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Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management. [ABSTRACT FROM AUTHOR]

Efficiently and accurately identifying the spatial distribution of tea plantations in the subtropical plateau regions of southwest China is of great significance for ecological and environmental protection. However, the lands of those regions are fragmented with complex vegetation types. Moreover, there is much cloudy and rainy weather over those areas, making it very difficult to identify tea plantations using only optical remote sensing data. In order to solve these problems, this paper uses Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data and Sentinel-2 (S2) optical data to design seven classification feature combinations to explore the influence of red edge features, radar features and texture features on the identification accuracy of tea plantations. The feasibility of Jeffreys-Matusita distance (JM) feature selection and Recursive Feature Elimination (RFE) feature selection algorithm to find the optimal feature combination is verified, and the distribution of tea plantations in the study area is acquired by using the object-oriented random forest algorithm. The study shows that (1) the combination of SAR data and optical data can effectively improve the identification accuracy of tea plantations. (2) S2 red edge features and S1 radar features can significantly improve the accuracy of the identification results of tea plantations. (3) After applying the JM distance and RFE feature selection algorithms, the producer's accuracy of tea plantations is improved by 1.39% and 2.38%, and the user's accuracy is improved by 1.02% and 1.3%, respectively, compared with the identification of all features. The overall accuracy of the random forest algorithm combined with RFE is 93.43%. This study proposes the application of feature selection algorithms in identification of tea plantations, which improves accuracy and increases efficiency while minimizing redundant features and provides an effective approach to identify tea plantations in cloudy and rainy areas in the subtropical plateau of southern China. [ABSTRACT FROM AUTHOR]

Accurate paddy rice mapping is crucial for ensuring food security and guiding agricultural production. Vegetation indices are extensively employed to map paddy rice. However, most traditional normalized indices tend to be oversaturated during periods of lush vegetation due to normalization errors, resulting in uncertainties in paddy rice mapping. To address this issue, we introduce a novel red-edge rice index (RERI) in this study; this index comprises information from red, near-infrared, and red-edge bands without normalization. To extract single- and double-cropping rice features from potential rice areas, we employ GF-6 and Sentinel-2 images based on the proposed RERI and the random forest algorithm. The proposed method is validated in the Dingcheng District of Changde city, China, and the results are compared with those based on three normalized vegetation indices. The results show that the RERI yielded the highest levels of the accuracy for all the metrics, achieving an overall accuracy (OA) of 92.50% and a kappa coefficient of 0.8875. The RERI exhibited F1 scores of 92.26% for single-cropping rice, 93.00% for double-cropping rice, and 92.28% for non-rice areas. Our results highlight the potential of using the RERI for rice identification, and the effectiveness of our method for rice extraction is demonstrated.

Soil salinization is an important factor that jeopardizes agricultural production and ecological environment. Rapid and accurate acquisition of soil salinity information in farmland is instructive for sustainable agricultural development and land resource management. In order to improve the accuracy of soil salinity prediction under vegetation cover conditions by satellite remote sensing, the eighth Agricultural Division of Xinjiang Production and Construction Corps was taken as the study area in this study. The soil surface (0-20 cm) samples were collected under high fractional vegetation cover conditions in July and August 2023, respectively, and synchronized satellite images were acquired. Sentinel-1, Sentinel-2 and environment variables provide 3 different types of explanatory variables. The dataset A (polarization indices, spectral indices), dataset B (polarization indices, environment variables), dataset C (spectral indices, environment variables), and dataset D (polarization indices, spectral indices, environment variables) were constructed separately from different combinations of Sentinel-1 radar information, Sentinel-2 multispectral information and environment variables. Then, three integrated machine learning algorithms, namely adaptive boosting (AdaBoost), gradient boost regression Tree (GBRT) and eXtreme gradient boosting tree (XGBoost), were applied to construct soil salinity inversion models based on different datasets. The results showed that Models constructed from dataset B (polarization indices and environmental variables) and C (spectral indices and environmental variables) achieved higher prediction accuracies compared to dataset A (polarization indices and spectral indices). It is shown that when environmental variables are involved in the prediction of soil salinity, the model effect is more effective than the model constructed by polarization and spectral indices suggesting that the model effects are more effective than those constructed from polarization and spectral indices. When environmental variables were applied to dataset D together with polarization indices and spectral indices, the prediction accuracy of all models constructed based on dataset D are generally higher than those constructed on dataset A, B, and C, and that the synergy of environmental variables with radar data and multispectral data can effectively improve the model accuracy. Radar information, spectral information and environmental variables are complementary in soil salinity prediction. Based on the correlation analysis, it can be seen that radar information, spectral information and environmental variables can be used as effective characteristic variables for soil salinity prediction in the study area. It was worth noting that the correlation between topographic factors and land surface temperature with soil salinity is relatively high, with the highest correlation between elevation and surface soil salinity (r = 0.52). Considering the spatial characteristics of soil salinity distribution in the study area can provide effective characteristic variables for soil salinity prediction under vegetation cover condition. In all datasets, the XGBoost had the best performance, followed by GBRT, and the AdaBoost had a large validation error. The D-XGBoost model having the highest accuracy with a validation set R² of 0.72, an RMSE of 2.40 g/kg, and an MAE of 1.29 g/kg. The integrated learning algorithms based on the combination of multiple source variables has a strong nonlinear fitting ability. XGBoost can better model the complex nonlinear relationship between soil salinity content and remote sensing information, environmental factors, and obtain ideal fitting results. The joint application of multi-source remote sensing data and integrated learning algorithms can obtain the ideal soil salinity inversion accuracy under vegetation cover conditions. This study provides an effective technical means for real-time dynamic monitoring of soil salinity by satellite remote sensing in farmland to optimize irrigation strategies and manage saline soils comprehensively in Xinjiang. [ABSTRACT FROM AUTHOR]

The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reach 0.97, 0.95, 0.98, and 0.90, respectively. The performance of the model transfer is good, with the F1-score decreasing from 0 to 0.04 and no change in EIT for different crops. It was also found that the EIT of crops obtained using S1 data alone was 50–90 days later than that obtained using S2 data alone. Additionally, when S1 and S2 were used jointly, S1 data contributed little to early crop identification. This study highlights the potential of early crop mapping using satellite data, which provides a feasible solution for the early identification of crops in the HID area and valuable information for food security assurance in the region. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Validating the satellite soil moisture products is always an active research topic for the application of the products and improvement of the retrieval algorithms, attracting extensive attention. Nevertheless, seldom existing validation activities focus on the validation of high-resolution soil moisture products at the fine scale. To this end, an experiment was conducted in the middle stream of the Heihe River Basin in northwestern China in August to October of 2021, aiming to validate high-resolution satellite remote sensing products of soil moisture. The paper introduces the design, composite, and preliminary results of the experiment. A soil moisture observation network was established with two kinds of sensors (CS616 and Stevens Hydra Probe) validated against soil core measurements. Several synchronized campaigns were performed, and data were collected to validate the SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 and 1 km EASE-Grid Soil Moisture (SPL2SMAP_S) products. Besides, an optical trapezoid model (OPTRAM) and collected Sentinel-2 data were applied to estimate soil moisture and to map irrigated area. Preliminary analyses show that: 1) Steven probes perform best, with an RMSE = 0.040 m3m−3 and ubRMSE=0.034 m3m−3; 2) Both the SPL2SMAP_S products at 3 km and 1 km show large RMSE (0.128 m3m−3 for 3 km and 0.158 m3m−3 for 1 km) and ubRMSE (0.115 m3m−3 for 3 km and 0.158 m3m−3 for 1 km); 3) The OPTRAM retrievals over bare surface present relatively smaller RMSE (0.06 m3m−3) and ubRMSE (0.057 m3m−3), while retrievals over vegetated croplands present a relatively large RMSE/ubRMSE (0.083/0.083 m3m−3), and the retrievals can identify the irrigated area at field scale. Overall, the experiment provides fruitful methodologies and datasets for the validation of high-resolution remote sensing products, benefiting the development and improvement of soil moisture retrieval algorithms and products to support irrigation scheduling and management at a precision agricultural scale in the future.

Upland crop-rice cropping systems (UCR) facilitate sustainable agricultural intensification. Accurate UCR cultivation mapping is needed to ensure food security, sustainable water management, and rural revitalization. However, datasets describing cropping systems are limited in spatial coverage and crop types. Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples, which limits its applications over large regions. We describe a novel algorithm (RRSS) for automatic mapping of upland crop–rice cropping systems using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) data. One indicator, the VV backscatter range, was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR. The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions. This study developed 10-m UCR maps of a major rice bowl in South China, the Xiang-Gan-Min (XGM) region. The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites, with an overall accuracy of 91.92%. There were 7348 km2 areas of UCR, roughly one-half of them located in plains. The UCR was represented mainly by oilseed-UCR and tobacco-UCR, which contributed respectively 69% and 15% of UCR area. UCR patterns accounted for only one-tenth of rice production, which can be tripled by intensification from single rice cropping. Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm, which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Acquiring a comprehensive understanding of cropping patterns and their spatiotemporal distribution is crucial for sustainable agricultural development and ecological environment protection. However, the similarity of crop spectra and the diversity of ecosystem types hinder the accurate mapping of cropping patterns, especially in agricultural landscape regions. Hence, taking Xinghua County as study area, this article proposed a novel method for integrating multidimensional feature indices and phenological windows, named phenological window feature (PWF), to achieve efficient and accurate mapping of cropping patterns. In this study, we adopt a two-step approach. First, time-series curves of feature indices were constructed using Sentinel-1/2 satellite data to determine the phenological windows of different cropping patterns and construct PWF sets. Then, the ruleset threshold method (RTM) and random forest (RF) algorithms were used to map cropping patterns including wheat-rice, crayfish-rice, vegetable-rice, rice-rapeseed, rapeseed-vegetable, and year-round vegetables. The results indicate that the phenological windows extracted from the cropping patterns in the study area were 30–120, 90–135, and 200–270 days, respectively. The overall accuracies of RTM and RF, based on PWF, were 85.91% and 89.50%, respectively, and the kappa coefficients for RTM and RF were 0.831 and 0.872, respectively. In terms of classification performance, RF slightly outperformed RTM. The study demonstrates that PWF proposed in this article can be effectively utilized for mapping cropping patterns in complex agricultural landscape regions.

Accurate high-resolution maps of oil palm plantations underpin effective management of environmental and socio-economic impacts at global, regional, and national levels. However, young industrial and highly irregular small-holder plantations are mostly unmapped and not included in official FAO statistics. This issue is addressed here by discriminating global oil palm plantation in 2020 into four subclasses: Industrial Mature Oil Palm (IMOP); Industrial Young Oil Palm (IYOP); Smallholder Mature Oil Palm (SMOP); and Smallholder Young Oil Palm (SYOP). Data, resolved to 4.77 m, from Planet & NICFI, Sentinel-1/2, were combined with other layers using the image-oriented classification and regression tree (CART) algorithm which performed best in classification tests. Results show that SMOP dominates distributional extent, but it was also the most accurately mapped subclass typically found at 500–1000 m altitude. IMOP had the most extensive altitude range of 500–1300 m, while IYOP and SYOP were found at similar altitudes of 500–800 m and 500–900 m respectively. Recent developments in South East Asia show oil palm plantations expanding into new areas with a slope of 24 degrees. Results provide data to support Sustainable Development Goal by assisting future oil palm-related development planning and monitoring in the world's major oil palm-growing countries.

The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reach 0.97, 0.95, 0.98, and 0.90, respectively. The performance of the model transfer is good, with the F1-score decreasing from 0 to 0.04 and no change in EIT for different crops. It was also found that the EIT of crops obtained using S1 data alone was 50–90 days later than that obtained using S2 data alone. Additionally, when S1 and S2 were used jointly, S1 data contributed little to early crop identification. This study highlights the potential of early crop mapping using satellite data, which provides a feasible solution for the early identification of crops in the HID area and valuable information for food security assurance in the region.

Lanzhou is the only provincial capital city in Northwest China where the main stream of the Yellow River and its tributaries flow through the city. Due to its geographical location and the influence of various factors, it is difficult to evaluate and simulate the climatic, hydrological, and ecological processes of the main stream of the Yellow River and its tributaries in the region. In this study, the Wanchuan River basin, currently undergoing ecological restoration, was selected as the study area. Seasonal backscatter differences generated using Sentinel-1/2 (S1/S2) data and the CD algorithm were used to reduce the effects of surface roughness; vegetation indices, soils, and field measurements were used to jointly characterize the vegetation contribution and soil contribution. Then, SM maps with a grid spacing of 10 m × 10 m were generated in the Wanchuan River basin, covering an area of 1767.78 km2. To validate the results, optimal factors were selected, and a training set and validation set were constructed. The results indicated a high level of the coefficient of determination (R2) of 0.78 and the root mean square error (RMSE) of 0.08 for the comparison of measured and inverted water contents, indicating that the algorithm retrieved the SM values of the study area well. Furthermore, Box line plots with ERA5-Land and GLDAS confirmed that the algorithm is in good agreement with current SM products and feasibility for soil water content inversion work in the Wanchuan River basin. [ABSTRACT FROM AUTHOR]

With China's fruit tree industry becoming the largest in the world, accurately understanding the spatial distribution of fruit tree growing areas is crucial for promoting socio-economic development and rural revitalization. Remote sensing offers unprecedented opportunities for fruit tree monitoring. However, previous research has mainly focused on UAV and near-ground remote sensing, with limited accuracy in obtaining fruit tree distribution information through satellite remote sensing. In this study, we utilized the Google Earth Engine (GEE) remote sensing cloud platform and integrated data from Sentinel-1, Sentinel-2, and SRTM sources. We constructed a feature space by extracting original band features, vegetation index features, polarization features, terrain features, and texture features. The sequential forward selection (SFS) algorithm was employed for feature optimization, and a combined machine learning and object-oriented classification model was used to accurately extract fruit tree crop distributions by comparing key temporal phases of fruit trees. The results revealed that the backscatter coefficient features from Sentinel-1 had the highest contribution to the classification, followed by the original band features and vegetation index features from Sentinel-2, while the terrain features had a relatively smaller contribution. The highest classification accuracy for jujube plantation areas was observed in November (99.1% for user accuracy and 96.6% for producer accuracy), whereas the lowest accuracy was found for pear tree plantation areas in the same month (93.4% for user accuracy and 89.0% for producer accuracy). Among the four different classification methods, the combined random forest and object-oriented (RF + OO) model exhibited the highest accuracy (OA = 0.94, Kappa = 0.92), while the support vector machine (SVM) classification method had the lowest accuracy (OA = 0.52, Kappa = 0.31). The total fruit tree plantation area in Aksu City in 2022 was estimated to be 64,000 hectares, with walnut, jujube, pear, and apple trees accounting for 42.5%, 20.6%, 19.3%, and 17.5% of the total fruit tree area, respectively (27,200 hectares, 13,200 hectares, 12,400 hectares, and 11,200 hectares, respectively). The SFS feature optimization and RF + OO-combined classification model algorithm selected in this study effectively mapped the fruit tree planting areas, enabling the estimation of fruit tree planting areas based on remote sensing satellite image data. This approach facilitates accurate fruit tree industry and real-time crop monitoring and provides valuable support for fruit tree planting management by the relevant departments. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is valuable basic data in climate, hydrological models, and agricultural applications. The rapid development of remote sensing technology can be used to monitor changes in SM at multiple spatial and temporal scales. In this article, we unfolded an SM retrieval method using ensemble learning combined with the Water Cloud Model (WCM) by Sentinel-1 and Sentinel-2 with multisource datasets. First, using the WCM, the influence of vegetation cover on the backscattering coefficient was removed, where we use three vegetation index (enhanced vegetation index (EVI), normalized difference vegetation index, and normalized difference water index) for analysis and comparison. Then, combined with other multisource datasets, an SM retrieval model was established based on the ensemble learning algorithm. Here, we choose two familiar ensemble learning algorithms for analysis and comparison, using Pearson correlation significance analysis, which are the random forest (RF) and the adaptive boosting (AdaBoost). The results revealed that the RF model performed is slightly superior to the AdaBoost model. The optimal performance mean absolute error, root-mean-square error (RMSE), and the unbiased RMSE of RF model are 2.289 vol%, 2.934 vol%, 2.934 vol%, respectively, which are slightly better than the AdaBoost model. EVI is suitable for WCM model to remove vegetation scattering effect. It shows that it is attainable to utilize the ensemble learning method to inversion of SM using radar data. The proposed framework maximizes the potential of WCM, RF model, and multisource datasets in deriving spatiotemporally continuous SM estimates, which should be valuable for SM inversion development.

Soil organic carbon (SOC) is a sensitive indicator of climate change, and small changes in the soil carbon pool will affect the carbon balance. Accurate and robust SOC quantitative prediction is of great significance to studying the carbon budget of swamp wetlands and its response to climate change. In this study, a new framework was proposed and assessed for predicting the SOC content based on Sentinel-2 (S2), Sentinel-1 (S1), and the digital elevation model (DEM) together with the extreme gradient boosting with random forest (XGBRF) model. The determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), and Lin's concordance correlation coefficient (LCCC) were applied to assess the performances of the models. The results revealed that the prediction performance of the XGBRF regression model was much better than that of extreme gradient boosting and random forest regression models. Compared with single sensor data, using multisensor data to predict the SOC content yielded more accurate results. The XGBRF model based on S1, S2, and DEM fusion yielded the highest prediction accuracy (R2_testing = 0.6639, RMSE = 1.3236 g/kg, MAE = 1.2546 g/kg, LCCC = 0.7621). Regarding the importance of the variables, the S1 and S2 features were major contributors to the SOC content prediction (41% and 52%, respectively), followed by the topographic variables extracted from the DEM (7%). The proposed framework can be used for SOC prediction based on a small sample dataset, and it provides a method for long-term and rapid monitoring of the SOC contents in wetlands.

Despite human activities are key influencing factors for cropland soil organic matter (SOM), detailed characterization of human activities has always been limited in the digital mapping of SOM due to the lack of proper representations of human’s cropland use activities. Crop rotation is an essential human agricultural practice significantly affecting the spatial–temporal variations of SOM due to the periodically dynamic changes of crops. Thus, incorporating crop rotation in the digital soil mapping holds high potential for improving SOM prediction. Here, we applied time-series radar Sentinel-1 and optical Sentinel-2 to map crop rotation systems by a hierarchical rule-based method. Then we explored the effectiveness of incorporating such information in predicting SOM by implementing various combinations of predictive variables. We chose a typical multiple cropping region with various crop rotations in southern China. The model performance was evaluated by 10-fold cross-validation. Results showed significant differences in SOM among the crop rotation systems, and the single rice rotated with vegetables has the highest SOM followed by the high-diversity vegetables and long-term orchard systems. Adding crop rotation enhanced the predictability of SOM with a decrease in RMSE by 7% and an increase in R2 by 24%. Furthermore, the crop rotation systems appeared more important in the predictive models than the soil, topographic, and climatic variables. Our results demonstrated the effectiveness of including crop rotation in predicting SOM over complex agricultural landscapes. Our study indicated that human activities should be characterized more detailedly in cropland soil mapping, and that crop rotation containing information on the seasonal dynamics of cropland may be an option for such characterization.

Mangrove forests with high vegetation productivity play crucial roles in the coastal blue carbon ecosystem. Accurate monitoring of mangrove canopy phenology is essential to improve the survival rate of restoration plantings and maintain the health and sustainability of mangrove ecosystems. Remote sensing technology has been commonly used for monitoring vegetation phenology, while there remain knowledge gaps in exploring the remotely-sensed phenology of mangrove forests in coastal China. In this study, we investigated the phenological characteristics of two typical mangrove sites in coastal China using remote sensing indices based on Sentinel-1 and Sentinel-2 time series during 2016–2020, compared the performances of mangrove phenology detection across different remote sensing indices and spatial scales, and explored the influences of environmental factors based on meteorological data. The results demonstrated that enhanced vegetation index (EVI), red-edge band index (NDRE2), phenology index (NDPI), and radar vegetation index (RVI) were efficient in characterizing mangrove phenological trajectories. Sentinel-2 data can precisely describe the phenological characteristics of mangroves and has the highest correlation with ground-observed phenology data (r = −0.581), when compared to Landsat-8 and MODIS data. The meteorological factors of precipitation, humidity, and wind speed mainly led to the differences in mangrove phenology across the two study sites. This finding can improve our understanding of the phenological characteristics of mangrove forests in coastal China, which facilitates local governments to develop appropriate mangrove restoration and management policies.

Accurate and timely acquisition of cropping intensity and spatial distribution of paddy rice is not only an important basis for monitoring growth and predicting yields, but also for ensuring food security and optimizing the agricultural production management system of cropland. However, due to the monsoon climate in southern China, it is cloudy and rainy throughout the year, which makes it difficult to obtain accurate information on rice cultivation based on optical time series images. Conventional image synthesis is prone to omission or redundancy of spectral and temporal features that are potentially important for rice-growth identification, making it difficult to determine the optimal features for high-precision mapping of paddy rice. To address these issues, we develop a method to granulate the effective use interval of classification features by extracting phenological signatures of rice to obtain cost-effective and highly accurate mapping results. Two steps are involved in this method: (1) analyzing the information on various features (spectra, polarization, and seasonal regularity) to identify three key phenological periods throughout the lifespan of paddy rice; (2) identifying the features with the highest class separation between paddy rice, non-paddy crops, and wetlands under different phenological stages; and (3) removing redundant features to retain the optimal feature combinations. Subsequently, the obtained feature sets are used as input data for the random forest classifier. The results showed that the overall accuracy of the identified rice results was 95.44% with F1 scores above 93% for both single- and double-cropping rice. Meanwhile, the correlation coefficient of our mapped rice area compared with the official statistics of rice area at county and district levels was 0.86. In addition, we found that combining Sentinel-1 and Sentinel-2 images for rice recognition was better than using Sentinel-1 or Sentinel-2 alone, and the classification accuracy was improved by 5.82% and 2.39%, which confirms that the synergistic Sentinel-1 and Sentinel-2 data can effectively overcome the problem of missing optical images caused by clouds and rain. Our study demonstrates the potential of distinguishing mixed rice-cropping systems in subtropical regions with fragmented rice-field distribution in a cloudy and rainy environment, and also provides a basis for the rational layout of rice production and improvement of cultivation systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, "calcl") from the "late winter–spring" time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view. [ABSTRACT FROM AUTHOR]

Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R2 = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction. [ABSTRACT FROM AUTHOR]

Accurate and efficient large-scale mapping of P. euphratica distribution is of great importance for managing and protecting P. euphratica forests, policy making, and realizing sustainable development goals in the ecological environments of desert areas. In large regions, numerous types of vegetation exhibit spectral characteristics that closely resemble those of P. euphratica, such as Tamarix, artificial forests, and allée trees, posing challenges for the accurate identification of P. euphratica. To solve this issue, this paper presents a method for large-scale P. euphratica distribution mapping. The geographical distribution characteristics of P. euphratica were first utilized to rapidly locate the appropriate region of interest and to further reduce background complexity and interference from other similar objects. Spectral features, indices, phenological features, and backscattering features extracted from all the available Sentinel-2 MSI and Sentinel-1 SAR data from 2021 were regarded as the input for a random forest model used to classify P. euphratica in the GEE platform. The results were then compared with the results from the method using only spectral features and index features, the results from the method that only added phenological features, and the results from the method that added phenological features and backscattering features by visually and quantitatively referencing field-surveyed samples, UAV data, and high-spatial-resolution data from Google Earth Data and Map World. The comparison indicated that the proposed method, which adds both phenological and time-series backscattering features, could correctly distinguish P. euphratica from other types of vegetation that have spectral information similar to P. euphratica. The rates of omission errors (OEs), commission errors (CEs), and overall accuracy (OA) for the proposed method were 12.53%, 11.01%, and 89.32%, respectively, representing increases of approximately 9%, 17%, and 13% in comparison with the method using only spectral and index features. The proposed method significantly improved the accuracy of P. euphratica classification in terms of both omission and, especially, commission. [ABSTRACT FROM AUTHOR]

Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed a P henology domain O ptical- S AR feature in T egration method to A utomatically generate single (SC-Rice) and double cropping R ice (DC-Rice) sample (POSTAR) for efficient and refined rice mapping. POSTAR includes three major steps: (1) generating a potential rice map using a phenology- and object-based classification method with optical data (Sentinel-2 MSI) to select candidate rice samples; (2) employing K-means to identify SC- and DC-Rice candidate samples according to unique SAR-based (Sentinel-1 SAR) phenological features; (3) implementing a two-step refinement strategy to filter high-confidence SC- and DC-Rice samples, maintaining a balance between intraclass phenological variance and sample purity. Test areas selected for validation include the Dongting Lake plain and Poyang Lake plain in South China, as well as Fujin county located in the Sanjiang plain of North China. POSTAR proved effective in producing reliable SC- and DC-Rice samples, achieving a high spectral correlation similarity (>0.85) and low dynamic time wrapping distance (<8.5) with field samples. Applying POSTAR-derived samples to random forest classifier yielded an overall accuracy of 89.6%, with F1 score of 0.899 for SC-Rice and 0.938 for DC-Rice in the Dongting Lake plain. Owing to the incorporation of knowledge-based optical and SAR phenological features, POSTAR exhibited strong spatial transferability, achieving an overall accuracy of 96.0% in the Poyang Lake plain and 97.8% in the Fujin county. These results demonstrated the effectiveness of the POSTAR method in accurately mapping rice cropping patterns without extensive field visits, providing valuable insights for crop monitoring in large, diverse, and cloudy regions through the integration of optical and SAR data. • An automated sample generation method (POSTAR) for rice cropping pattern mapping. • Integrate Sentinel-1/2 to capture unique phenological features of SC- and DC-Rice. • SC- and DC-Rice mapping based on generated samples yielded good performance. • POSTAR exhibited robust generalization across varied regions and time periods. • POSTAR offers new insights for efficient and refined rice mapping in cloudy regions. [ABSTRACT FROM AUTHOR]

The research on the identification of dominant tree species in the coniferous forest of Shangri-La was carried out to provide a reference for subsequent forest resource management and research in the area. Based on the Google Earth Engine (GEE) platform and Sentinel-1/2 time series images of 2020, the temporal characteristics of vegetation were constructed, and a total of 43 features were combined with radar features, spectral features, texture features, and terrain features. Through different combination schemes of features, hierarchical classification and random forest classification algorithms were used to finely identify the dominant tree species of four coniferous forests of Shangri-La: Pinus densata, Pinus yunnanensis, Picea asperata and Larix gmelinii. The results showed that the classification accuracy of multi-source time series data combined with all features was the highest at three levels. The overall accuracy of forest and non-forest types in the study area was 98. 73%, and the Kappa coefficient was 0. 97, harmonic average of user accuracy and mapping accuracy F1 was 98. 71%. The overall accuracy of coniferous and broad-leaved forests was 92. 80%, the Kappa coefficient was 0. 85, F2 was 92. 58%. The overall accuracy of 4 dominant tree species was 89. 51%, the Kappa coefficient was 0. 86, F1 was 89. 36%. Different tree species had separability on different features. The combination of multiple features can improve the accuracy of tree species identification to a certain extent. Based on GEE platform and Sentinel-1/2 multi-source time series data can perform fine identification of forest dominant tree species at a spatial resolution of 10 m. [ABSTRACT FROM AUTHOR]

The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize the level of urbanization and measure the intensity of human activities, and hence, the timely understanding of ISA dynamic changes is of great significance to protect the ecological safety of the YRDHEEZ. Based on the multi-source and multi-modal Sentinel-1/2 remotely sensed data provided by Google Earth Engine (GEE) cloud computing platform, this study developed a novel approach for the extraction of time-series ISA in the YRDHEEZ through a combination of random forest algorithm and numerous representative features extracted from Sentinel-1/2. Subsequently, we revealed the pattern of the ISA spatial-temporal evolution in this region over the past five years. The results demonstrated that the proposed method has good performance with an average overall accuracy of 94.84% and an average kappa coefficient of 0.9393, which verified the feasibility of the proposed method for large-scale ISA mapping with 10 m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km2 in 2018 to 5147.02 km2 in 2022 with an average rate of −16.09 km2/year in the last 5 years, suggesting that the ISA of YRDHEEZ has decreased while its overall pattern was not significantly changed over time. The presented workflow can provide a reference for large-scale ISA mapping and its evolution analysis, especially in regions on estuarine deltas. [ABSTRACT FROM AUTHOR]

Accurate high-resolution maps of oil palm plantations underpin effective management of environmental and socio-economic impacts at global, regional, and national levels. However, young industrial and highly irregular small-holder plantations are mostly unmapped and not included in official FAO statistics. This issue is addressed here by discriminating global oil palm plantation in 2020 into four subclasses: Industrial Mature Oil Palm (IMOP); Industrial Young Oil Palm (IYOP); Smallholder Mature Oil Palm (SMOP); and Smallholder Young Oil Palm (SYOP). Data, resolved to 4.77 m, from Planet & NICFI, Sentinel-1/2, were combined with other layers using the image-oriented classification and regression tree (CART) algorithm which performed best in classification tests. Results show that SMOP dominates distributional extent, but it was also the most accurately mapped subclass typically found at 500–1000 m altitude. IMOP had the most extensive altitude range of 500–1300 m, while IYOP and SYOP were found at similar altitudes of 500–800 m and 500–900 m respectively. Recent developments in South East Asia show oil palm plantations expanding into new areas with a slope of 24 degrees. Results provide data to support Sustainable Development Goal by assisting future oil palm-related development planning and monitoring in the world's major oil palm-growing countries. [ABSTRACT FROM AUTHOR]

Crop rotation increases crop yield, improves soil health, and reduces plant disease. Mapping crop rotation is difficult because crop data from a single time point do not sufficiently represent the dynamics of a system. Studies have tried to map crop rotation by sequentially combining crop maps. However, this produced a large number of meaningless crop sequences, hindering the assessment of rotational benefits at regional scales. Here, we propose a crop rotation classification scheme that integrates temporal information into static crop maps and develop an innovative approach to map crop rotation. We chose a typical multiple cropping region in southern China. Given that the landscape is characterized by high crop diversity (e.g., food crops, cash crops, and permanent crops) and variable cropping intensity, our classification scheme first defines three main rotation systems, i.e., paddy, vegetable, and orchard systems, and then further divides the systems into nine subsystems according to their seasonal dynamics. Finally, we apply time series of Sentinel-1 and Sentinel-2 images to identify the systems by a hierarchical rule-based method. The map of crop rotation systems in 2020 had producer, user, and overall accuracies of 81%, 79%, and 81%, respectively. The results indicate that integrating temporal information into the classification scheme is vital to representing complex rotation systems and that remotely sensed temporal dynamics of crops are useful to characterize these systems. It also shows that crop rotation can be mapped directly rather than aggregating multiple crop layers, thus providing a new perspective for mapping and understanding crop rotation systems.

The propagation of the invasive Spartina alterniflora (S. alterniflora) has seriously affected the health of coastal wetland ecosystems in China and thus requires an urgent response. In this article, we construct a feature vector set containing phenological and other time-series features based on the google earth engine platform by combining dense time-series images from the sentinel-1 and sentinel-2 satellites. We obtained the dataset of the annual distribution of S. alterniflora in the Yellow river delta from 2016 to 2021 by developing an object-oriented random forest classification model. The results show that S. alterniflora has different phenological features from other wetland plants that played an important role in its classification based on the images. A combination of multiple phenological and temporal features improved the classification accuracy of S. alterniflora (multi-year average overall accuracy: 95.38%; user accuracy: 95.01%; producer accuracy: and 95.17%). Our results suggest that from 2016 to 2021, the growth rate of the area occupied by S. alterniflora was 2.17 km2 per year, and a new patch of the S. alterniflora appeared in the south of the study area in 2018. The article here provides scientific data to support the monitoring and control of the invasive S. alterniflora at a large scale.

Ensuring food security in precision agriculture demands early prediction of corn yield in the USA at international, regional, and local levels. Accurate corn yield estimation can play a crucial role in averting famine by offering insights into food availability during the growing season. To address this, we propose a Concatenate-based 2D-CNN-BILSTM model that integrates Sentinel-1, Sentinel-2, and Soil GRIDS (global gridded soil information) data for corn yield estimation in Iowa State from 2018 to 2021. This approach utilizes Sentinel-2 features, including spectral bands (Blue, Green, Red, Red Edge 1/2/3, NIR, n-NIR, and SWIR 1/2), and vegetation indices (NDVI, LSWI, DVI, RVI, WDRVI, SAVI, VARIGREEN, and GNDVI), alongside Sentinel 1 features (VV, VH, difference VV, and VH, and RVI), and soil data (Silt, Clay, Sand, CEC, and pH) as initial inputs. To extract high-level features from this data each month, a dedicated 2D-CNN was designed. This 2D-CNN concatenates high-level features from the previous month with low-level features of the subsequent month, serving as input features for the model. Additionally, to incorporate single-time soil data features, another 2D-CNN was implemented. Finally, high-level features from soil, Sentinel-1, and Sentinel-2 data were concatenated and fed into a BILSTM layer for accurate corn yield prediction. Comparative analysis against random forest (RF), Concatenate-based 2D-CNN, and 2D-CNN models, using metrics like RMSE, MAE, MAPE, and the Index of Agreement, revealed the superiority of our model. It achieved an Index of Agreement of 84.67% with an RMSE of 0.698 t/ha. The Concatenate-based 2D-CNN model also performed well with an RMSE of 0.799 t/ha and an Index of Agreement of 72.71%. The 2D-CNN model followed closely with an RMSE of 0.834 t/ha and an Index of Agreement of 69.90%. In contrast, the RF model lagged with an RMSE of 1.073 t/ha and an Index of Agreement of 69.60%. Integration of Sentinel 1–2 and Soil-GRIDs data with the Concatenate-based 2D-CNN-BILSTM model significantly improved accuracy. Combining soil data with Sentinel 1–2 features reduced the RMSE by 16 kg and increased the Index of Agreement by 2.59%. This study highlighted the potential of advanced machine learning (ML)/deep learning (DL) models in achieving precise and reliable predictions, which could support sustainable agricultural practices and food-security initiatives.

Affected by geographical location and climatic conditions, crop classification in the Yunnan Plateau of China is greatly restricted by the low utilization rate of annual optical data, complex crop planting structure, and broken cultivated land. This paper combines monthly Sentinel-2 optical remote sensing data with Sentinel-1 radar data to minimize cloud interference to conduct crop classification for plateau areas. However, pixel classification will inevitably produce a "different spectrum of the same object, foreign objects in the same spectrum". A principal component feature synthesis method is developed for multi-source remote sensing data (PCA-MR) to improve classification accuracy. In order to compare and analyze the classification effect of PCA-MR combined with multi-source remote sensing data, we constructed 11 classification scenarios using the Google Earth Engine platform and random forest algorithm (RF). The results show that: (1) the classification accuracy is 79.98% by using Sentinel-1 data and 91.18% when using Sentinel-2 data. When integrating Sentinel-1 and Sentinel-2 data, the accuracy is 92.31%. By analyzing the influence of texture features on classification under different feature combinations, it was found that optical texture features affected the recognition accuracy of rice to a lesser extent. (2) The errors will be reduced if the PCA-MR feature is involved in the classification, and the classification accuracy and Kappa coefficient are improved to 93.47% and 0.92, respectively. [ABSTRACT FROM AUTHOR]

Rapid mapping of disasters using any kind of satellite imagery is a challenge. The faster the response, the better the service is for the end users who are managing the emergency activities. Indeed, production rapidity is crucial whatever the satellite data in input. However, the speed of delivery must not be at the expense of crisis information quality. The automated flood and fire extraction pipelines, presented in this technical note, make it possible to take full advantage of advanced algorithms in short timeframes, and leave enough time for an expert operator to validate the results and correct any unmanaged thematic errors. Although automated algorithms aren't flawless, they greatly facilitate and accelerate the detection and mapping of crisis information, especially for floods and fires. ExtractEO is a pipeline developed by SERTIT and dedicated to disaster mapping. It brings together automatic data download and pre-processing, along with highly accurate flood and fire detection chains. Indeed, the thematic quality assessment revealed F1-score values of 0.91 and 0.88 for burnt area and flooded area detection, respectively, from various kinds of high- and very-high- resolution data (optical and SAR). [ABSTRACT FROM AUTHOR]

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Accurate garlic identification and mapping are vital for precise crop management and the optimization of yield models. However, previous understandings of garlic identification were limited. Here, we propose an automatic garlic mapping framework using optical and synthetic aperture radar (SAR) images on the Google Earth Engine. Specifically, we firstly mapped winter crops based on the phenology of winter crops derived from Sentinel-2 data. Then, the garlic was identified separately using Sentinel-1 and Sentinel-2 data based on the winter crops map. Additionally, multi-source validation data were used to evaluate our results. In garlic mapping, coupled optical and SAR images (OA 95.34% and kappa 0.91) outperformed the use of only optical images (OA 74.78% and kappa 0.50). The algorithm explored the potential of multi-source remote sensing data to identify target crops in mixed and fragmented planting regions. The garlic planting information from the resultant map is essential for optimizing the garlic planting structure, regulating garlic price fluctuations, and promoting a healthy and sustainable development of the garlic industry.

• 12 scenarios of using different satellite images for crop recognition were compared. • Combining GF-1, GF-6, Sentinel-1, Sentinel-2 and Landsat-8 identified crops earliest. • Early identification depends on feature properties, crop type, and climate condition. Early-season crop identification by remote sensing is challenging due to insufficient spectral and temporal information at early stage, especially when relying on single satellite data. Although multi-source datasets may offer additional useful information for classification, the specific significance of different satellite data and their combinations in early-season crop type mapping remains largely unclear. This study investigated the potential of integrating publicly available medium-and high-resolution image data (i.e., Landsat-8, Sentinel-1/2 and GF-1/6) on early-season crop type mapping, with Longjiang County in Heilongjiang Province, China serving as the study area. The results showed that among five single data sources, Sentinel-2 provided the earliest identification (F1-score exceeded 0.9) of rice, followed by GF-1 and Sentinel-1. In terms of corn identification, GF-6 demonstrated the earliest identifiable capability, followed by GF-1 and Sentinel-2. Combining multi-source datasets proves to be more effective for early-season crop classification compared to using single-source datasets. Among all 12 scenarios, the integration of GF-1, GF-6, Sentinel-1, Sentinel-2, and Landsat-8 yielded the best performance in early-season crop identification, achieving accurate identification of rice at the transplanting stage (4 months ahead of harvest) and corn at the heading stage (2 months before harvest). Feature separability analysis further revealed that the crucial spectral/temporal features for specific crop types and image availability related to climate conditions were the main factors affecting early-season crop identification. This work can provide valuable insights for selecting various satellite datasets to facilitate early crop identification and enhances our understanding of the possibilities in early-season crop type mapping by leveraging medium- and high-resolution satellite data. [ABSTRACT FROM AUTHOR]

As an indispensable surface parameter, soil moisture is of great significance for analyzing global water cycle, establishing the hydrological model and assessing drought. Due to differences in the capability of establishing vegetation water content (VWC) models based on different vegetation indices, the influence of the different VWC models on retrieval accuracy has not been effectively assessed. Therefore, the suitability of vegetation parameters obtained from different VWC models established by different vegetation indices for accurate soil moisture retrieval requires further investigation. In this study, the different VWC models were established by different vegetation indices derived from Sentinel‐2 data and compared. Based on the Sentinel‐1 Synthetic Aperture Radar and Sentinel‐2 data, combined with microwave scattering models, the impact of different VWC models on the accuracy of soil moisture retrieval was investigated using the Look Up Table algorithm. The results indicate that, an exponential relationship between the measured VWC and different vegetation indices was obtained in the growth cycle of winter wheat, except for quadratic relationship of Modified Soil Adjusted Vegetation Index vegetation index. Compared with other VWC models, higher accuracy of VWC could be retrieved using NDWI1, and the short‐wave infrared band located at 1610 nm achieved higher accuracy than the band located at 2190 nm. With respect to all VWC models, the combination of near‐infrared and short‐wave infrared bands was more suitable for VWC retrieval. When the contribution of soil scattering exceeded that of vegetation scattering, the results of soil moisture retrieval with different VWC models established by vegetation indices exhibit no discernible difference, regardless of the differences that occurred in VWC itself. Key Points: Based on different vegetation indices, different vegetation water content (VWC) models were developed and they evaluated the impact on the retrieval of soil moistureAccuracy evaluation of VWC models and band analysis suitable for VWC retrievalThe retrieval accuracy of soil moisture with different VWC models exhibit no discernible difference under low vegetation conditions [ABSTRACT FROM AUTHOR]

Accurate and early crop-type maps are essential for agricultural policy development and food production assessment at regional and national levels. This study aims to produce a crop-type map with acceptable accuracy and spatial resolution in northern Mongolia by optimizing the combination of Sentinel-1 (S1) and Sentinel-2 (S2) images with the Google Earth Engine (GEE) environment. A total of three satellite data combination scenarios are set, including S1 alone, S2 alone, and the combination of S1 and S2. In order to avoid the impact of data gaps caused by clouds on crop classification, this study reconstructed the time series of S1 and S2 with a 10-day interval using the median composite method, linear moving interpolation, and Savitzky–Golay (SG) filter. Our results indicated that crop-type classification accuracy increased with the increase in data length to all three data combination scenarios. S2 alone has higher accuracy than S1 alone and the combination of S1 and S2. The crop-type map with the highest accuracy was generated using S2 data from 150 days of the year (DOY) (11 May) to 260 DOY (18 September). The OA and kappa were 0.93 and 0.78, respectively, and the F1-score for spring wheat and rapeseed were 0.96 and 0.80, respectively. The classification accuracy of the crop increased rapidly from 210 DOY (end of July) to 260 DOY (August to mid-September), and then it remained stable after 260 DOY. Based on our analysis, we filled the gap of the crop-type map with 10 m spatial resolution in northern Mongolia, revealing the best satellite combination and the best period for crop-type classification, which can benefit the achievement of sustainable development goals 2 (SDGs2). [ABSTRACT FROM AUTHOR]

High-precision spatial mapping of paddy planting areas is very important for food security risk assessment and agricultural monitoring. Previous studies have mainly been based on multi-source satellite imagery, the fusion of Synthetic Aperture Radar (SAR) with optical data, and the combined use of multi-scale and multi-source sensors. However, there have been few studies on paddy spatial mapping using collaborative multi-source remote sensing product information, especially in tropical regions such as Southeast Asia. Therefore, based on the Google Earth Engine (GEE) platform, in this study, Cambodia, which is dominated by agriculture, was taken as the study area, and an extraction scheme for paddy planting areas was developed from collaborative multi-source information, including multi-source remote sensing images (Sentinel-1 and Sentinel-2), multi-source remote sensing land cover products (GFSAD30SEACE, GLC_FCS30-2015, FROM_GLC2015, SERVIR MEKONG, and GUF), paddy phenology information, and topographic features. Evaluation and analysis of the extraction results and the SERVIR MEKONG and ESACCI-LC paddy products revealed that the accuracy of the paddy planting areas extracted using the proposed method is the highest, with an overall accuracy of 89.90%. The results of the proposed method are better than those of the other products in terms of the outline of the paddy planting areas and the description of the road information. The results of this study provide a reference for future high-precision paddy mapping. [ABSTRACT FROM AUTHOR]

A large, deep-seated ancient landslide was partially reactivated on 17 June 2020 close to the Aniangzhai village of Danba County in Sichuan Province of Southwest China. It was initiated by undercutting of the toe of this landslide resulting from increased discharge of the Xiaojinchuan River caused by the failure of a landslide dam, which had been created by the debris flow originating from the Meilong valley. As a result, 12 townships in the downstream area were endangered leading to the evacuation of more than 20000 people. This study investigated the Aniangzhai landslide area by optical and radar satellite remote sensing techniques. A horizontal displacement map produced using cross-correlation of high-resolution optical images from Planet shows a maximum horizontal motion of approximately 15 meters for the slope failure between the two acquisitions. The undercutting effects on the toe of the landslide are clearly revealed by exploiting optical data and field surveys, indicating the direct influence of the overflow from the landslide dam and water release from a nearby hydropower station on the toe erosion. Pre-disaster instability analysis using a stack of SAR data from Sentinel-1 between 2014 and 2020 suggests that the Aniangzhai landslide has long been active before the failure, with the largest annual LOS deformation rate more than 50 mm/yr. The 3-year wet period that followed a relative drought year in 2016 resulted in a 14% higher average velocity in 2018–2020, in comparison to the rate in 2014–2017. A detailed analysis of slope surface kinematics in different parts of the landslide indicates that temporal changes in precipitation are mainly correlated with kinematics of motion at the head part of the failure body, where an accelerated creep is observed since spring 2020 before the large failure. Overall, this study provides an example of how full exploitation of optical and radar satellite remote sensing data can be used for a comprehensive analysis of destabilization and reactivation of an ancient landslide in response to a complex cascading event chain in the transition zone between the Qinghai-Tibetan Plateau and the Sichuan Basin. [ABSTRACT FROM AUTHOR]

Paddy rice agriculture in Southern China, especially Hunan Province, has been suffered from soil contamination. Several policies including rice fallow and decreasing cropping intensity have been implemented for food safety here. It is thus important to monitor rice planting area and cropping intensity to understand the effectiveness of those land-use policies. However, it is challenging to map rice planting areas due to the complex cropping systems (mixed single- and double-cropping), persistent cloud covers, small crop fields, let alone cropping intensity. Here we used all the available Sentinel-2 and all-weather Sentinel-1 imagery to generate a time series data cube to extract paddy rice planting areas and the rice cropping intensity in the Changsha, Zhuzhou, and Xiangtan areas, which is a traditional rice-growing region with small farms in China. Specifically, we investigated the performances of different features (i.e., spectral, seasonal, polarization backscatter) by comparing five scenarios with different combinations of sensors and features, and identified the most suitable features for certain rice types (early, middle, and late rice). The random forest classifier was used for the classification in the Google Earth Engine (GEE) platform, and a reference map in 2017 based on visual interpretation on the GaoFen-2 images were used for collecting the training and validation samples. The results showed the combined data from Sentinel-1/2 generally outperformed classifications using only a single sensor (Sentinel-1/2), but the contribution of different sensors to certain rice types varied. The early, middle and late rice with the highest accuracies within the five scenarios had the overall accuracies of 85%, 95%, and 95%, respectively (F1 = 0.55, 0.85, and 0.85). The compositing of different types of rice allowed us to generate the rice cropping intensity map with an overall accuracy of 81%, which to our limited knowledge is the first effort to map cropping intensity at 10-m resolution in such a fragmented subtropical region. The result showed the single cropping dominated the rice cropping system in the study area 88%, which used to be a typical area with double cropping of rice. Our study demonstrates the potential of mapping rice cropping intensity in a cloudy and highly fragmented region in South China using all the available Sentinel-1/2 data, which would advance our understanding of regional rice production and mitigation of soil contamination.

Building height is an important indicator for assessing the level of urban development along the vertical dimension. Existing large-scale building height estimation studies focus on coarse spatial resolution (e.g., 10, 500, and 1000 m), which cannot reveal height variations across buildings in urban areas. High-resolution images (e.g., < 5 m resolution) can support building-scale height estimation, but they usually have small spatial coverage and are not openly accessible. In this context, we proposed a deep learning-based super-resolution method to generate building height maps at a spatial resolution of 2.5 m using Sentinel-1/2 images. The proposed method consisted of two parts: 1) a super-resolution module (SR) for learning high-resolution features; and 2) a height stratification estimation module (HS) for guiding the network to learn different height levels to mitigate the imbalanced distribution of height values. We created an open building height dataset with 45,000 samples covering multiple urban areas in the Northern Hemisphere, including China, the conterminous United States (CONUS), and Europe. Experimental results showed that for height estimation at the pixel level, the proposed method obtained a root mean square error of 10.318 m in China, 5.654 m in CONUS, and 4.113 m in Europe, respectively. Predicted results provided rich spatial details, due to the inclusion of the super-resolution module, which was heavily missed by existing large-scale studies. Moreover, we calculated the mean and standard deviation of building height in 301 urban centers, each having at least a population of 500,000, and found that the buildings in China were the highest (11.353 m ± 9.543 m), followed by CONUS (8.487 m ± 6.202 m) and Europe (8.136 m ± 5.020 m). Ablation studies indicated that the joint use of Sentinel-1/2 images and the proposed modules (SR and HS) can effectively improve the performance of building height estimation. The building dataset we generated provides great potential in high-resolution database updating, urban planning, and natural disaster assessment, and indeed, a new perspective of how we can utilize cutting-edge satellite imaging technology in urban observation, measurement, monitoring, and management. The dataset and code of this study will be available at: https://github.com/lauraset/Super-resolution-building-height-estimation. • Deep learning-based super-resolution method for building height estimation at 2.5 m. • Open building height dataset across urban areas of China, CONUS, and Europe. • Building height of 301 urban centers with population over 500,000 in N. Hemisphere. • Predicted results exhibited building-scale height variations within the urban areas. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) and biomass are important indicators that reflect the growth status of maize. The optical vegetation indices and the synthetic-aperture radar (SAR) backscattering coefficient are commonly used to estimate the LAI and biomass. However, previous studies have suggested that spectral features extracted from a single pixel have a poor ability to describe the canopy structure. In this paper, we propose a method for estimating LAI and biomass by combining spectral and texture features. Specifically, LAI, biomass and remote-sensing data were collected from the jointing, trumpet, flowering, and filling stages of maize. Then we formed six remote-sensing feature matrices using the spectral and texture features extracted from the remote sensing data. Principal component analysis (PCA) was used to remove noise and to reduce and integrate the multi-dimensional features. Multiple linear regression (MLR) and support vector regression (SVR) methods were used to build the estimation models. Tenfold cross-validation was adopted to verify the effectiveness of the proposed method. The experimental results show that using the texture features of both optical and SAR data improves the estimation accuracy of LAI and biomass. In particular, SAR texture features greatly improve the estimation accuracy of biomass. The estimation model constructed by combining spectral and texture features of optical and SAR data achieves the best performance (highest coefficient of determination ($R^{2}$ ) and lowest root mean square error (RMSE)). Specifically, we conclude that the best window sizes for extracting texture features from optical and SAR data are $3\times 3$ and $7 \times 7$ , respectively. SVR is more suitable for estimating the LAI and biomass of maize than MLR. In addition, after adding texture features, we observed a significant improvement in the accuracy of estimation of LAI and biomass for the growth stages, which have a larger variation in the extent of the canopy. Overall, this work shows the potential of combining spectral and texture features for improving the estimation accuracy of LAI and biomass in maize.

Urban land-use maps outlining the distribution, pattern, and composition of various land use types are critically important for urban planning, environmental management, disaster control, health protection, and biodiversity conservation. Recent advances in remote sensing and social sensing data and methods have shown great potentials in mapping urban land use categories, but they are still constrained by mixed land uses, limited predictors, non-localized models, and often relatively low accuracies. To inform these issues, we proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geospatial "big data". With street blocks generated from OpenStreetMap (OSM) data as the minimum classification unit, we integrated an expansive set of multi-scale spatially explicit information on land surface, vertical height, socio-economic attributes, social media, demography, and topography. We further proposed to apply the automatic ensemble learning that leverages a bunch of machine learning algorithms in deriving optimal urban land use classification maps. Results of block-level urban land use classification in five metropolitan areas of the United States found the overall accuracies of major-class (Level-I) and minor-class (Level-II) classification could be high as 91% and 86%, respectively. A multi-model comparison revealed that for urban land use classification with high-dimensional features, the multi-layer stacking ensemble models achieved better performance than base models such as random forest, extremely randomized trees, LightGBM, CatBoost, and neural networks. We found without very-high-resolution National Agriculture Imagery Program imagery, the classification results derived from Sentinel-1, Sentinel-2, and other open big data based features could achieve plausible overall accuracies of Level-I and Level-II classification at 88% and 81%, respectively. We also found that model transferability depended highly on the heterogeneity in characteristics of different regions. The methods and findings in this study systematically elucidate the role of data sources, classification methods, and feature transferability in block-level land use classifications, which have important implications for mapping multi-scale essential urban land use categories. [ABSTRACT FROM AUTHOR]

Opportunistic macroalgal blooms have been used for the assessment of the ecological status of coastal and estuarine areas in Europe. The use of earth observation (EO) data sets to map green algal cover based on a Normalized Difference Vegetation Index (NDVI) was explored. Scenes from Sentinel-2A/B, Landsat-5, and Landsat-8 missions were processed for eight different Irish estuaries of moderate, poor, and bad ecological status using European Union Water Framework Directive (WFD) classification for transitional water bodies. Images acquired during low-tide conditions from 2010 to 2018 within 18 days of field surveys were considered. The estimates of percentage coverage obtained from different EO data sources and field surveys were significantly correlated (R2 = 0.94) with Cohen’s kappa coefficient of 0.69 ± 0.13. The results showed that the NDVI technique could be successfully applied to map the coverage of the blooms and to monitor estuarine areas in conjunction with other monitoring activities that involve field sampling and surveys. The combination of wide-spread cloud-coverage and high-tide conditions provided additional constraints during the image selection. The findings showed that both Sentinel-2 and Landsat scenes could be utilized to estimate bloom coverage. Moreover, Landsat, because of its legacy program, can be utilized to reconstruct the blooms using historical archival data. Considering the importance of biomass for understanding the severity of algal accumulations, an artificial neural networks (ANN) model was trained using the in situ historical biomass samples and the combination of radar backscatter (Sentinel-1) and optical reflectance in the visible and near-infrared (NIR) regions (Sentinel-2) to predict the biomass quantity. The ANN model based on multispectral imagery was suitable to estimate biomass quantity (R2 = 0.74). The model performance could be improved with the addition of more training samples. The developed methodology can be applied in other areas experiencing macroalgal blooms in a simple, cost-effective, and efficient way. The study has demonstrated that both the NDVI-based technique to map spatial coverage of macroalgal blooms and the ANN-based model to compute biomass have the potential to become an effective complementary tool for monitoring macroalgal blooms where the existing monitoring efforts can leverage the benefits of EO data sets.

Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas.

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Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis-support vector machine (PCA-SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer (MODIS) snow cover products and the Sentinel-1 synthetic aperture radar (SAR) scattering characteristics. First, derived from the Sentinel-1A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis (PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation (FB1=93.86, FB2=59.78). The evaluation of the threat score (TS), probability of detection (POD), and false alarm ratio (FAR) for the snow-covered pixels obtained from the two-stage SAR images were different (TS1=86.84, POD1=90.10, FAR1=4.01; TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period. [ABSTRACT FROM AUTHOR]