Background Mastering the spatial distribution and planting area of paddy can provide a scientific basis for monitoring rice production, and planning grain production layout. Previous remote sensing studies on paddy concentrated in the plain areas with large-sized fields, ignored the fact that rice is also widely planted in vast hilly regions. In addition, the land cover types here are diverse, rice fields are characterized by a scattered and fragmented distribution with small- or medium-sized, which pose difficulties for high-precision rice recognition. Methods In the paper, we proposed a solution based on Sentinel-1 SAR, Sentinel-2 MSI, DEM, and rice calendar data to focus on the rice fields identification in hilly areas. This solution mainly included the construction of rice feature dataset at four crucial phenological periods, the generation of rice standard spectral curve, and the proposal of spectral similarity algorithm for rice identification. Results The solution, integrating topographical and rice phenological characteristics, manifested its effectiveness with overall accuracy exceeding 0.85. Comparing the results with UAV, it presented that rice fields with an area exceeding 400 m 2 (equivalent to 4 pixels) exhibited a recognition success rate of over 79%, which reached to 89% for fields exceeding 800 m 2 . Conclusions The study illustrated that the proposed solution, integrating topographical and rice phenological characteristics, has the capability for charting various rice field sizes with fragmented and dispersed distribution. It also revealed that the synergy of Sentinel-1 SAR and Sentinel-2 MSI data significantly enhanced the recognition ability of rice paddy fields ranging from 400 m 2 to 2000 m 2 .

Accurate long-term (6–24 h) prediction of PM2.5 is critical to human health and daily life. While deep learning techniques have been extensively used to forecast PM2.5, prior studies have primarily relied on shallow recurrent neural networks (RNNs), which may accumulate errors and limit the long-term prediction capability of the model. To address this issue, a new hybrid model has been proposed in this study, which combines the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method with a deep Transformer neural network (DeepTransformer) to enhance the accuracy of long-term PM2.5 forecasting. The model includes a new embedding layer that efficiently models historical, meteorological, and discrete-time data. Additionally, to improve the long-term inference capability of DeepTransformer, a non-autoregressive direct multi-step (DMS) prediction strategy is introduced, and a novel DMS decoder replaces the vanilla Transformer decoder. Experiments conducted on two public datasets demonstrate that the novel model achieves excellent prediction performance. Specifically, DeepTransformer achieves R2 = 0.984 and RMSE = 11.61 µg/m3 in 1-hour prediction and R2=0.704 and RMSE = 30.78 µg/m3 in 24-hour prediction. Compared to single models, DeepTransformer achieves a 30% decrease in MAE, a 27% decrease in RMSE, and a 59% increase in R2 for the long-term (24-hour) prediction of PM2.5

Most of the existing object detection methods have complicated hand-designed components, such as nonmaximum suppression procedures and manual resizing of anchor boxes. Based on detection transformer (DETR), this letter not only eliminates the need for manual component adjustment but also solves three problems of poor remote sensing image for directional object capture, slow DETR convergence, and the same attention allocated by different layers of decoder. First, the D-angle module is used to align the rotating object region while accelerating the convergence using the a priori angle. Then, the overall computation of the model is reduced by using adaptive proposal selection (APS) in the cascade structure. Finally, the adaptive query selection (AQS) module is applied so that the decoder in different layers gets different attention weights to optimize the layer-by-layer fine-tuning process. In this letter, the effectiveness of the proposed method is verified using two public datasets, DOTA and HRSC2016.

Accurate mapping of rice-growing areas is essential to ascertain the spatial distribution of rice fields, and ensure food security. It is a challenging task to timely and accurate identify rice under the complex terrain due to its diversified land cover, small- or middle-sized rice fields with fragmented distribution. In this paper, the time series VV and VH backscatter coefficient datasets were first constructed based on 411 sentinel-1 synthetic aperture radar (SAR) images in Chongqing city with complex terrain. Then, the rice multi-characteristic parameters, including SAR backscatter features, composite features, rice phenological parameters, texture features and topographic features, were generated. On this basis, the homogeneous image objects were produced. Furthermore, a rice identification algorithm combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization SAR (MPHO-DPSAR) was established. The research demonstrated that the MPHO-DPSAR algorithm can achieve accurate mapping of small and medium-sized and fragmented rice fields in regions under complex terrain according to the accuracy evaluation at three levels and the comparison with other three classical rice identification methods. The suitability and limitations of proposed MPHO-DPSAR algorithm were also discussed from the aspects of SAR data temporal and spatial resolution, rice phenology, and surface landscape complexity.

A deeper understanding of the spatiotemporal variation characteristics of ecosystem health and its driving mechanism are important for ecosystem management and restoration. Under the complex environment in the southwestern mountainous area of China, various natural and anthropogenic factors interact with each other, complicating the mechanism driving ecosystem health. Quantitatively exploring the interaction among driving factors is challengeable but worthwhile. Based on the pressure-state-response (PSR) framework, the ecosystem health value of the study area was computed for the years of 2000 and 2018 at the grid scale. A geographical detector model was adopted to explore the factors driving ecosystem health change. We found that, compared to the year of 2000, there was an improvement in ecosystem health in 2018. The most significant improvement occurred in ample rural areas scattered in remote mountain areas. Nevertheless, there were grids experiencing ecosystem health deterioration. The appearance of a "deterioration ring " surrounding the metropolis region was the typical representative. Among the 10 selected driving factors, in-migrant population, out-migrant population, population density, elevation, and slope were found to be the most important factors. There were obvious impact thresholds of elevation, slope, distance to towns and distance to road on ecosystem health change. The out -migrant population in rural areas strongly promoted the local ecosystem health improvement by alleviating the pressure of human activity. In contrast, the in-migrant population presented the opposite effects on ecosystem health. All driving factors produced enhanced effects on the ecosystem health change through interaction effects. The largest enhanced effects occurred between the migrant population (in-and out-migrant) and the other eight driving factors. Our findings emphasize the key role of migrant population in ecosystem health change, which has not been considered in previous studies.

采用西南地区巫溪大官山同一坡面10个不同海拔高度梯度观测站2019～2020年逐小时温湿观测资料,分析了气温、气温直减率、日较差和相对湿度的梯度变化特征.结果表明:观测期间,气温随海拔升高而降低,海拔2000 m以上区域秋、冬季常出现逆温或同温现象;年平均气温递减率为0.57℃/100 m,最大值出现在3月和9月,分别为0.63℃/100 m和0.62℃/100 m,2月最低为0.49℃/100 m;日较差总体随海拔升高而减小,但在海拔1065～1222 m,出现了日较差随海拔升高而快速下降的突变区;年、春季在海拔1222～2180 m,秋季在海拔1222～2550 m,出现了日较差相对稳定层,其它季节不太明显.在海拔1670 m以下区域,年相对湿度为78.5％,夏季最大(85.3％),秋季次之(82％),冬季再次(74.3％),春季最低(72.3％);随着海拔升高云雾出现频率增大,年和各季相对湿度均随之增大;海拔1670～1930 m为突变区间,相对湿度迅速增加,在海拔1930～2550 m,年、春、夏、秋季处于云中的时间较多,相对湿度变化不大;冬季由于云层低,海拔较高的区域常处于云的上方,相对湿度随海拔升高反而有所减小.

柑橘是世界第一大水果,面积和产量均居于首位,是全球第四大贸易农产品.柑橘产业是我国南方山区特色支柱产业之一,对精准扶贫、乡村振兴和生态保护贡献突出.重庆立体气候特点鲜明,夏季高温高湿,秋冬阴雨,日照偏少,气候生态因子与品种适应性关系复杂,新品种生态适应性数据缺失而盲目推广对产业发展带来严峻挑战和巨大风险.

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The mechanisms by which pollution forms and acts in mountain cities are unique and remain unclear owing to terrain and meteorological conditions in the mountains. This study investigates the pollution characteristics in Chongqing, a typical mountain city, over the period from 2013 to 2017. Routine observed surface meteorological data, CALIPSO data, ERA5 reanalysis data and the backward trajectory calculation were used for analysis and comparison among four typical pollution cases. The results can be summarized as follows: (1) Pollutants in firework and unfavorable weather condition case mainly originated from local emissions. Anthropogenic aerosols that were characterized by spherical and small particles were detected, which contributed to the higher atmospheric scattering ability. Meanwhile, high humidity was beneficial to hygroscopic growth, resulting in a mixture of haze and fog forming low-visibility conditions. The strong inversion of the upper-layer and the presence of high mountains meant that local pollutants became confined within the basin region, leading to the formation and maintenance of pollution. The important roles of the weather conditions that are associated with mountains, such as cold air damming, low-level jet, valley and gap wind, were also revealed by the variable pollution conditions. (2) Airflow originating from Guizhou, Guangxi, and Hunan, where numerous fire points were detected, transported pollutants that were released from straw burning through upper-lower layer transport to the basin during the straw burning case. The aerosols detected showed low depolarization but a high color ratio, which are the typical characteristics of smoke. The inversion layer did not cover the entire basin, indicating that smoke aerosols were transported by downdraft to mix with local pollutants. (3) During the dust case, large amounts of dust aerosols were detected reaching from the surface to the troposphere, with a special double-layer structure. A lower depolarization ratio but stronger scattering ability was observed for the dust aerosols above the study region, which was attributed to mixing with other aerosols and chemical processes. Secondary circulation occurred when the inversion layer was at around 700 hPa, bringing dust aerosols from around 850 hPa to the surface. This study provides an understanding of the optical properties of the various types of aerosols and highlights the importance of weather condition in the formation and maintenance of pollution events. The results emphasize the need for intense vertical measurement in mountain cities.

利用1980—2019年重庆市中心城区4个气象站点的气温、降水等观测资料以及典型时段卫星资料,分析重庆市热岛效应的时空变化特征以及不同天气状况对热岛的影响.结果表明:20世纪90年代中期以来,重庆城市热岛效应增强趋势明显,21世纪10年代达最强,近年有减缓迹象.热岛的日、月及季节变化特征分布为:白天弱,夜间强;8月最强,6月最弱;盛夏最强,初春次之,仲春至初夏最弱.卫星遥感显示城市热岛呈东北、西南走向分布,强热岛主要位于人口密集的老城区、商业区、广场、车站、工业园以及城市新区等区域.21世纪10年代,城市热岛效应受雨天、阴天等负向驱动因素的影响以及多云天、晴天等正向驱动因素的影响,重庆市中心城区雨天、阴天、多云天、晴天时的平均热岛强度分别为0.19、0.52、0.69、0.76℃.

基于2019年1月～2020年12月西南地区东部大官山降水观测数据,分析了降水随海拔高度的变化特征.结果表明:2019～2020年,大官山降水量总体随海拔升高而增大,多年平均梯度变化率为1.32％/100 m,最大降水高度在海拔1900 m左右.各季降水梯度变化率中,夏、秋季高,冬、春季低,夏季为3.31 mm/100 m,秋季为1.39 mm/100 m,冬季为0.50 mm/100 m,春季为0.67 mm/100 m.各月降水梯度变化率中,7月最高,达5.06 mm/100 m,1月和11月最低,分别为0.23 mm/100 m和0.29 mm/100 m.降水日数和小雨日数随高度的线性变化趋势较明显,平均上升率分别为2.86 d/100 m和2.56 d/100 m.大雨日数在海拔1900 m左右最大,暴雨日数在海拔2500 m左右最大.降水日变化表现出多峰值特征,降水量和降水强度均在06～09时达到最大,降水频率也随海拔高度升高而增大,其中,高海拔降水频率在15时左右达到最大.降水随海拔高度的变化与天气过程密切相关,持续阴雨天气过程降水量的梯度变化较为平缓,暴雨天气过程降水量随海拔的升高而升高,局地阵雨中单次过程降水量与海拔高度相关性不明显.

Spatial assessment of rice cultivation area is a crucial activity, which is the foundation for the government to effectively improve the comprehensive rice production, promote the continuous increase of farmers' income and accelerate the construction of a modern rice industry system. In recent decades, large areas of arable land in Chongqing were converted to built-up land or restored to forests and grasses, resulting in a rapid decline in the rice planting area. Chongqing has an annual average of 104 cloudy and foggy days, the terrain is complex, and rice fields are generally fragmented with small- or middle-sized. How to timely obtain updated and accurate rice planting maps in Chongqing remains challenging. In this study, we processed 411 time series Sentinel-1A SAR scenes of vertical-horizontal polarization in 2020 over Chongqing, and analyzed the characteristics of rice phenological parameters under different terrain conditions, including rice transplanting date, mature grain date, length of growing season, rice agronomy flooding decline speed during sowing-transplanting period, and green-up speed during transplanting-mature period. On these bases, a decision tree algorithm integrating topographical features and rice phenological characteristics was proposed for collectively mapping patches of rice field. The identified rice results had user, producer, and overall accuracies of 0.96, 0.85 and 0.88, respectively. Meanwhile, SAR-derived rice area was compared against official rice statistical area at the county/district level with the correlation coefficient of 0.96. According to the rice map in 2020, there was a total area of 6125.70 km2 paddy rice in Chongqing, which was 6.27% lower than the data from the Chongqing statistical yearbook. Our study demonstrates the robustness and effectiveness of the proposed rice mapping method that comprehensively deliberate the topographical and rice phenological features in the complex landscapes with diverse crop types, small-medium sized and fragmented rice fields, and frequent cloudy and foggy weather.

Modeling fire susceptibility in fire-prone areas of forest ecosystems was essential for providing guidance to implement prevention and control measures of forest fires. Traditional models were developed on the basis of random selection of absence data (i.e., nonfire data from unburned areas), which could bring uncertainties to modeling results. Here, a new model with the genetic algorithm for Rule-set Production (GARP) algorithm and 10 environmental layers was proposed to process presence-only data in the susceptibility modeling of forest fires in Chongqing city. To do this, 70% of 684 fire occurrence data (479) during the period of 2000–2018 were applied to train the proposed model. And, 30% of these fire occurrence data (205) and the same amount of no-fire data (205) were emerged as validation dataset. The results showed that, for some environmental layers (i.e., distance to the nearest road, land cover, precipitation, distance to the nearest settlement, aspect, relative humidity, elevation, wind speed, and temperature), their P values were less than 0.05, indicating that these 9 environmental layers have significant influence on the spatial distribution of fire susceptibility in Chongqing city. On the contrary, with a higher P value (i.e., 0.126), the slope layer has an insignificant effect on fire susceptibility in the study area. Furthermore, the results of receiver operating characteristic analysis (ROC) showed that the proposed model has a good performance with an AUC value of 0.869, an accuracy value of 0.732, a sensitivity value of 0.59, a specificity value of 0.873, a positive predictive value of 0.823, and a negative predictive value of 0.681. This study revealed the validity of the proposed model in modeling the susceptibility of forest fires.

Fine particulate matter (PM2.5) has attracted extensive attention due to its harmful effects on humans and the environment. The sparse ground-based air monitoring stations limit their application for scientific research, while aerosol optical depth (AOD) by remote sensing satellite technology retrieval can reflect air quality on a large scale and thus compensate for the shortcomings of ground-based measurements. In this study, the elaborate vertical-humidity method was used to estimate PM2.5 with the spatial resolution 1 km and the temporal resolution 1 hour. For vertical correction, the scale height of aerosols (Ha) was introduced based on the relationship between the visibility data and extinction coefficient of meteorological observations to correct the AOD of the Advance Himawari Imager (AHI) onboard the Himawari-8 satellite. The hygroscopic growth factor (f(RH)) was fitted site-by-site and month by month (1–12 months). Meanwhile, the spatial distribution of the fitted coefficients can be obtained by interpolation assuming that the aerosol properties vary smoothly on a regional scale. The inverse distance weighted (IDW) method was performed to construct the hygroscopic correction factor grid for humidity correction so as to estimate the PM2.5 concentrations in Sichuan and Chongqing from 09:00 to 16:00 in 2017–2018. The results indicate that the correlation between “dry” extinction coefficient and PM2.5 is slightly improved compared to the correlation between AOD and PM2.5, with r coefficient values increasing from 0.12–0.45 to 0.32–0.69. The r of hour-by-hour verification is between 0.69 and 0.85, and the accuracy of the afternoon is higher than that of the morning. Due to the missing rate of AOD in the southwest is very high, this study utilized inverse variance weighting (IVW) gap-filling method combine satellite estimation PM2.5 and the nested air-quality prediction modeling system (NAQPMS) simulation data to obtain the full-coverage hourly PM2.5 concentration and analyze a pollution process in the fall and winter.

2015-2020年成渝地区各城市PM2.5浓度(国控点监测数据)下降显著,但重庆市、川东北城市群及川南城市群PM2.5污染问题依然存在,且存在一定的传输影响关系.以重庆市为例,使用HYSPLIT模型计算了2015-2020年秋冬季PM2.5污染期间气流后向轨迹,利用轨迹聚类和潜在源贡献算法,分析了不同年份的PM2.5输送特征.结果表明:重庆市PM2.5污染主要受西偏南方向(约占58％,长距离为主)、北偏西方向(约占26％,中距离为主)和南略偏西方向(约占16％,短距离为主)传输影响;川东北城市群和川南城市群对重庆市PM2.5污染传输贡献较为显著,6年平均贡献率分别为23％和15％;不同年份的污染传输贡献差异明显,2015-2017年以川南城市群污染传输为主(平均贡献24％),2018-2020年以川东北城市群污染传输为主(平均贡献37％),川渝以外污染传输影响逐年减弱(平均贡献由33％降至5％).在"成渝双城经济圈"背景下,重庆市与周边川东北城市群及川南城市群建立大气污染联防联控工作机制是深化PM2.5污染防控的有效途径.

Ocean currents are a key element in ocean processes and in meteorology, affecting material transport and modulating climate change patterns. The Doppler frequency shift information of the synthetic aperture radar (SAR) echo signal can reflect the dynamic characteristics of the sea surface, and has become an essential sea surface dynamic remote sensing parameter. Studies have verified that the instantaneous Doppler frequency shift can realize the SAR detection of the sea surface current. However, the validation of SAR-derived ocean current data and a thorough analysis of the errors associated with them remain lacking. In this study, we derive high spatial resolution flow measurements for the Kuroshio in the East China Sea from SAR data using a theoretical model of shifts in Doppler frequency driven by ocean surface current. Global ocean multi observation (MOB) products and global surface Lagrangian drifter (GLD) data are used to validate the Kuroshio flow retrieved from the SAR data. Results show that the central flow velocity for the Kuroshio derived from the SAR is 0.4–1.5 m/s. The error distribution between SAR ocean currents and MOB products is an approximate standard normal distribution, with the 90% confidence interval concentrated between −0.1 m/s and 0.1 m/s. Comparative analysis of SAR ocean current and GLD products, the correlation coefficient is 0.803, which shows to be significant at a confidence level of 99%. The cross-validation of different ocean current dataset illustrate that the SAR radial current captures the positions and dynamics of the Kuroshio central flow and the Kuroshio Counter Current, and has the capability to monitor current velocity over a wide range of values.

Human activity is becoming the key factor influencing ecological health changes and clarifying the nature of that influence could effectively promote regional ecological restoration.Commonly used human-related factors (e.g., social and economic census data) can only be constrained at the administrative division, and they lead to a loss of detailed spatial characteristics.In this study, we applied human activity intensity (HAI), mapped at the grid scale, to represent pressures of human activity on the ecosystem.Taking the Three Gorges Reservoir Region (TGRR) as the study area, the spatial heterogeneity of the influences of human pressure on ecological health was analyzed.The results showed that, in the TGRR from 2000 to 2018, there was an overall improvement of ecological health and a significant spatial reorganization of HAI.The impact of HAI on ecological health had significant spatial heterogeneity.In areas where HAI had decreased, ecological health was prone to improve significantly.In contrast, in areas where HAI had increased, ecological health tended to become worse, and only when the increase of HAI exceeded 0.08 would that tendency be significant.Compared to urban areas, HAI change had even broader impacts on ecological health in remote mountain areas, where a more remarkable restoration of the ecosystem was experienced.

Potential relationships among heavy air pollution, weather conditions, and meteorological effects are unclear and require further investigation, especially for areas with complex terrains, such as the Sichuan Basin (SCB), one of the most polluted regions in China. In this study, air pollution in the SCB was examined and 18 regional persistent heavy pollution events (RPHEs) were identified for the winters of 2014-2018. The average persistent period of the RPHEs was 8.89 days, and the number of affected cities was 17. Based on ground-based observations, CALIPSO satellite data, reanalysis data, and backward trajectory calculations, the synergistic effects of the thermodynamic structures, synoptic circulations and the radiative feedback of aerosols on the formation of RPHEs were revealed. The results can be summarized as follows: (1) An abnormal warming center, attributing to the warm southerly advection in the upper layer and the cold air dammed by the topography near the surface, always presented around 800-700 hPa to form a deep stable layer. (2) The diurnal variations in vertical motions triggered by the thermodynamic structures could regulate the pollution episodes. During the daytime, pollutants accumulated rapidly and thoroughly mixed under the control of sinking airflow from 800 hPa layer to the ground. At night, pollutants sometimes slowly diffused when weak ascending airflow appeared. (3) Forced by the stable layer and topography of the Tibetan Plateau, the local circulation was confined within SCB, resulting in the intensive mixing of local emissions and transport pollutants from other regions. This situation could be maintained for a long time with stable synoptic circulation in winter, leading to the formation of RPHEs. (4) The pollution episodes were featured with multi-layer pollutants above SCB according to the CALIPSO observations, including the local anthropogenic aerosols near the surface, dust aerosols originating from the Taklamakan Desert, and biomass burning aerosols from Southeast Asia. Solar absorption aerosols, including black carbon and dust above the region, could cause meteorological feedback, making the vertical layer more stable and enhancing the persistence and intensity of the pollution episodes. This study highlights the appreciable effects of synoptic circulations on the vertical thermodynamic structures of the atmosphere and air quality, and raises the understanding of the environmental and climate impacts of RPHEs in complex terrains.

偏振遥感技术监测细模态气溶胶光学物理特性的优势,是监测大区域大气污染的有效手段.基于高分五号(GF-5)携带的多角度偏振成像仪(DPC)的多角度偏振观测数据开展全球陆地上空的细模态气溶胶光学厚度(AODf)反演研究.主要通过地表二向偏振反射(BPDF)模型估算出地表偏振反射率,结合评价函数得出了最优气溶胶模型以及AODf反演结果,将反演结果与AERONET地基观测数据进行了对比验证.结果显示:地基数据与反演结果相关性系数达到0.903,平均绝对误差,平均相对误差、均方根误差分别为0.026、0.43％、0.060,反演结果总体可靠,反演方法具备可行性.

The surface information in cloud-covered regions cannot be captured by thermal infrared sensors.Therefore, thermal infrared remote sensing product data have lost their ability to monitor drought in cloudy regions.In this paper, remotely sensed daily land surface temperature reconstruction (RSDAST) model is used to reconstruct LST value of cloud pixels in FY3C/VIRR LST product data, and the reconstructed LST and NDVI data are used to monitor drought in Chongqing in 2018 by TVDI index.And the correlation between soil moisture and OTVDI (original TVDI) and RTVDI (reconstructed TVDI) was examined in this study so that we can evaluate the ability of RTVDI to monitor drought under cloudy conditions.The evaluation results show that the RSDAST model not only expands the spatial scope and temporal continuity of drought monitoring in cloudy regions, but also raises the accuracy of regional drought monitoring (the R value between RTVDI and soil moisture in long time series and spatial distribution is higher than that of OTVDI), which greatly improves the availability and reliability of thermal infrared remote sensing data in cloudy conditions.