Flash droughts, characterized by their abrupt onset and rapid intensification, are predicted to increase in frequency and severity under global warming. Understanding the incidence and progression of a flash drought and its impact on maize growth is crucial for maize production to withstand flash drought events. This study used the evaporative demand drought index (EDDI) method to evaluate the incidence of summer drought in Liaoning during the period 1961–2020. It examined the incidence and characteristics of summer flash droughts in Liaoning Province in the period of 1961–2020 and evaluated the factors responsible and the impact on maize during the critical development period. The ratio of the number of stations recording a disaster to total number of stations (IOC) curve, i.e., the ratio of the number of stations recording disasters and total stations, for summer flash droughts in Liaoning showed an upward trend during the period of 1961–2020, with large-scale, regional, and local flash droughts occurring in 8, 10, and 31 years, respectively. Summer flash droughts in Liaoning were mainly in the extreme drought category and ranged in frequency from 10% to 20% in most areas. Before the flash drought occurrence in three typical years (1989, 1997, and 2018), a precipitation deficit without large-scale high-temperature events was observed, and the cumulative water deficit caused the flash drought. Regional or large-scale high-temperature events were often accompanied by flash droughts, and the drought intensified rapidly, owing to the influence of heat waves and water deficits. Summer flash droughts caused a reduction in total primary productivity (GPP) of maize by more than 20% in most areas in the three typical years. The yield reduction rate in 1989, 1997, and 2018, was 27.6%, 26.4%, and 5%, respectively. The degree of decline in maize productivity and yield was associated with the onset and duration of the flash drought. The atmospheric conditions of summer flash droughts were characterized by high-pressure anomalies and atmospheric subsidence, which were unconducive for precipitation but conducive to flash drought occurrence. The continuous high-pressure anomaly promoted the maintenance of the flash drought.

The crop coefficient (Kc) is widely used for operational estimation of actual evapotranspiration (ETa) and crop water requirements. The standard method for obtaining Kc is via a lookup table from FAO-56 (Food and Agriculture Organization of the United Nations Irrigation and Drainage Paper No. 56), which broadly treats Kc as a function of four crop-growing stages. However, the distinctive physiological characteristics of overwintering crops, such as winter wheat (Triticum aestivum L.), which is extensively planted in the North China Plain (NCP), are not addressed in this method. In this study, we propose a stage-wise method that accounts for Kc variations for winter wheat at each critical phenological stage, thereby estimating Kc at finer temporal scales. Compared with the conventional FAO method, the proposed stage-wise method successfully captures the bimodal pattern in Kc time series for winter wheat, which is shown at both ten-day and phenological time scales. In addition, the accuracies of the proposed stage-wise Kc method and the FAO method were evaluated using micro-meteorological measurements of ETa collected at the Gucheng agrometeorological experimental station in the NCP. Using a leave-one-out strategy, the evaluation revealed that the stage-wise method significantly outperformed the FAO method at both daily and critical phenological time scales, with root-mean-square errors in ETa for the stage-wise method and the FAO method being 0.07 mm·day-1 and 0.16 mm·day-1, respectively, at the daily time scale, and 0.01 mm·day-1 and 0.27 mm·day-1 at the critical phenological time scale. Generally, the FAO method underestimates ETa during the initial stage and overestimates ETa during both the development and mid-season stages. It is shown that the proposed stage-wise method is important for the water-stressed NCP where precision irrigation is highly desirable, especially during the critical phenological stages. Results from this study provide insight into accurate estimation of water requirements for winter wheat at phenological time scales.

As the main body of the Liaohe River Delta, the Panjin Wetland plays a vital role in maintaining the ecological balance throughout the region. The analysis of the correlation between NDVI and meteorological factors was carried out on reed wetland by using the normalized differential vegetation index (NDVI) data of Panjin Reed Wetland from 2006 to 2015 and such meteorological data as ten-day average temperature, ten-day precipitation, ten-day sunshine duration, ten-day relative humidity and ten-day evaporation capacity. The results showed that the NDVI of Panjin Reed Wetland had no obvious year-to-year changes from 2006 to 2015 and showed a slight upward trend with small fluctuations. However, the NDVI of Panjin Reed Wetland had significant intra-annual changes and was depicted as a single-peaked curve in the crop growing season. The curve reached the peak value in early- and mid-July, which was highly consistent with the phenophase. The ten-day NDVI of reed wetland is most and positively correlated to the ten-day average temperature, with the correlation coefficient reaching a value of 0.7874.

The leaf area index (LAI) serves as a key metric for tracking crop growth and can be integrated into crop models for yield estimation. Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable errors due to the pixel-level heterogeneity. To improve the applicability and inversion accuracy of MODIS LAI products in the Northeast China (NEC) region, this study upscaled the 500-m resolution MODIS LAI product to a 5-km resolution by initially calculating the mean value. Then, the scale factors were estimated based on the observed LAI data of spring maize. To further refine the accuracy of the remotely sensed LAI, 1-km resolution land use data were resampled to 500-m resolution, and the pixel purity of spring maize was calculated for each 5-km grid cell. The scale factor time series was fitted with and without consideration of pixel purity, and the accuracy of the adjusted LAI using these two methods was compared. Our findings demonstrate that the optimal method for fitting scale factors for spring maize LAI data is piecewise function method which combines Gaussian and quadratic polynomial functions. The time series of scale factors derived from high- and low-purity pixels, differentiated by a 50% purity threshold, resulted in improved performance in adjusting the spring maize LAI compared to traditional remote sensing LAI data. The adjusted LAI performed better in reflecting the growth characteristics of spring maize in the NEC region, with the relative mean square errors between observed and adjusted LAI of spring maize during 2016 and 2020 below 1 m²/m². This study provides crucial support for monitoring the growth process and estimating the yield of spring maize in the NEC region and also offers valuable scientific references for the optimization and application of remote sensing data.

To gain correct sea ice data, high time resolution (MODIS) and high spatial resolution (GF-2) materials are combined to achieve wide-range high precision monitoring of sea ice area in Liaodong Bay. This article takes GF-2 sea ice area as the baseline, intercepts corresponding MODIS sea ice monitoring scope, establishes a sea ice area correction model, and conducts long time series' analysis of sea ice area change. Here's the result: spectral observation result shows that comparing with CH2 channel, spectral reflectance of the snow-covering sea ice, ice-water mixture, seawater and sea ice in CH1 are with a greater difference value and easier to distinguish. Comparing with the accuracy of 3 extraction methods of MODIS sea ice, the optimal method is CH1 single channel extraction. This paper takes the sea ice area extracted by GF-2 satellite as the benchmark data, then establishes a MODIS sea ice area correction model. The model's correlation coefficient is 0.96. The monitoring error after correction is reduced from 39% to 10%. Sea ice area change in long time sequence shows that the sea ice area of Liaodong Bay is in slight downtrend. The inter-annual variation is featured with 4 years' periodical change. In every winter, sea ice area is in unimodal change, and reaches the maximum value in about early Feb.

Almost all forest fires in Liaoning were caused by the act of men. Between 15:00-19:00, which is when the frequencies of human activities and the occurrence of fire peak, Liaoning is out of the path of the polar-orbiting satellites, making these 4 hours the blind spot domain in fire-incidence monitoring. At the same time, the trend of fire accidents changed quickly under the influence of local meteorological conditions, leading to the demand for a higher temporal resolution. Therefore, there is an urgent need to utilize the Himawari satellite in the forest fire intensity monitoring so as to compensate for the drawbacks of the polar-orbiting satellites. By improving the forest fire identification model of the Himawari-8 satellite, a forest fire monitoring done every ten minutes was realized , thereby enhancing the effectiveness of fire monitoring and rescue within a given period of time.