Spectral characteristics of CDOM (Chromophoric dissolved organic matter) in water columns are a key parameter for bio-optical modeling. Knowledge of CDOM optical properties and spatial discrepancy based on the relationship between water quality and spectral parameters in the Yinma River watershed with in situ data collected from highly polluted waters are exhibited in this study. Based on the comprehensive index method, the riverine waters showed serious contamination; especially the chemical oxygen demand (COD), iron (Fe), manganese (Mn), mercury (Hg) and dissolved oxygen (DO) were out of range of the contamination warning. Dissolved organic carbon (DOC) and total suspended matter (TSM) with prominent non-homogenizing were significantly high in the riverine waters, but chlorophyll-a (Chl-a) was the opposite. The ternary phase diagram showed that non-algal particle absorption played an important role in total non-water light absorption (>50%) in most sampling locations, and mean contributions of CDOM were 13% and 22% in the summer and autumn, respectively. The analysis of the ratio of absorption at 250–365 nm (E_250:365) and the spectral slope (S_275–295) indicated that CDOM had higher aromaticity and molecular weight in autumn than in summer, which is consistent with the results of water quality and the CDOM relative contribution rate. Redundancy analysis (RDA) indicated that the environmental variables OSM (Organic suspended matter) had a strong correlation with CDOM absorption, followed by heavy metals, e.g., Mn, Hg and Cr⁶⁺. However, for the specific UV absorbance (SUVA₂₅₄), the seasonal values showed opposite results compared with the reported literature. The potential reasons were that more UDOM (uncolored dissolved organic matter) from human sources (wastewater effluent) existed in the waters. Terrigenous inputs simultaneously are in relation to the a_CDOM(440)-DOC relationship with the correlation coefficient of 0.90 in the summer (two-tailed, p < 0.01), and 0.58 in the autumn (two-tailed, p < 0.05). Spatial distribution of the CDOM parameters exhibited that the downstream regions focused on dry land have high CDOM molecular weight and aromatic hydrocarbon. Partial sampling locations around the cities or countries generally showed abnormal values due to terrigenous inputs. As a bio-optical model parameter, the spectral characteristic of CDOM is helpful in adjusting the derived algorithms in highly polluted environments. The study on organic carbon and pollutants in highly polluted waters had an important contribution to global carbon balance estimation and water environment protection.

Frequent droughts and waterlogging events are a threat to food security. An understanding of the spatial and temporal variations that occur during such events is essential when conducting a regional and/or global drought and waterlogging assessment. The goal of this study is to define a comprehensive index that considers the continuum system of atmosphere, crops, and soil moisture impacts on droughts and waterlogging events, and to analyze the temporal and spatial distribution of such events in the development of maize. The results show that the proposed comprehensive drought and waterlogging index (CDWI) can bring together the advantages of a single drought and waterlogging index and reasonably describe its range. During the study period, the annual trends of the CDWI decreased at different growth stages from 1982 to 2015, whereas the CDWI did not show significant spatial heterogeneity during any particular stage. Increasing trends of CDWI over 0.019/year were found in the northern part of Midwestern Jilin Province from the emergence to tasseling stages. In addition, decreasing trends were observed in the study area from the tasseling to maturation stages. Slight drought and waterlogging events occurred more frequently than moderate and serious drought and waterlogging events.

Examining the effects of climate change on spring maize, and its suitability under dynamic cultivation patterns, will aid strategic decision-making for future agricultural adaptation. This paper investigates the climate suitability of spring maize, based on daily data from 50 meteorological stations, and statistics on maize yield and area at the county level in Jilin Province, China, between 1986 and 2015. Based on a significant correlation between the cultivation patterns indicator ≥10 °C accumulated temperature (AAT10) and the average yield (R² = 0.503), the yield data are used to determine suitable thresholds for meteorological factors under the dynamic cultivation pattern, and a fuzzy fitness approach is used to evaluate the climate suitability. The results showed a good agreement between suitability estimates and scaled observed yields (average $d$ = 0.705). Moreover, good consistency between cultivation patterns, climate suitability and yield show that the late-maturing varieties of maize have gradually moved northward and eastward, and the areas of high suitability and high yield have gradually expanded eastward. In addition, drought and chilling hazard factors limit the suitability of climate resources, especially in the eastern and western regions.

Landslide identification is an increasingly important research topic in remote sensing and the study of natural hazards. It is essential for hazard prevention, mitigation, and vulnerability assessments. Despite great efforts over the past few years, its accuracy and efficiency can be further improved. Thus, this study combines the two most popular approaches: susceptibility analysis and change detection thresholding, to derive a landslide identification method employing novel identification criteria. Through a quantitative evaluation of the proposed method and masked change detection thresholding method, the proposed method exhibits improved accuracy to some extent. Our susceptibility-based change detection thresholding method has the following benefits: (1) it is a semi-automatic landslide identification method that effectively integrates a pixel-based approach with an object-oriented image analysis approach to achieve more precise landslide identification; (2) integration of the change detection result with the susceptibility analysis result represents a novel approach in the landslide identification research field.

Drought vulnerability analysis of crops can build a bridge between hazard factors and disasters and become the main tool to mitigate the impact of drought. However, the resulting disagreement about the appropriate definition of vulnerability is a frequent cause for misunderstanding and a challenge for attempts to develop formal models of vulnerability. This paper presents a generally applicable conceptual framework of vulnerability that combines a nomenclature of vulnerable situations and a terminology of vulnerability based on the definition in the intergovernmental panel on climate change (IPCC) report. By selecting 10 indicators, the drought disaster vulnerability assessment model is established from four aspects. In order to verify our model, we present a case study of maize drought vulnerability in the Midwest of the Jilin Province. Our analysis reveals the relationship between each single factor evaluation indicator and drought vulnerability, as well as each indicator to every other indicator. The results show that the drought disturbing degree in different growth periods increases from the central part of the Jilin Province to the western part of the Jilin Province. The sensitivity degree showed an increasing trend from the southeast to the northwest. The places with the strongest self-recovery ability are mainly concentrated in Changchun, Siping, Baicheng, and the other area. The ability to adjust to drought in each growth period is weak and crop yield reduction caused by drought is easy to create. Environmental adaptability is closely related to the social and economic situation every year, so it changes greatly and is flexible. Areas with strong drought vulnerability are mainly concentrated in Baicheng, Tongyu, and Qianguo. The research results can provide a certain basis for risk assessment, early warning, and disaster prevention and mitigation of agricultural drought disaster in the research area.