Engine Platform Research Articles

Analysis of lake changes and their influence factors in the three river regions from 2000 to 2020 in the Sanjiangyuan Region, China

An important factor for investigating climate change in the Sanjiangyuan is the evolution of the spatio-temporal pattern of lakes in this region. The present study used the Google Earth Engine (GEE) platform to extract lakes from 2000 to 2020. The present approach created a lake distribution dataset yearly and analyzed spatial and temporal patterns over 20 years. The analysis of lakes focused on the reaction of the Sanjiangyuan Lakes area to changes in climate, glaciers, and permafrost. The findings indicated that the Sanjiangyuan region contains 143 lakes, the majority of which are predominantly small, measuring 1–10 km2. The small lakes account for 60.14 % of the total and are primarily located in the source regions of the Yangtze River and Yellow River. The findings demonstrated that the Sanjiangyuan lakes experienced a significant expansion over the past two decades, particularly from 2011 to 2020. These lakes are divided into expanded, atrophic, and stable categories. Expanded lakes showed significant inter-annual trends in expansion, while atrophic lakes showed smaller fluctuations. The area of stable lakes experienced a consistent decline after 2010, despite a consistent expansion tendency from 2001 to 2010. Moreover, the results indicated that alterations in the size of glaciers and ice reserves in the Sanjiangyuan region have had the greatest influence on the fluctuation in lake area. Among the factors that affect the climate, temperature had the most significant effect on the change in lake area, followed by precipitation.

An integrated approach to obtain high-precision regional root water uptake maps

Root water uptake (RWU) is exceptionally difficult to determine in situ due to its unique nature of occurring underground. Accurately characterizing the spatial–temporal characteristics of RWU at a regional scale remains a significant challenge, and high-precision regional-scale RWU maps have not yet been reported. In this study, we develop a novel approach that integrates the Google Earth Engine (GEE) platform, mathematical models, and machine learning algorithms to generate high-precision regional maps of maize RWU in China’s primary maize production area. The GEE platform was used to extract essential parameters such as soil properties, leaf area index, and growth period. These variables, along with meteorological data from 122 agricultural meteorological stations, were used to calculate RWU at a point scale using the Hydrus-1D and AquaCrop models. Finally, we utilized Hydrus-calculated point-scale RWU fluxes, along with 31 covariates extracted from the GEE platform, in conjunction with the CatBoost algorithm to develop a machine-learning model for estimating regional-scale RWU. The accuracy of the developed machine-learning model was validated using statistical measures, such asa mean absolute error (0.0299 cm d−1), a root mean squared error (0.0398 cm d−1), and the correlation coefficient (0.9178). The model was then applied to generate high-precision (500 m × 500 m) regional-scale RWU maps from 2015 to 2019. Additionally, our analysis identified several parameters, including TempDew (i.e., 2-m dewpoint temperature), SurfaceSM (i.e., top layer soil moisture), TempMax (i.e., max 2-m air temperature), and Latitude, as key factors influencing regional-scale RWU. This approach provides a robust strategy for accurately generating regional-scale RWU maps, even with limited data. Furthermore, it can be extended to obtain other regional indices, offering valuable insights for various real-world applications.

Driving factors and trend prediction for annual runoff in the upper and middle reaches of the yellow river from 1990 to 2020

The Yellow River Basin (YRB) plays a pivotal role in the water resources management of its region, significantly influenced by the interplay between climate change and human activities, particularly in its upper and middle reaches (UMRYR). This study aims to elucidate the evolving patterns and determinants of runoff within the UMRYR, a matter of considerable importance for the basin’s water resource management, strategy, and distribution. Utilizing the Google Earth Engine (GEE) platform, this research accessed comprehensive datasets including precipitation, drought index, and terrace area, among others, to examine their effects on runoff variations at five gauge stations across the YRB. Terrace data was extracted from Landsat imagery via the Random Forest Model, while annual runoff figures from 1990 to 2020 were sourced from the Sediment Bulletin of China River. Employing the Mann-Kendall test, we assessed the temporal changes in runoff over three decades. In addition, runoff drivers were analyzed by stepwise regression and redundancy analysis, leading to the construction of a multiple linear regression model. The accuracy of predicting annual runoff using the multiple linear model was verified through cross-validation and comparison with the ARIMA time series model. Our findings reveal the efficacy of the random forest algorithm in classifying terraces, achieving an accuracy rate exceeding 0.8. The period from 1990 to 2020 saw a general increase in annual runoff across the five gauging stations in the UMRYR, albeit with variations in the pattern, particularly at the Tangnaihai gauge station which presented the most complex changes. Crucially, three main drivers—summer precipitation (SP), terrace area (TR), and drought index (DI)—were identified as significant predictors in the regression models. The multiple linear regression model outperformed the ARIMA model in forecasting accuracy, underlining the significance of integrating these drivers into runoff prediction models for the UMRYR.

Urban sprawl and thermal response in the Guangdong-Hong Kong-Macao Greater Bay Area, 2000–2020

The urban heat island (UHI) environment is closely related to the daily life of residents, and the impact of UHI is further expanding in the era of rapid urbanization, which is particularly obvious in the Beijing-Tianjin-Hebei urban agglomeration and Guangdong-Hong Kong-Macao Greater Bay Area (GBA). To explore the connection between UHI and urban expansion, this study takes GBA as the study area, based on Google Earth Engine platform and Landsat satellite data. The urban-rural temperature difference method was used to analyze the heat island zoning in the GBA from 2000 to 2020, and the direction and type of urban expansion in the GBA were investigated by standard deviation ellipse and LEI, to explore the link between the type of urban expansion and the heat island region through geospatial analysis methods. The results of the study show that: (1) The proportion of the area of strong and extreme heat islands in the GBA was 37.64% in 2000; 38.61% in 2005; 47.07% in 2010; 55.76% in 2015; and finally 59.69% in 2020. The proportion of the area of heat island regions in the period from 2000 to 2020 showed an overall increasing trend, with the largest increase in heat island regions in the period from 2015 to 2020. (2) The intensity of urban expansion in the GBA is 32% in both 2000–2005 and 2005–2010, which is significantly higher than the 9% in 2010–2015, and then increases to 24 per cent in 2015–2020. The speed of movement of urban centroids decreases in 2000–2015 and increases in 2015–2020. The direction of urban expansion in the first 5 years is dominated by the northeast, and the direction of urban expansion in the latter 15 years is generally dominated by the southwest, and the type of urban expansion is dominated by the fringe type, with the enclave type of expansion accounting for a smaller proportion. (3) Pearson correlation analysis further confirms that there is a significant positive correlation between urban sprawl and the urban heat island effect, with a correlation coefficient of 0.78 and a p-value of 0.041. There is a high degree of spatial consistency between the distribution of built-up urban areas and the distribution of heat island areas in GBA, and the direction of the evolution of the urban heat island areas is the same as the direction of the evolution of the built-up urban areas in general, and the thermal response of the marginal expansion to the ground surface temperature is stronger than that of enclave expansion. The thermal response to the surface temperature is stronger than that of the enclave type of expansion.

Kajian Ruang Terbuka Biru Guna Menunjang Optimasi Ecosytem Services (Lokasi Kota Bandar Lampung)

One of the methods to maintain the balance of the perkotaan environment is to maintain its blue open space. There have been a lot of research on Green Open Space, but not on Blue Open Space (BOS). However, BOS also has a very important function and moreover, the ecosystem services provided by BOS can give positive implications. This research is aims to identify the existing BOS area in Bandar Lampung City. The data used in this research are Sentinel-2 satellite images for the extraction of BOS using AWEI on the Google Earth Engine platform, administrative data for the study area, field survey data for validation of the extraction results, Google Earth images for the delineation of existing BOS objects, and river stream data for the river objects of BOS. The methods used for obtaining BOS objects are machine learning-based supervised classification on Google Earth Engine, delineation, buffering, and overlay. The results of the identification of existing RTB in Bandar Lampung city are 206,01 ha consisting of embung/reservoirs, lakes, and rivers.

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

Urban Sprawl Analysis Using Landscape Metrics in Chiba Prefecture, Japan from 1990-2021

Abstract Urban sprawl, characterized by unregulated development on the outskirts of urban areas, leads to agricultural land loss, reduced biodiversity, and environmental issues. The study employs landscape metrics to analyze the spatial patterns and effects of urban sprawl by measuring the fragmentation and structure of urban areas in Chiba Prefecture, Japan. The research employs data collected from Landsat imagery 5 (1990) and 8 (2021) and utilizes the Google Earth Engine platform for spatial and temporal analysis. The Land Use Land Cover (LULC) assessment reveals the transformation of Chiba’s landscape from 1990 to 2021, with urban areas expanding significantly and vegetation decreasing. The analysis using 8 selected landscape metrics highlights the varying degrees of urban sprawl across Chiba Prefecture. Cities near Tokyo show higher levels of sprawl, while some areas outside the edge experience growing development. Notably, there is a trend of increasing urban sprawl toward the edge. As Chiba Prefecture faces challenges from an aging population, the findings emphasize the need for controlled urban development to mitigate the negative impacts of sprawl and the importance of sustainable urban planning especially towards the edge area to ensure a balance between development and environmental preservation.

Comparative Study of the Effect of Volcanic Activity and the El Niño Phenomenon on Surface Thermal Changes in the Arjuno-Welirang Crater Area Using GEE Cloud Computing

The increasing seismic activity of Arjuno Welirang on February 7, 2018, warned about the dangers of volcanic eruptions that must be watched out for, so that activity needs to be monitored continuously. There is little information available about seismic activity at the Arjuno Welirang complex. Most studies have investigated geothermal potential and subsurface structures. Due to the limitations of this data, land surface temperature (LST) analysis was carried out in this research to monitor the development of Arjuno-Welirang volcanic activity. Several volcano studies have used LST criteria in remote sensing applications to detect volcanic activity. However, the performance of LST has not been tested in the case of normal volcanic activity, which is influenced by external thermal disturbances. This study aims to show the performance of LST detection on changes in the normal volcanic activity of Arjuno Welirang, which is disturbed by temperature noise caused by the El Niño phenomenon. The LST calculations are carried out by including surface emissivity corrections obtained based on Normalized Difference Vegetation Index (NDVI) values. The LST calculation algorithm is implemented with the Google Earth Engine (GEE) platform. LST analysis was carried out on Landsat 8 images for the May-August 2023 time period obtained from the United States Geological Survey (USGS) database via GEE cloud computing. The reason for determining this time period is because the release of the daily Arjuno Welirang volcanic activity report by Magma Indonesia coincided with the Meteorology Climatology and Geophysical Agency (BMKG) notification regarding the occurrence of El Niño phenomenon. The output is an LST curve against time, which shows a tendency for the surface temperature of the Arjuno-Welirang crater to increase in July. This is similar to the trend of increasing seismic activity reported on the Magma Indonesia website and conversely is not similar to the increase in temperature due to the El Niño effect predicted by BMKG. The highest temperature increase occurred on July 24 2023, reaching 31.78 ⁰C, and then the temperature decreased. The method used in this research has proven that LST analysis is able to detect changes in normal volcanic activity in the Arjuno-Welirang crater even though there is thermal noise due to the influence of the impact of El Niño. LST values are more dominantly influenced by volcanic activity than by extreme El Niño weather.

Water Resources Monitoring in a Remote Region: Earth Observation-Based Study of Endorheic Lakes

In the western Andes, climate changes have led to drastic ecological changes during the Pleistocene and Holocene. Given the debate surrounding precipitation pattern changes and the lack of research on lakes in the Chilean Altiplano, this study aims to assess recent climate changes. The paper presents an innovative methodology based on Google Earth Engine (GEE), utilizing fluctuations in water levels in endorheic lakes as natural precipitation indicators. Three lakes (Chungará, Miscanti, and Miniques) in isolated drainage systems were studied, where changes in water levels directly reflect rainfall variations. Data from Landsat-OLI 8, Landsat-ETM+, Landsat-TM 5, and MODIS spanning 31 years were processed using the Google Earth Engine platform. The shapes of the water bodies were extracted using hue saturation value (HSV) composites. The surface areas of the lakes were compared with precipitation data from national meteorological stations and the Tropical Rainfall Measuring Mission (TRMM) using linear regression analyses. Both lake area and rainfall volume showed a decrease over time, with varying trends depending on environmental conditions. However, the analysis consistently indicates a reduction in the area and volume of Chilean lakes corresponding to observed rainfall patterns over the past three decades.

Spatial Analysis Model For Landslide Detection Using Relative Different NDVI (rdNDVI) Method Thought The Google Earth Engine Platform (Case Study: Sukajaya District, Bogor Regency)

This research utilizes the Google Earth Engine platform to detect landslides with relatively different NDVI (rdNDVI) methods. The purpose of the research is to improve our understanding of landslide analysis and detection, particularly those that occurred in Sukajaya district, Bogor Regency, Indonesia, on January 1, 2020. This research identifies vegetation changes associated with landslide likelihood using Sentinel-2A satellite image data available on Google Earth Engine. The results show that the rdNDVI method is effective in detecting landslides and can be used to determine areas that may be affected by landslides. This research also evaluates the accuracy of landslide detection by determining the threshold value to determine which areas are affected by landslides, by applying different slope values, the slopes used are slope 10, 15, 20, and 25. Comparing each slope results in a slope of 10 percent and a slope of 15 percent with 90% accuracy making the best accuracy compared to other slopes. The results of this research are expected to help the Regional Disaster Management Agency (BPBD) of Bogor Regency in managing landslides by conducting a careful and accurate analysis of areas that may be affected by landslides.

Revealing the Ground Deformation and Its Mechanism in the Heihe River Basin by Interferometric Synthetic Aperture Radar and Optical Images.

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to -70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone.

Extraction of 10 m Resolution Global Mangrove in 2022

With the intensification of global climate change, there is an increasing emphasis on protecting natural resources. Mangrove forests, critical to tropical and subtropical intertidal ecosystems, have garnered considerable attention in recent years for their strong carbon sink capacity, rich species diversity, and abundant natural resources. This study utilizes the 2020 global mangrove vector data as a baseline to construct a reasonable buffer zone by calculating the increase in mangrove crown width. The Google Earth Engine (GEE) platform and its Sentinel-2 data from 2022 are employed to acquire synthetic images across all regions using the mosaic algorithm. Then, mangrove forests are extracted using the Otsu algorithm, and a map depicting the global spatial distribution of mangrove forests in 2022 is obtained. The average overall accuracy of the extracted mangrove forests in this study reaches 92.4%, and it is determined that the global mangrove forest area expanded by 4920.6 km2 between 2020 and 2022, This study provides crucial data support for the global monitoring of mangrove changes and holds significant importance for protecting and restoring mangrove ecosystems.

Integration of Building Information Model into a Game Engine Platform for Indoor Accessibility Analyses

Understanding the movement patterns of individuals within a structure is crucial for efficient simulation. This entails the examination of network accessibility based on insights into the intricate indoor three-dimensional network topology. The combination of Building Information Modeling with Game Engines can streamline this approach. Hence, this study proposes a pipeline integrating the A* shortest path algorithm and walkable three-dimensional navigation meshes to analyze indoor accessibility. The pipeline design was deployed in a public building, where scenario-based analyses were conducted to determine the average distance and time shifts based on blockages. According to the results, exits' positioning and availability significantly impact indoor navigation and accessibility, underscoring their significance in building design and emergency preparedness in complex buildings.

Estimation of Actual Evapotranspiration and Water Stress in Typical Irrigation Areas in Xinjiang, Northwest China

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas.

Evaluation and application of satellite rainfall datasets using Google Earth Engine platform in Arunachal Pradesh, Eastern Himalayan Region of India

Evaluation and application of satellite rainfall datasets using Google Earth Engine platform in Arunachal Pradesh, Eastern Himalayan Region of India

Forecasting Land Use Dynamics in Talas District, Kazakhstan, Using Landsat Data and the Google Earth Engine (GEE) Platform

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km2 of herbaceous wetlands, 2271.41 km2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km2. In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas.

Monitoring and analyzing the dynamics of Zizania floating mats with PlanetScope imagery and Google Earth Engine

Zizania latifolia has been observed to generate floating mats due to water level fluctuations and wind-waves, leading to significant detrimental impacts on both water quality and biodiversity. The key to controlling and monitoring the spread and movement of Zizania floating is the early detection of outbreaks and the clarification of its formation mechanisms in response to water levels and wind actions. The temporal and spatial resolutions of conventional remote sensing monitoring methods are challenging to satisfy the requirements for continuous and accurate tracking of Zizania floating, which undergoes daily movement. This study develops a timely and accurate monitoring method for Zizania floating by calculating the Normalized Difference Vegetation Index (NDVI) from high spatiotemporal resolution PlanetScope (PS) imagery and building a decision tree on the Google Earth Engine (GEE) platform. Taking the Zizania floating outbreak in Honghu Wetland as the subject of our study, we extracted the continuous movement trajectories of Zizania floating from May to September 2019. Subsequently, we analyzed the interrelationship between the formation of Zizania floating and hydrometeorological factors. We then investigated the effects of Zizania floating on water quality and biodiversity. The results show that: (1) The integration of PS imagery and the GEE platform holds the potential to simplify the workload of remote sensing data processing. Based on the difference in NDVI between water bodies and Zizania floating, the dynamic expansion and reduction areas of the Zizania floating were successfully extracted by setting the threshold at 0.3, enabling timely and accurate tracking. (2) Abrupt fluctuations in water levels during brief time intervals serve as the primary trigger for Zizania floating. Additionally, wind speed during the same timeframe acts as a catalyst for the emergence of Zizania floating, with wind direction influencing its movement. (3) The outbreak of Zizania floating significantly elevates the concentrations of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (CODmn), and total ammonia nitrogen (TAN), while concurrently reducing the dissolved oxygen (DO) concentration in water bodies. The prolonged presence and movement of Zizania floating has reduced the biodiversity of the wetland ecosystem. Zizania floating mats present unique challenges due to their unpredictability, complicating the monitoring and tracking processes. It is necessary to develop a prompt, accurate, and cost-effective detection program for Zizania floating to reduce the risk it poses to wetland ecosystems.

High-resolution cropland mapping in China’s Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information

Numerous map products containing cropland data are currently available. However, due to their low temporal and spatial resolutions and limited accuracy, their practical applications are limited. Thus, research on the precise mapping of croplands with high temporal and spatial resolutions is still urgently needed. The Huang-Huai-Hai Plain, a critical grain production area in China, was used as the research area to produce high-resolution annual cropland maps from 2018 to 2022. Sentinel-2 imagery, digital elevation model (DEM) data, and China Agricultural Ecological Zoning (CAEZ) data were collected. Based on these data, the study area was stratified into different zones, and then the distinct characteristics and optimal timing for differentiating cropland from other land cover types within each zone were analyzed and used as prior information for guiding time-series image compositing. Furthermore, the spectral information divergence (SID) index was used to modify the existing sample migration technique to transfer training samples across years. Via the Google Earth Engine (GEE) platform, the random forest (RF) classification method and positive and unlabeled learning (PUL) method were combined to construct cropland maps from composited images and terrain information from DEMs. Compared with the seasonal or spectral-temporal metric (STM) image composition methods, the image composition method based on prior information significantly improved the distinction between cropland and other land cover types. Moreover, the newly proposed sample migration strategy substantially increased the accuracy of training samples transferred across years. Compared with existing cropland maps from products such as the ESA, ESRI, GlobeLand30, GLAD, and CLCD, the cropland maps generated in this study demonstrated superior accuracy, with average overall accuracy (OA) values from 88.81% to 91.66% for different zones, thus outperforming other products with OA values from 77.53% to 88.90%. This study supports the development of related agricultural and ecological research and provides a valuable reference for cropland mapping.

ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021

Abstract. Soybean, an essential food crop, has witnessed a steady rise in demand in recent years. There is a lack of high-resolution annual maps depicting soybean-planting areas in China, despite China being the world's largest consumer and fourth-largest producer of soybean. To address this gap, we developed the novel Regional Adaptation Spectra-Phenology Integration method (RASP) based on Sentinel-2 remote sensing images from the Google Earth Engine (GEE) platform. We utilized various auxiliary data (e.g., cropland layer, detailed phenology observations) to select the specific spectra and indices that differentiate soybeans most effectively from other crops across various regions. These features were then input for an unsupervised classifier (K-means), and the most likely type was determined by a cluster assignment method based on dynamic time warping (DTW). For the first time, we generated a dataset of soybean-planting areas across China, with a high spatial resolution of 10 m, spanning from 2017 to 2021 (ChinaSoyArea10m). The R2 values between the mapping results and the census data at both the county and prefecture levels were consistently around 0.85 in 2017–2020. Moreover, the overall accuracy of the mapping results at the field level in 2017, 2018, and 2019 was 77.08 %, 85.16 %, and 86.77 %, respectively. Consistency with census data was improved at the county level (R2 increased from 0.53 to 0.84) compared to the existing 10 m crop-type maps in Northeast China (Crop Data Layer, CDL) based on field samples and supervised classification methods. ChinaSoyArea10m is very spatially consistent with the two existing datasets (CDL and GLAD (Global Land Analysis and Discovery) maize–soybean map). ChinaSoyArea10m provides important information for sustainable soybean production and management as well as agricultural system modeling and optimization. ChinaSoyArea10m can be downloaded from an open-data repository (DOI: https://doi.org/10.5281/zenodo.10071427, Mei et al., 2023).

Investigation of Split Diesel Injections in Methanol/Diesel Dual-Fuel Combustion in an Optical Engine

Methanol is a promising alternative fuel due to its wide availability of raw materials, mature production processes, and low production cost. However, because of the low cetane number, methanol must include a more reactive fuel to assist with combustion when used in compression ignition (CI) engines. In this study, based on the optical CI engine platform, methanol is injected into the intake port, and diesel is directly injected into the cylinder to achieve dual-fuel combustion. The effects of the methanol energy ratios and diesel split injection strategies on combustion are investigated. The results show that the premixed blue flame was mainly concentrated in the near wall region, whereas the yellow flame produced by diesel combustion tended to concentrate in the central region as the methanol energy ratio increased. When the methanol energy ratio exceeded 50%, the ignition delay was significantly prolonged, while the flame area was greatly reduced. Meanwhile, the peak values for the cylinder pressure and heat release rate decreased significantly, indicating a significant deterioration in combustion. At the earlier diesel pre-injection timing at −58°, the overall dual-fuel combustion at each main injection timing exhibited low-temperature premixed combustion characteristics, with a lower peak exothermic rate and flame brightness. At the later pre-injection timing at −33°, the spray flame at all main injection timings could be observed, with higher peak heat release rates and indications of thermal efficiency. Combustion at later main injection timings was characterized by diffusion combustion, and the main injection timing could effectively regulate the combustion process through phase adjustment.