Haowei Zhang

Compared to single-band spaceborne lidars such as the Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), multi-band spaceborne lidars improve detection of the canopy and ground. However, research on geographic elevation inversion with multi-band spaceborne lidars is limited, especially in developing algorithms that fully utilize multiple wavelengths for accurate measurements. A high-precision Multi-Band Fusion Algorithm (MBFA) is proposed for global geographic elevation inversion for multi-band spaceborne lidar of China's Daqi-1 satellite (DQ-1), enhancing the ranging capability of the 1572 nm channel by approximately 5 times. Compared with ICESat-2, GEDI and airborne scanning data in Finland, the geographic elevation results of MBFA showed average biases of -0.09, 0.1 and -0.95 m, with Root Mean Square Errors (RMSE) of 3.68, 4.51 and 7.70 m, respectively. Accurate forest canopy heights can be obtained using the decomposed signal approach in MBFA, which has been verified in Finland. The validation results (R² = 0.72, RMSE = 1.38m, BIAS = -0.75m) demonstrate the DQ-1 satellite's effectiveness in measuring canopy height. The results indicate that the MBFA model has potential for global forest canopy height extraction and carbon sink research. The proposed MBFA can also provide guide for high-precision inversion of future multi-band lidar satellites.

On June 6, 2023, the Kakhovka Dam in Ukraine was damaged, causing a flood that could have had a significant impact on society. A single satellite may not provide sufficient data for timely disaster response. To overcome this, a combination of optical images, new generation of hydrological monitoring satellites data and nighttime light data were employed to analyze the disaster. Sentinel-3 provides useful hydrological information for quickly identifying and locating disaster areas, while SWOT is able to detect changes in water surface elevation, providing a direct view of the flood's impact on the water area and surface elevation. The datasets both provide information about the extent of the flood area, which is more detailed than that provided by a single source. Furthermore, the NPP-VIIRS data not only reflects the indirect impact of the disaster on the lives and production of the local population, but also provides an intuitive assessment of the damage to the Zaporizhzhya nuclear power plant's power supply. The data showed that the disaster affected an area of about 8 km on either side of the river downstream of the dam. This enables the prediction of the damage and the post-disaster reconstruction strategy from a humanistic perspective. The combination of these three data types enables the specific impact of the disaster to be gauged in terms of its scope, extent, impact on human life and the post-disaster recovery situation. This provides scientific reference for the timely formulation and implementation of disaster relief and post-disaster reconstruction measures.

The Surface Water and Ocean Topography (SWOT) satellite, carrying a Ka-band Radar Interferometer, is designed to detect global hydrological, ecological, and climatic changes through high-precision measurements of water elevation level and to promote the sustainable use and conservation of water resources. The accuracy of water level elevations of inland from SWOT was verified globally in this paper by comparing SWOT data with Hydroweb and G-REALM data, so as to better utilize the advanced global remote sensing observation capabilities of SWOT in hydrology. Through spatiotemporal matching validation, this experiment validates the SWOT data by time series within different regions, confirming that SWOT observations have an accuracy of more than 99% for inland lakes and rivers globally in eight geographic subregions. Compared with Hydroweb and G-REALM, SWOT's lake products have an MAE of less than 0.5 and 0.3 m, as well as an RMSE of less than 1.5 and 2 m, respectively. SWOT's data reduce the error in the measurement of the lake by more than 0.1 m compared with the priori data and improves the accuracy by more than 10%. As for river measurements, SWOT's global average measurement error is less than 0.15 m, with an RMSE of less than 1.5 m, providing highly accurate measurements for rivers in most regions of the world. Overall, the product of SWOT can provide high-precision heights of terrestrial water bodies, which is of great significance for small inland water body measurements, global water quantity monitoring, and water circulation research.

Carbon observation satellites based on passive theory (e.g., OCO-2/3, GOSAT-1/2, and TanSat) have relatively high carbon dioxide column concentration (XCO ₂ ) accuracy when the observation conditions are met. Passive satellites have data bias and coverage deficiencies due to cloud cover, low albedo, low light conditions, and aerosol scattering, resulting in carbon observation satellites based on passive theory that cannot meet the demand for high-precision, all-day, all-weather XCO ₂ monitoring. Active detection satellites are urgently needed to support global carbon sources, sinks, and carbon neutrality. China intends to launch a sensor satellite with active detection of XCO ₂ in the coming years. In this work, based on the satellite’s scaled-down airborne experiments, a spectral energy model was developed to optimize the conventional inversion algorithm and achieve a more accurate XCO ₂ inversion. The 1.572 μm IPDA lidar column length is used indirectly to evaluate the accuracy of the spectral energy model for signal extraction. And the experimental results show that the accuracy of the signal extracted by the 1.572 μm IPDA lidar column length is 0.74 and 6.20 m at sea and on land based on the indirect evaluation of the length of the 1.572 μm IPDA lidar column length. The optimized XCO ₂ was evaluated (standard deviation as an evaluation metric) and its XCO ₂ standard deviation reduced by 31%, 63% and 66% in the ocean, plains and mountains, respectively. Our algorithm can obtain the XCO ₂ with a consistent trend by using XCO ₂ from the OCO-2 satellite as a reference. The calculated XCO ₂ is more accurate in areas dominated by anthropogenic factors (plains), owing to the accuracy of the IPDA detection mechanism. This algorithm improves the accuracy and robustness of XCO ₂ inversion and has important reference significance for the IPDA lidar carried by China’s satellites to be launched in this year.

Rapid detection of oil tank targets has become a topic of significant and increasing interest because the quick acquisition of the distribution and volume of oil storage tanks has high economic and military value. However, research in the literature still faces many challenges in different scenarios. This letter presents a new oil tank detection approach for improved EfficientDet model. The proposed approach has three primary operations: 1) first, adding 3-D deformable convolution to the EfficientDet model as predetection to limit detection in the smaller area; 2) second, loading an attention mechanism appropriate for oil tank detection on the myNet model highlights tiny target information; and 3) lastly, training the myNet model repeatedly by focal loss function to seek better results. The result shows that the overall accuracy of mean average precision in the proposed approach increased by at least 8.25% compared with that of the tested conventional approaches. Therefore, it provides advantageous capabilities for monitoring oil tanks in high-resolution remote sensing imagery.

A novel framework was proposed to measure atmospheric concentration of δ13C using a multi-wavelength integrated path differential absorption (IPDA) LIDAR. The spectroscopy range of the multi-wavelength IPDA LIDAR is recommended as from 2264.5 to 2265.5 cm-1. Using the proposed retrieving method, the relative error of δ13C retrievals would be within 0.16 % under reasonable settings. Moreover, the proposed method shows reliable performances in different circumstances. It would be of great significance for exploring the characteristic of δ13C in ecosystem and anthropogenic emissions in future.

In recent years, as China’s peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO₂, O₃, and CO₂ column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO₂ decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO₂, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO₂ and O₃ concentrations on changes in XCO₂. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO₂ concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O₃ concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO₂ concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O₃ concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO₂ concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O₃ concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO₂. Moreover, the results show that the effect of NO₂ concentration on XCO₂ is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO₂ and the O₃ concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan’s carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan’s XCO₂ fell by about 1.12 ppm in February. At last, the government should consider reducing XCO₂ and NO₂ concentration at the same time to make a synergistic reduction.