The South China Sea (SCS) is one of the largest marginal sea in southeast Asia. The northern continental margin of the SCS has been considered an intermediate between the magma-poor and magma-rich end members. Baiyun sag is located in the deep water area where the northern continental crust had been hyper-thinned to < 12km. The syn-rifting deposition in the Baiyun sag shows distinct features to a classic rift basin at the shallow water area. Based on the high-resolution 3D seismic data interpretation and drilling logging analysis, we interpret a structural sequence corresponding to the hyper-thinning process before the final breakup of the continent. This sequence is bounded by a classic breakup unconformity and a regional transgression surface in the syn-rift strata of the Baiyun Sag; within the sequence, a set of foreset reflectors suggesting a prograding shelf-margin delta. Analog to the Iberia margin of North Atlantic, we use the definitions of Lithospheric Breakup Surface (LBS) and Breakup sequence (BS) and determine the distributions of the LBS and BS in the Baiyun sag. Based on the logging results, we apply subsidence back stripping to recover the palaeogeomorphology background with the development of BS. Finally, based on isostasy, we restore the basin structure using the balanced section technique and analyze the controlling factor of coarse-grained deposition distribution. Our results refine the deposition process related to the hyper-extension and the final breakup of the continental margin.

Fire is an essential global phenomenon that existed soon after the appearance of terrestrial plants and is vital for the regeneration of the plant species. Human activities have contributed to a changing climate and impacted fire regimes, resulting in more intense, frequent and severe fires. In particular, the 2019-20 bushfires in south-eastern Australia were unprecedented in their extent and intensity. However, human activities can also play a dominant role in regulating fire behaviour effectively through better fire management practices. In Northern Australia, indigenous fire managers use prescribed burns during the early dry season to prevent large late dry season fires, which shifts the overall temporal distribution of fire activity earlier during the primary biomass burning season. This increasing trend of prescribed burns has helped to significantly reduce the size and extent of the intense late dry season fires, indicating that such fire management practices can be effective at managing wildfires in savannas. Biomass burning can emit many chemical species that have an impact on human health. One of the most abundant and widely measured is carbon monoxide (CO), whose long-term exposure can lead to potential human health risk. CO is also a good proxy for emissions of other shorter-lived and harder-to-measure atmospheric constituents. This study is focussed on understanding how the earlier fire season in Northern Australia impacts the temporal shift in annual cycle of CO. Column CO data from the ground-based Total Carbon Column Observing Network site in Darwin will be used together with surface measurements, complemented by the surface mixing ratio observations from MOPITT, in order to disentangle the CO emitted from the study region from that measured in the column from remote emissions coupled with long-range transport. GEOS-Chem CO tagged tracer modelling capability will be used to better understand the effect of local fire emissions on the surface and column CO.

Water stress mapping in crops and its spatial disparity study at field scale is important for precise management of irrigation. Results obtained from conventional airborne practice (balloons, airplanes, and satellites) are less acceptable for timely irrigation management due to lack in spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with multispectral (MS) and thermal cameras with higher spectral and temporal resolutions can be used as a promising tool for preparing water stress maps under different water deficit conditions. In this study, Water Deficit Index (WDI) maps are generated at different days after sowing (DAS) in wheat crops under three different water conditions (WI (well water), WS1(irrigation at 5 days’ interval), and WS2 (irrigation at 11 days’ interval)) using the concept of Vegetation Index Trapezoid (VIT) using UAV based thermal and MS imageries. The UAV is flown at 60m altitude during the Rabi season 2018-19. After pre-processing of images in Pix4dMapper, nine vegetation indices are calculated from MS images and one of the indices, Normalized Green Red Difference Index (NGRDI) is selected based on the higher correlation with ground truth data (R2 greater than 0.5) and visual interpretation according to the real field condition to construct the VIT. Vegetation index and temperature values are calculated for four points of VIT by using four boundary conditions such as bare soil with (1) dry and (2) wet conditions, and full vegetation with (3) well-watered and (4) water stress conditions. By using the ArcGIS, geo-referencing of thermal images with respect to MS images is done to get the exact overlap of both images, and resampling of thermal and MS images are also carried out to get the same pixel size. WDI values are estimated using VIT of the surface-air temperature difference and NGRDI, and WDI maps are generated from the UAV-based thermal and MS imageries for potential detection of crop water stress. The conventional Crop Water Stress Index (CWSI) which is solely based on the crop canopy temperature is outperformed by the WDI, which is integration of composite land surface temperature (LST) and degree of greenness, and could be effective enough for irrigation water management. Keywords: UAV, Multispectral and Thermal imageries, NGRDI, WDI, and Wheat crop.

Evaluation of spatially distributed crop coefficient (Kc) for estimating evapotranspiration (ETc) based on remotely sensed imagery has become an essential topic in managing the demand for agricultural water. Currently, satellite (MODIS, Landsat, etc.) imageries are not insufficient to detect variability within the small agricultural field due to its lack of desired spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with various sensors like Multispectral (MS), Thermal, and Hyperspectral cameras is becoming an emerging technology to overcome these limitations over small agricultural fields. A field experiment is carried out in the Agricultural and Food Engineering (AGFE) Department, IIT Kharagpur, to estimate Kc over the small Agri. Field using UAV-based MS cameras during Kharif (monsoon) 2019-2020 season. Lysimeters are used for estimating daily ETc for conventionally irrigated paddy crops. Reference evapotranspiration (ET0) is also calculated using the weather data of the study area. High-resolution multispectral imageries are acquired using a quad-copter UAV. The imageries are pre-processed using Pix4Dmapper software, and various vegetation indices (such as NDVI, TNDVI, NDRE, RVI, GNDVI, and LCI) are evaluated. The vegetation indices (VIs) are correlated with ground truth Kc values and spatially distributed Kc maps for the whole study area are generated based upon the excellent correlation between the VIs and ground Kc. The spatial Kc maps clearly show the variation in Kc within the plots and will be helpful for the calculation of Kc for any field without a lysimeter experiment. Generated Kc maps describe the crop water demand by visual color variations within the field. This approach may be helpful in understanding the variability in crop water requirements within the field Keywords: UAV, Crop Coefficient (Kc), Crop Evapotranspiration (ETc), Vegetation Indices, Remote Sensing.