Mapping flash flood susceptibility is effective for mitigating the negative impacts of flash floods. However, a variety of conditioning factors have been used to generate susceptibility maps in various studies. In this study, we proposed combining logistic regression (LR) and random forest (RF) models with embedded feature selection (EFS) to filter specific feature sets for the two models and map flash flood susceptibility in the mainstream basin of the Songhua River. According to the EFS results, the optimized feature sets included 32 and 28 features for the LR and RF models, respectively, and the composition of the two optimal feature sets was similar and distinct. Overall, the relevant vegetation cover and river features exhibit relatively high effects overall for flash floods in the study area. The LR and RF models provided accurate and reliable flash flood susceptibility maps (FFSMs). The RF model (accuracy = 0.8834, area under the curve (AUC) = 0.9486) provided a better prediction capacity than the LR model (accuracy = 0.8634, AUC = 0.9277). Flash flood-prone areas are mainly distributed in the south and southwest and areas close to rivers. The results obtained in this study is useful for flash flood prevention and control projects.

Accurate estimates of aboveground biomass (AGB) are valuable for monitoring forest degradation and carbon stocks on Earth. However, the validity of multiple data types and diverse data combinations for AGB estimation is unclear. In this study, recursive feature elimination (RFE) combined with machine-learning regression models for AGB were developed using field data and multi-source remote sensing data, which included Sentinel-1, Sentinel-2, PALSAR, and DEM. The spatial distribution of AGB was mapped for the Daxing’anling region in the northernmost part of China at 30m resolution. We compared the ability of multiple data combinations to perform AGB estimation and found that using all four types of data combinations resulted in the highest estimation accuracy with fewer predictors. The combination of diverse data sources substantiates enhancements in the precision of AGB estimation, surpassing the utilization of singular or dual sensor modalities. In addition to the optical remote sensing data sentinel-2, topographic data has a non-negligible role in the AGB estimation in this study, even more than microwave remote sensing data. Finally, the extreme gradient boosting model (R ² =0.67, RMSE=22.57 Mg/ha) based on the combination of all four data types had the highest accuracy and mapped the AGB of the study area. The results indicate that the AGB can be estimated with reasonable accuracy for the boreal forest region based on publicly available multi-source remote sensing data. This study proposes diverse data combinations as well as derived variables for AGB estimation, aiming to explore the possibilities of more remote sensing data in AGB studies.

Solar-induced chlorophyll fluorescence (SIF), when used as a proxy for plant photosynthesis, can provide an indication of the photosynthesis rate and has the potential to improve our understanding of carbon exchange mechanisms within an ecosystem. However, the relationships between SIF and vegetation indices (VIs) operating within different ecological contexts and the effect of other environmental factors on SIF remain unclear. This study focused on three ecosystems (cropland, forest, and grassland), with different ecological characteristics, located in Northeast China. These areas provide case studies where numerous relationships can be explored, including the correlations between the Orbiting Carbon Observatory-2 (OCO-2) SIF and MODIS products, meteorological factors, and the differences in the relationships between the three different ecosystems. Some interesting results and conclusions were obtained. First, in different ecosystems, the relationships between SIF and MODIS products show different correlations, whereby the enhanced vegetation index (EVI) has a close relationship with SIF in all the three ecosystems of forest, cropland, and grassland. Second, forest-type ecosystems appear to be sensitive to changes in daily temperature, whereas cropland and grassland areas respond more closely to changes in previous 16-day daily minimum temperature. Compared with forest and cropland areas, grasslands were more sensitive to precipitation (although the R² value was small). Third, different ecosystems have different mechanisms of photosynthesis. Hence, we suggest that it is better to use SIF in areas exhibiting different ecological characteristics, and different models should be employed while simulating SIF.

Peatlands, which constitute only 3% of Earth’s land surface, store an estimated 612 Pg of carbon. Due to escalating warming and anthropogenic impacts, numerous peatlands undergo degradation, releasing stored carbon into the atmosphere and exacerbating global warming. The accurate delineation of the extents and boundaries of peatlands is imperative to mitigate peatland degradation, but it also remains a considerable challenge. This study developed a peatland machine-learning classification model using an object-based random forest (RF) algorithm, comprehensive spatiotemporal features, and multisource remote sensing data, including Sentinel-1, Sentinel-2 imagery, and digital elevation model (DEM). A high-resolution peatland map (10 m) was generated for the Changbai Mountains (CBM) in northeastern China. The results show that spectral indices from May play a particularly significant role in enhancing the accuracy of the classification. The study achieved an overall accuracy (OA) exceeding 94%, with the classification accuracy for the peatland category surpassing 91%. The analysis revealed that peatlands cover approximately 1197.09 km2 in the area, with over 80% situated below 1000 m a.s.l., around 70% on slopes less than 4°, and more than 80% within 5 km of a water body. This study proposes the novel methods for selecting feature variables in classification, aiming to enhance the accuracy of peatland mapping, thus providing critical insights and a benchmark for future refined mapping efforts. The results from this mapping endeavor hold substantial implications for the conservation and management of peatlands.