissolved POPs in the water are collected using a solid phase extraction column packed with XAD-2. (3) Gymnocypris namensis is the most widely distributed fish in Namco. A total of 35 samples of different sizes were collected, and the concentration of POPs in the back muscle samples was analyzed. Each medium sample was prepared and analyzed by the Key Laboratory of Tibetan Environment Changes and Land Surface Processes of CAS. The sample preparation steps include Soxhlet extraction, silica-alumina column purification, removal of macromolecular impurities by a GPC column, concentration and constant volume. The analytical test instrument was a gas chromatography-mass spectrometer (GC-MS, Finnigan-Trace GC/PolarisQ) manufactured by American Thermoelectric Corporation. The column separating OCPs and PCBs was a CP-Sil 8CB capillary column (50 m × 0.25 mm × 0.25 μm), and the column separating PAHs was a DB-5MS capillary column (60 m × 0.25 mm × 0.25 μm). Sampling and laboratory analysis procedures followed strict quality control measures with lab blanks and field blanks. The detection limit of the compound is the average of the concentration of the corresponding compound in the field blank plus 3 times the standard deviation; if the compound is not detected in the field blank, the signal-to-noise ratio, 10 times the lowest concentration of the working curve, will be considered as the detection limit. Data below the detection limit are considered undetected and labeled as BDL; data marked in italics are detected by 1/2 times the detection limit. The recovery of PAHs is between 65% and 92%, the recovery of OCPs is between 64% and 112%, and the sample concentration is not corrected using recovery.