There is currently no unified remote sensing system available that can simultaneously produce images with fine spatial, temporal, and spectral resolutions. This letter proposes a unified spatiotemporal spectral blending model using Landsat Enhanced Thematic Mapper Plus and Moderate Resolution Imaging Spectroradiometer images to predict synthetic daily Landsat-like data with a 15-m resolution. The results of tests using both simulated and actual data over the Poyang Lake Nature Reserve show that the model can accurately capture the general trend of changes for the predicted period and can enhance the spatial resolution of the data, while at the same time preserving the original spectral information. The proposed model is also applied to improve land cover classification accuracy. The application in Wuhan, Hubei Province shows that the overall classification accuracy is markedly improved. With the integration of dense temporal characteristics, the user and producer accuracies for land cover types are also improved.
Wetlands act as persistent natural carbon sinks over long time scales. Understanding the response of these carbon reservoirs to climate change is critical to assessing potential climate feedbacks. We conducted a study of an 860‐cm‐long sediment core in Dahu Swamp in south China to determine how the carbon accumulation rate ( CAR ) has varied as a function of palaeohydrology and palaeoclimate over the past 47 000 years. From an orbital time scale, our results show that the CAR in Dahu Swamp is relatively low in the wet periods of Marine Isotope Stage 3 (MIS 3) (mean: 46.7 gC m −2 a −1 ) and MIS 1 (mean: 28.2 gC m −2 a −1 ), compared to the dry periods of MIS 2 (mean: 59.9 gC m −2 a −1 ). At centennial and millennial scales, the highest CARs of Dahu Swamp mainly occur in organic‐rich silt or clay (gyttja) layers, which correspond to the relatively dry climate (e.g. c . 48 000–41 000, c. 33 000–32 000, c. 15 800–14 900 and c. 4400–4250 cal. a BP ). The CAR of Dahu Swamp is mainly controlled by local hydrological variations that are closely related to the East Asian summer monsoon ( EASM ) intensity, which may be co‐influenced by orbitally induced summer insolation forcing and internal feedback processes (e.g. Atlantic Meridional Overturning Circulation and El Niño/Southern Oscillation). Based on comparison with the CARs in monsoonal regions of China, we consider that precipitation may be the key factor for wetland CAR in EASM areas, whereas temperature is more important in Qinghai–Tibetan Plateau regions under Indian summer monsoon influence. The CAR of Dahu Swamp provides valuable records of wetland carbon accumulation dynamics in subtropical monsoon regions, which contradict the traditional patterns in global northern wetlands.
With decreasing water availability as a result of climate change and human activities, analysis of the influential factors and variation trends of chlorophyll a has become important to prevent reservoir eutrophication and ensure water supply safety. In this paper, a structurally simplified hybrid model of the genetic algorithm (GA) and the support vector machine (SVM) was developed for the prediction of monthly concentration of chlorophyll a in the Miyun Reservoir of northern China over the period from 2000 to 2010. Based on the influence factor analysis, the four most relevant influence factors of chlorophyll a (i.e., total phosphorus, total nitrogen, permanganate index, and reservoir storage) were extracted using the method of feature selection with the GA, which simplified the model structure, making it more practical and efficient for environmental management. The results showed that the developed simplified GA-SVM model could solve nonlinear problems of complex system, and was suitable for the simulation and prediction of chlorophyll a with better performance in accuracy and efficiency in the Miyun Reservoir.
Mechanisms for the enrichment and re-precipitation of gold in the giant Jiaodong gold deposits (eastern North China Craton) remain poorly constrained. To better understand the mineralization mechanism, we did in situ analyses of S isotopes on sulfides such as pyrite, pyrrhotite, galena and chalcopyrite from the disseminated (altered-rock type) and quartz-vein type gold deposits by femtosecond laser ablation coupled multi-collector inductively coupled plasma mass spectrometry. Pyrites from the altered-rock type gold deposit show δ34S values in the range from 7.4 to 11.3 ‰, which is obviously heavier than the quartz-vein type gold deposits with δ34S = 6.2 ∼ 8.8 ‰. Traditionally, the difference of sulfur isotopic compositions between the two types of gold deposits was attributed to the change in oxygen fugacity. However, we found that, from early to late metallogenic stage, sulfur isotopes of pyrites from the altered rock type gold deposits tend to decrease gradually and pyrrhotites can always be observed in the third stage. Moreover, the S isotopic compositions (δ34S = 7.9 to 8.2 ‰) of the pyrites coexisting with magnetite are comparable with those (δ34S = 6.2 to 8.0 ‰) of the pyrites coexisting with pyrrhotite in the quartz vein type gold deposits. These features indicate that the decrease of sulfur isotopes in pyrites was not caused by increase of oxygen fugacity. We suggest that the S isotopic and fO2 variation could be ascribed to an increase of pH of the ore-forming fluid, which is supported by the typically quartz dissolution and common occurrence of calcite and pyrrhotite in the late metallogenic stage (the third stage) and an overall decrease of aluminum contents of quartz from core to rim. We further proposed that the variation of pH of ore-forming fluids is probably related to a process of decompression due to development and enlargement of fractures filled with ore-forming fluids. Gold enrichment in the main ore-forming stage of the northwest Jiaodong gold deposit probably was realized by multiple phases of fluid pressure fluctuation, which subsequently led to repeatedly dissolution and re-precipitation of Au from pyrites due to decreasing oxygen fugacity and increasing pH values of the ore-forming fluids.
Long-term record of fine spatial resolution remote sensing datasets is critical for monitoring and understanding global environmental change, especially with regard to fine scale processes. However, existing freely available global land surface observations are limited by medium to coarse resolutions (e.g., 30 m Landsat) or short time spans (e.g., five years for 10 m Sentinel-2). Here we developed a feature-level data fusion framework using a generative adversarial network (GAN), a deep learning technique, to leverage the overlapping Landsat and Sentinel-2 observations during 2016–2019, and reconstruct 10 m Sentinel-2 like imagery from 30 m historical Landsat archives. Our tests with both simulated data and actual Landsat/Sentinel-2 imagery showed that the GAN-based fusion method could accurately reconstruct synthetic Landsat data at an effective resolution very close to that of the real Sentinel-2 observations. We applied the GAN-based model to two dynamic systems: (1) land over dynamics including phenology change, cropping rotation, and water inundation; and (2) human landscape changes such as airport construction, coastal expansion, and urbanization, via historical reconstruction of 10 m Landsat observations from 1985 to 2018. The resulting comparison further validated the robustness and efficiency of our proposed framework. Our pilot study demonstrated the promise of transforming 30 m historical Landsat data into a 10 m Sentinel-2-like archive with advanced data fusion. This will enhance Landsat and Sentinel-2 data science, facilitate higher resolution land cover and land use monitoring, and global change research.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(144 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(216 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(288 450 300)'/%3e%3c/svg%3e)