Google Earth Engine Cloud Platform Research Articles

Remote Sensing-Based Monitoring of Cotton Growth and Its Response to Meteorological Factors

Cotton is an important economic crop and strategic resource. Monitoring its growth and analysing its response to meteorological factors are crucial for field management and yield estimation. This study selects the primary cotton-producing regions in northern Xinjiang as the study area. Firstly, using the Google Earth Engine cloud platform, the Cotton Mapping Index (CMI) was utilised to extract cotton planting areas from 2019 to 2023. Secondly, Sentinel-2A data were used to calculate the NDVI of cotton during the growing season and analyse its variation characteristics. Finally, correlation, lag, and partial correlation analyses were conducted between cotton NDVI and meteorological factors, including effective accumulated temperature, wind speed, precipitation, and solar shortwave radiation, to explore the response relationship. The results indicate the following: (1) The optimal classification threshold of CMI in the study area was determined to be 0.74, which was applied to extract cotton planting areas over the years. The overall classification accuracy achieved was 84.85%. The R2 value for the cotton area extracted by CMI compared to the cotton planting area in the statistical yearbook data is 0.98, with an average relative error of 16.84%. CMI’s classification use effectively distinguishes cotton from other major crops, such as wheat and corn, in the study area. Compared with different classification methods, CMI is more convenient and efficient for extracting cotton planting areas, contributing significantly to yield estimation and management. (2) We found that from 2019 to 2023, some fields were planted with cotton yearly. In order to prevent land degradation, a crop rotation system should be implemented, in which cotton rotates with other crops to reduce the rate of soil nutrient loss and achieve sustainable agricultural development. (3) NDVI can effectively monitor the spatiotemporal changes and regional variations in cotton growth. Sentinel-2 multi-spectral imagery possesses high spatial and temporal resolution, enabling effective monitoring of cotton growth, provision of cotton growth data for field managers, and application in cotton production management. Additionally, cotton yield estimation can be achieved by comparing the overall growth of cotton across different years. (4) Cotton NDVI exhibits a strong correlation with effective accumulated temperature and solar radiation, with the majority passing the significance test, suggesting a significant promotion effect on cotton growth by accumulated temperature and solar radiation. In cotton cultivation management, attention should be directed toward monitoring changes in accumulated temperature and solar radiation. Moreover, NDVI changes in response to solar radiation exhibit a certain lag. The correlation between NDVI and precipitation is low, likely attributed to local cotton cultivation primarily relying on drip irrigation. Cotton NDVI is negatively correlated with wind speed. Cotton planting should consider weather changes and take corresponding preventive management measures. The research results have significant reference value for monitoring cotton growth, disaster prevention, and sustainable agricultural development.

Identification of Key Areas for Ecological Conservation and Restoration based on Multi-source Data and Remote Sensing Time Series Analysis

Abstract. Ecological conservation and restoration of national land space is an important aspect for implementing the national ecological civilization strategy, and an important measure for establishing the national ecological security pattern and coordinating the systematic management of mountains, water, forests, fields, lakes, grass and sand. The identification of key areas for ecological conservation and restoration is a precondition to spatially and temporally laying out ecological conservation and restoration planning, which is crucial for the ecological conservation and restoration of national land space. Therefore, this paper considers Henan Province as the study area and carries the research and practice of identifying key areas for ecological conservation and restoration by integrating multi-source data and remote sensing time series analysis. Firstly, based on the Google Earth Engine (GEE) cloud platform and MOD13A1 data, the Mann-Kendall method is used to analyze the trend of time series NDVI in the study area from 2011 to 2020. Secondly, based on multi-source data such as meteorological, soil, topographic, net primary production (NPP), etc., an ecosystem health analysis and assessment based on ecosystem service trade-off and synergy is used to select ecological source. Finally, based on the results of time series NDVI analysis and ecological source selection, fusion analysis is used to identify key areas for ecological conservation and restoration. The results show that this proposed research framework not only considers the “static” ecosystem services and ecosystem health attributes, but also measures the “dynamic” ecosystem change trends. It can effectively identify key areas for ecological conservation and restoration in national space. The results of the study can provide technical support for the background survey, problem identification, planning and engineering layout of ecological conservation and restoration in the national land space.

Geospatial Assessment of Urban Sprawl: A Case Study of Herat City, Afghanistan

This study aims to investigate the spatial and temporal dynamics of urban sprawl in Herat City, Afghanistan, from 2000 to 2021 using GIS and remote sensing data (Landsat 7 and 8). In this study, three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART), were employed to classify the study area, and the accuracy of each algorithm for each study period was assessed. Based on the assessment results, the RF algorithm demonstrated higher accuracy and was selected as the classification algorithm. The Google Earth Engine cloud platform was utilized to classify the study area, and the GIS environment was employed for the creation of thematic layers. The analysis revealed a 30.06% increase in built-up areas from 2000 to 2021. Conversely, vegetation, water bodies, and bare land decreased by 8.51%, 1.08%, and 20.53%, respectively, during the same period. The findings indicated that Herat City experienced high-speed expansion between 2000 and 2013, while from 2013 to 2021; it developed at a medium speed. The Relative Shannon's entropy statistical algorithm was employed to quantify urban sprawl, and the results suggest a dispersed urban sprawl pattern. Internal migration to major cities due to conflicts, limited employment opportunities, and inadequate living amenities in rural areas has been a primary driver of urban sprawl in Herat City, Afghanistan.

Land-Use Transitions Impact the Ecosystem Services Value in a Coastal Region by Coupling the Geo-Informatic Tupu and Benefit-Transfer Method: The Case of Ningde City, China

Exploring the mechanisms and processes of land-use transitions (LUTs) and their impact on ecosystem services can effectively elucidate the intricate interactions between human and natural systems, which is pivotal for advancing the sustainable development of regional economies and enhancing ecological environments. However, the existing literature lacks comprehensive analysis regarding the spatial and temporal evolution of LUTs, with insufficient integration of the “spatial pattern” and “time process”. Moreover, traditional assessments of the ecosystem services value (ESV) often overlook their negative costs. To address these gaps, this study first utilized the Google Earth Engine (GEE) cloud platform and employed the random forest algorithm to conduct supervised classification on Landsat remote-sensing images from the years 2000, 2010, and 2020 within the research area, thereby obtaining land-use data for three distinct periods. And then, we investigated the geographic features of LUTs and their ecological effects in the Ningde City of China from 2000 to 2020. The geo-informatic Tupu model and a newly revised method of benefit transfer were primarily employed for this purpose. The findings indicate the following: (1) Over the study period, the land-use structure of Ningde City predominantly comprised cultivated land and forest land, with continuous decreases in both types and a concurrent increase in built-up land. (2) Significant disparities exist in the spatial distribution of Tupu units, notably with “forest land → cultivated land” and “cultivated land → built-up land” as crucial units influencing ESV changes. (3) The ESV in Ningde City decreased from CNY 1105.54 × 108 to CNY 1020.47 × 108 over 2000–2020, while the ecosystem dis-services value exhibited an opposing trend, rising from CNY 12.68 × 108 to CNY 20.39 × 108. (4) The net ESV in Ningde City showed a decline over the same period, indicating a certain vulnerability in the city’s ecological system structure. This study aims to enhance our understanding of the influence of land-use patterns on ESV, offering valuable insights for regional ecological–environment management and land-use policy formulation, thereby fostering sustainable development in ecological, environmental, and socio-economic dimensions. Furthermore, the results serve as a reference for evaluating net ecosystem services value in other countries/regions.

Developing a Wilderness Quality Index for Continental Europe

This paper presents an updated wilderness quality map, WQI 2.0, for Europe, which extends the existing map (WQI 1.0) to include non-EU states in Eastern Europe. The analysis utilizes the Google Earth Engine (GEE) cloud platform and incorporates contemporary datasets to assess wilderness quality across the continent. WQI 2.0 is compared to the previous version from the EU Wilderness register and global data from the WCS Human Influence Index (HII). Results indicate a high level of consistency between the versions, validating the robustness of the approach and the value of up-to-date datasets. WQI 2.0 serves as a valuable tool for developing a coordinated European policy on wilderness protection, encompassing both EU and non-EU states. By identifying areas outside current protected boundaries, the map helps to identify regions at risk of degradation and loss, due to resource exploitation. While small changes are seen between WQI 1.0 and WQI 2.0, expanding the coverage over the whole of continental Europe provides a foundation for the longer-term monitoring and evaluation of conservation targets. The findings contribute to meeting international commitments, such as the COP15 Kunming–Montreal Agreement and CBD targets, by highlighting the importance of preserving intact wilderness areas and increasing protected areas through restoration and rewilding efforts. Future iterations, such as WQI 3.0+, can track trends and potential threats to wilderness areas, while also identifying opportunities for ecosystem recovery through restoration and rewilding. To ensure comprehensive coverage, there is a need to update the existing Wilderness Register 1.0 and expand its scope to include non-EU states. This can be facilitated through collaboration with national WQI mapping programs, building on the experiences of countries such as Scotland, France, Iceland, and Germany, which have well-established national mapping initiatives. Overall, WQI 2.0 and the proposed updates provide valuable tools for informed decision-making in wilderness conservation and restoration efforts across Europe.

Spatio-Temporal Evolution Characteristics and Driving Factors of Typical Karst Rocky Desertification Area in the Upper Yangtze River

Karst rocky desertification (KRD) has become the most serious ecological disaster in the southwest of China and is a major obstacle to the sustainable development of the karst region in the southwest. Remarkably, scientific understanding of the spatial-temporal evolution of rocky desertification and the corresponding driving mechanism is the primary prerequisite crucial to controlling rocky desertification. Hence, the typical rocky desertification area of Qujing City, located in the upper reaches of the Yangtze River, was selected as the research object. On the basis of the Google Earth Engine (GEE) cloud platform and decision tree classification, the spatial-temporal evolution process of rocky desertification in Qujing City from 1990 to 2020 was investigated, and the driving factors of rocky desertification were explored in terms of the natural environment and socio-economic aspects. Consequently, over this period, the area of rocky desertification had decreased by 1728.38 km2, while the no rocky desertification area had increased by 1936.61 km2. Notably, the major driving factors of rocky desertification were fractional vegetation cover (FVC) (q = 0.41), land use type (q = 0.26), slope (q = 0.21), and land reclamation rate (q = 0.21). Typically, rocky desertification is likely to occur in areas with moderate or low FVC (<0.7), a low slope (0–8°) or high slope (35°–80°), a land type of cultivated-land or grassland, and a land reclamation rate of 10–70%. In addition, all two-factor interactions acted as drivers that exacerbate rocky desertification. Furthermore, FVC ∩ slope (q = 0.79) and slope ∩ land use type (q = 0.56) were two interacting drivers that promote rocky desertification strongly.

Geomorphological changes and landscape ecosystem services dynamics of the Bhasan Char Island in the Northern Bay of Bengal, Bangladesh

Geomorphological changes have had numerous environmental impacts on landscape features, natural resources, landform stability, and ecosystem services on Bhasan Char Island in the Northern Bay of Bengal, Bangladesh. Therefore, the present study examines the morphological change of the island and its impacts on landscape features, landform stability, and coastal mangrove forest resources since its evolution. Optical satellite images from Landsat (TM and OLI) spanning from 2003 to 2023 have been used to assess geomorphological parameters using the bio-physical indices NDWI and MNDWI. A hybrid classification method involving unsupervised classification, supervised machine learning, and post-classification refinement was employed to precisely delineate landscape features and evaluate ecosystem services. Indeed, to evaluate forest cover health dynamics, the leaf area index (LAI) was estimated using the Google Earth Engine (GEE) cloud platform. The study results reveal that the geomorphology of the island was changed by fluvial and marine processes and responses, whereas only 3% of the landscape ages older than 19 years. Results also disclosed that built-up area construction for Rohingya nations drastically lost ∼ 7% of mangrove plantations over the last 8 years, from 2015 to 2023. The current research delivers crucial management strategies regarding further structural development by evading unplanned construction, hastening reforestation, and evolving the green belt along the existing embankment. The study findings will help policymakers, environmental managers, coastal and marine resource planners, ecologists, and geomorphologists take an eco-friendly sustainable development plan and conserve the coastal island and its landscape resources from the risk of fluvial and marine processes.

Understanding urban expansion and shrinkage via green plastic cover mapping based on GEE cloud platform: A case study of Shandong, China

Green Plastic Cover (GPC) has been widely used in construction sites of China as the main measure for dust suppression. As a unique land cover in China, GPC could be viewed as a sign for evaluating the intensity of urban construction. Therefore, time series GPC maps could provide a new perspective to understand the pattern of urban development, including both urban expansion and urban shrinkage. The objective of this study is to present a novel framework for detection of urban development patterns based on high resolution GPC maps generated from Google Earth Engine (GEE) cloud platform. Specifically, we first generated the GPC maps of 137 districts and counties in Shandong Province of China from 2018 to 2022 by combining time series spectral features and textural features derived from Sentinel-1 and Sentinel-2 images provided by GEE cloud platform. Afterwards, we designed a GPC-based urban development vigor index (GPC-UDVI) to evaluate the vitality of each city, which offers a novel perspective to identify shrinking cities. Experimental results demonstrated that the average overall accuracy of GPC maps is 89.696 %, which is accurate enough for the following analysis. Besides, the proposed GPC-UDVI exhibits a consistent trend with statistical data and could reveal urban development patterns with a high performance. Above all, dynamic monitoring of urban GPC through remote sensing is significant for understanding urban expansion modes and aiding in urban reconstruction management.

Improving river medium–high flow estimation by CM Hierarchical Classification (CMHC) method using Sentinel-2 imagery

River discharge monitoring is one of the most important parts of water resources management. However, due to hydrological stations' high operating and maintenance costs, discharge monitoring data has gradually become scarce. Remote sensing technology has great potential to supplement observations in this field. The close relationship between the reflectance ratio of dry (C) and wet (M) pixels around the river in the near-infrared (NIR) band and river discharge has been effectively proved, and many CM-based methods have also achieved good results, but often underestimate medium–high flow in rivers. However, medium–high flow usually occurs during the rainy season or flood-prone periods, and their underestimation not only affects the risk assessment of possible or already occurred floods but may also cause damage to people's lives, property, and social assets. To improve this phenomenon, we proposed a new CM method called CM Hierarchical Classification (CMHC) based on the Sentinel-2 dataset and Google Earth Engine (GEE) cloud platform to monitor the river discharge at different classes and achieve high-precision discharge estimation at medium–high flow. The study was conducted at four stations along the Po River in Italy, where measured discharge data were used to derive the calibration curve and evaluate the performance of the estimated results. The Nash-Sutcliffe efficiency (NS) values for the estimates reached an average of 0.84 and up to 0.89, indicating the excellent performance of the method. Compared with existing methods, our method substantially improves the monitoring accuracy of medium–high flow of the river. Furthermore, additional experiments in the Han River in China also confirmed the applicability of the CMHC method, implying that this method can be used to improve the ability of Sentinel-2 to estimate river discharge, especially for medium–high flow, and provide support for river discharge monitoring on a global scale.

Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data.

Despite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (Cab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March-April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35µg/cm2. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R2 of 0.61-0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices.

Long-Term Analysis of Regional Vegetation Correlation with Climate and Phenology in the Midsection of Maowusu Sandland

It is essential to monitor the dynamics of vegetation at different scales in space and time to promote the sustainable development of terrestrial ecosystems. We used the Google Earth Engine (GEE) cloud platform to perform a comprehensive analysis of the changes in normalized difference vegetation index (NDVI) Mann-Kendall (MK) + Sen trend in the hinterland region of the Maowusu sandland in China over the last two decades. We performed bias-correlation studies using soil and climate data. Furthermore, we performed a partial Mantel test to analyze the spatial and temporal fluctuations of vegetation health-related indices. Additionally, we developed a logistic dual model of the phenology index using the Lenvenberg–Marquardt technique. The objective was to uncover the factors contributing to the regional shifts in vegetation dynamics. We provide a comprehensive analytic method designed to monitor vegetation over some time and forecast its future recovery. The findings indicate that over the past 20 years, more than 90% of the regional NDVI in the study area has exhibited a consistent and significant upward trend. This trend is primarily influenced by the adverse impact of temperature and the beneficial impact of precipitation. Additionally, long-term phenological indicators in the study area reveal that the vegetation’s growth cycle commences on the 125th day of the year and concludes on the 267th day of the year. This suggests that the shorter duration of the vegetation’s growth season may be attributed to the local climate and unfavorable groundwater depth conditions. levated temperatures throughout the next spring and autumn seasons would significantly affect the wellbeing of plants, with soil moisture being a crucial determinant of plant development in the examined region. This study presents a wide range of analytical tools for monitoring vegetation over a long period and predicting its future recovery. It considers factors such as vegetation health, phenology, and climatic influences. The study establishes a solid scientific foundation for understanding the reasons behind regional vegetation changes in the future.

2000–2020年丹江口水库（河南部分）水体面积数据集

Danjiangkou Reservoir serves as the water source of the South-to-North Water Diversion Project. The long-term monitoring of changes of surface water for the reservoir using the remote sensing images is of great significance in ensuring the water transfer volume of the South-to-North Water Diversion. In this study, we used multi-temporal Landsat remote sensing images to obtain a long-time series of spatial surface water datasets for Danjiangkou Reservoir (Henan ) based on Google Earth engine cloud platform through the relationship among modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI). This dataset includes the surface water of Danjiangkou Reservoir from 2000 to 2020. It can provide a basis for exploring the spatiotemporal changes of the surface water and ecological environment changes in Danjiangkou Reservoir.

2000–2020年海南岛天然橡胶人工林分布变化数据集

Natural rubber plantations are one of the primary forest ecosystems on Hainan Island and serve as a burgeoning focal point for ecosystem services in the Tropics. Therefore, it is crucial for regional ecological environment and economic development to accurately monitor and map the spatial and temporal pattern of rubber plantations. Employing Landsat TM/OLI image data acquired through the Google Earth Engine (GEE) cloud platform spanning from 1998 to 2022 in Hainan Island, coupled with information from the Third National Land Survey Report, we obtained a dataset of distribution changes of natural rubber plantations in Hainan Island from 2000 to 2020, by using a random forest classification model. The dataset demonstrates a relatively high accuracy, with an overall accuracy of 96.93%; Specifics include a producer's accuracy of 89.10% and a user's accuracy of 94.72%. The rubber plantation area in Hainan Island has exhibited a growing trend, increasing from 3,661.65 km2 in 2000 to 7,422.02 km2 in 2020. This dataset can provide fundamental support for monitoring, managing, and decision-making concerning rubber plantation ecosystems.

2001–2020年中国东北区域土壤水蚀数据集

The black soil area in Northeast China plays an important role in food production and ecological security in China. However, over-cultivation practices have led to severe soil erosion, which seriously threatens food security and ecological environment in Northeast China. Accurate simulations of water erosion with high spatio-temporal resolution are important for advancing sustainable development goals, such as promoting sustainable agriculture and monitoring land degradation in Northeast China. This dataset is based on Google Earth Engine (GEE) cloud platform, integrating multi-source remote sensing data, deconstructing RUSLE model, and optimizing algorithm combinations for each factor of the model. We compared and verified the annual sand transport volume and sand transport modulus data from the main hydrological monitoring stations in Northeast China with the soil erosion modulus estimated by the RUSLE model. Then, we selected the optimal factor algorithm combination based on three accuracy metrics (time series correlation, root mean square error and mean absolute error), and obtained the estimation results of soil water erosion modulus with the resolution of 250 m. This dataset can better depict the spatial distribution and temporal changes of soil erosion modulus in Northeast China from 2001 to 2020. It can serve as an effective reference for soil erosion control and assessments in Northeast China.

Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province.

The ongoing global warming trajectory poses extensive challenges to plant ecosystems, with rubber plantations particularly vulnerable due to their influence on not only the longevity of the growth cycle and rubber yield, but also the complex interplay of carbon, water, and energy exchanges between the forest canopy and atmosphere. However, the response mechanism of phenology in rubber plantations to climate change remains unclear. This study concentrates on sub-optimal environment rubber plantations in Yunnan province, Southwest China. Utilizing the Google Earth Engine (GEE) cloud platform, multi-source remote sensing images were synthesized at 8-day intervals with a spatial resolution of 30-meters. The Normalized Difference Vegetation Index (NDVI) time series was reconstructed using the Savitzky-Golay (S-G) filter, coupled with the application of the seasonal amplitude method to extract three crucial phenological indicators, namely the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS). Linear regression method, Pearson correlation coefficient, multiple stepwise regression analysis were used to extract of the phenology trend and find the relationship between SOS, EOS and climate factors. The findings demonstrated that 1) the phenology of rubber plantations has undergone dynamic changes over the past two decades. Specifically, the SOS advanced by 9.4 days per decade (R2 = 0.42, p< 0.01), whereas the EOS was delayed by 3.8 days per decade (R2 = 0.35, p< 0.01). Additionally, the LOS was extended by 13.2 days per decade (R2 = 0.55, p< 0.01); 2) rubber phenology demonstrated a notable sensitivity to temperature fluctuations during the dry season and precipitation patterns during the rainy season. The SOS advanced 2.0 days (r =-0.19, p< 0.01) and the EOS advanced 2.8 days (r =-0.35, p< 0.01) for every 1°C increase in the cool-dry season. Whereas a 100 mm increase in rainy season precipitation caused the SOS to be delayed by 2.0 days (r = 0.24, p< 0.01), a 100 mm increase in hot-dry season precipitation caused the EOS to be advanced by 7.0 days (r =-0.28, p< 0.01); 3) rubber phenology displayed a legacy effect of preseason climate variations. Changes in temperature during the fourth preseason month and precipitation during the fourth and eleventh preseason months are predominantly responsible for the variation in SOS. Meanwhile, temperature changes during the second, fourth, and ninth preseason months are primarily responsible for the variation in EOS. The study aims to enhance our understanding of how rubber plantations respond to climate change in sub-optimal environments and provide valuable insights for sustainable rubber production management in the face of changing environmental conditions.

Spatiotemporal disturbances and attribution analysis of mangrove in southern China from 1986 to 2020 based on time-series Landsat imagery

As one of the most productive ecosystems in the world, mangrove has a critical role to play in both the natural ecosystem and the human economic and social society. However, two thirds of the world's mangrove have been irreversibly damaged over the past 100 years, as a result of ongoing human activities and climate change. In this paper, adopting Landsat for the past 36 years as the data source, the detection of spatiotemporal changes of mangrove in southern China was carried out based on the Google Earth Engine (GEE) cloud platform using the LandTrendr algorithm. In addition, the attribution of mangrove disturbances was analyzed by a random forest algorithm. The results indicated the area of mangrove recovery (5174.64 hm2) was much larger than the area of mangrove disturbances (1625.40 hm2) over the 35-year period in the study area. The disturbances of mangrove in southern China were dominated by low and low-to-medium-level disturbances, with an area of 1009.89 hm2, accounting for 57.50 % of the total disturbances. The mangrove recovery was also dominated by low and low-to-medium-level recovery, with an area of 3239.19 hm2, accounting for 62.61 % of the total recovery area. Both human and natural factors interacted and influenced each other, together causing spatiotemporal disturbances of mangrove in southern China during 1986–2020. The mangrove disturbances in the Phase I (1986–2000) and Phase III (2011–2020) were characterized by human-induced (50.74 % and 58.86 %), such as construction of roads and aquaculture ponds. The mangrove disturbances in the Phase II (2001–2010) were dominated by natural factors (55.73 %), such as tides, flooding, and species invasions. It was also observed that the area of mangrove recovery in southern China increased dramatically from 1986 to 2020 due to the promulgation and implementation of the Chinese government's policy on mangrove protection, as well as increased human awareness of mangrove wetland protection.

Mapping Main Grain Crops and Change Analysis in the West Liaohe River Basin with Limited Samples Based on Google Earth Engine

It is an important issue to explore achieving high accuracy long-term crop classification with limited historical samples. The West Liaohe River Basin (WLRB) serves as a vital agro-pastoral ecotone of Northern China, which experiences significant changes in crop planting structure due to a range of policy. Taking WLRB as a case study, this study constructed multidimensional features for crop classification suitable for Google Earth Engine cloud platform and proposed a method to extract main grain crops using sample augmentation and model migration in case of limited samples. With limited samples in 2017, the method was employed to train and classify crops (maize, soybean, and rice) in other years, and the spatiotemporal changes in the crop planting structure in WLRB from 2014 to 2020 were analyzed. The following conclusions were drawn: (1) Integrating multidimensional features could discriminate subtle differences, and feature optimization could ensure the accuracy and efficiency of classification. (2) By augmenting the original sample size by calculating the similarity of the time series NDVI (normalized difference vegetation index) curves, migrating the random forest model, and reselecting the samples for other years based on the model accuracy scores, it was possible to achieve a high crop classification accuracy with limited samples. (3) The main grain crops in the WLRB were primarily distributed in the northeastern and southern plains with lower elevations. Maize was the most predominant crop type with a wide distribution. The planting area of main grain crops in the WLRB exhibited an increasing trend, and national policies primarily influenced the variations of planting structure in maize and soybean. This study provides a scheme for extracting crop types from limited samples with high accuracy and can be applied for long-term crop monitoring and change analysis to support crop structure adjustment and food security.

Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data

Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study’s results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix’s evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of staminate pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency.

A Method of Chestnut Forest Identification Based on Time Series and Key Phenology from Sentinel-2

Chestnut trees hold a prominent position in China as an economically significant forest species, offering both high economic value and ecological advantages. Identifying the distribution of chestnut forests is of paramount importance for enhancing efficient management practices. Presently, many studies are employing remote sensing imaging methods to monitor tree species. However, in comparison to the common classification of land cover types, the accuracy of tree species identification is relatively lower. This study focuses on accurately mapping the distribution of planted chestnut forests in China, particularly in the Huairou and Miyun regions, which are the main producing areas for Yanshan chestnuts in northeastern Beijing. We utilized the Google Earth Engine (GEE) cloud platform and Sentinel-2 satellite imagery to develop a method based on vegetation phenological features. This method involved identifying three distinct phenological periods of chestnut trees: flowering, fruiting, and dormancy, and extracting relevant spectral, vegetation, and terrain features. With these features, we further established and compared three machine learning algorithms for chestnut species identification: random forest (RF), decision tree (DT), and support vector machine (SVM). Our results indicated that the recognition accuracy of these algorithms ranked in descending order as RF &gt; DT &gt; SVM. We found that combining multiple phenological characteristics significantly improved the accuracy of chestnut forest distribution identification. Using the random forest algorithm and Sentinel-2 phenological features, we achieved an impressive overall accuracy (OA) of 98.78%, a Kappa coefficient of 0.9851, and a user’s accuracy (UA) and producer’s accuracy (PA) of 97.25% and 98.75%, respectively, for chestnut identification. When compared to field surveys and official area statistics, our method exhibited an accuracy rate of 89.59%. The implementation of this method not only offers crucial data support for soil erosion prevention and control studies in Beijing but also serves as a valuable reference for future research endeavors in this field.

Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ &gt; 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences.