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ABSTRACTIn the era of Earth Observation (EO) big data, interactive spatiotemporal aggregation analysis is a critical tool for exploring geographic patterns. However, existing methods are inefficient and complex. Their interactive performance greatly depends on large-scale computing resources, especially data cube infrastructure. In this study, from a green computing perspective, we propose a lightweight data cube model based on the preaggregation concept, in which the frequency histogram of EO data is employed as a specific measure. The cube space was divided into lattice pyramids by the Google S2 grid system, and histogram statistics of the EO data were injected into in-memory cuboids. Therefore, exploratory aggregation analysis of EO datasets could be rapidly converted into multidimensional-view query processes. We implemented the prototype system on a local PC and conducted a case study of global vegetation index aggregation. The experiments showed that the proposed model is smaller, faster and consumes less energy than ArcGIS Pro and XCube, and facilitates green computing strategies involving a cube infrastructure. Due to the standalone mode, larger dataset will result in longer cube building time with indexing latency. The efficiency of the approach comes at the expense of accuracy, and the inherent uncertainties were examined in this paper.

Black spot, one of the major diseases of kiwifruit, is caused by Alternaria alternata. A comprehensive investigation into its pathogenicity mechanism is imperative in order to propose a targeted and effective control strategy. The effect of L-arginine on the pathogenicity of A. alternata and the underlying mechanisms were investigated. The results showed that treatment with 5 mM L−1 of L-arginine promoted spore germination and increased the colony diameter and lesion diameter of A. alternata in vivo and in vitro, which were 23.1% and 9.3% higher than that of the control, respectively. Exogenous L-arginine treatment also induced endogenous L-arginine and nitric oxide (NO) accumulation by activating nitric oxide synthase (NOS), arginine decarboxylase (ADC) and ornithine decarboxylase (ODC). In addition, exogenous L-arginine triggered an increase in reactive oxygen species (ROS) levels by activating the activity and inducing gene expression upregulation of NADPH oxidase. The hydrogen peroxide (H2O2) and superoxide anion (O2.−) levels were 15.9% and 2.2 times higher, respectively, than in the control group on the second day of L-arginine treatment. Meanwhile, antioxidant enzyme activities and gene expression levels were enhanced, including superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), glutathione peroxidase (GPX), and glutathione reductase (GR). In addition, exogenous L-arginine stimulated cell wall-degrading enzymes in vivo and in vitro by activating gene expression. These results suggested that exogenous L-arginine promoted the pathogenicity of A. alternata by inducing the accumulation of polyamines, NO, and ROS, and by activating systems of antioxidants and cell wall-degrading enzymes. The present study not only revealed the mechanism by which low concentrations of L-arginine increase the pathogenicity of A. alternata, but also provided a theoretical basis for the exclusive and precise targeting of A. alternata in kiwifruit.

Black spot caused by Alternaria alternata is one of the most common postharvest diseases in fruit and vegetables. A comprehensive investigation into its pathogenicity mechanism is imperative in order to propose a targeted and effective control strategy. The effect of nitric oxide (NO) on the pathogenicity of A. alternata and its underlying mechanism was studied. The results showed that treatment with 0.5 mM L−1 of sodium nitroprusside (SNP) (NO donor) increased the lesion diameter of A. alternata in vivo and in vitro, which was 22.8% and 13.2% higher than that of the control, respectively. Exogenous NO treatment also induced endogenous NO accumulation by activating nitric oxide synthase (NOS). In addition, NO triggered an increase in reactive oxygen species (ROS) levels. NO enhanced activities and gene expression levels of NADPH oxidase (NOX), superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), glutathione peroxidase (GPX), and glutathione reductase (GR). Moreover, NO stimulated cell wall degrading enzymes by activating the corresponding gene expression in vivo and in vitro. These results suggested that exogenous NO promoted the pathogenicity of A. alternata by inducing ROS accumulation and activating antioxidants and cell wall degrading enzymes. The present results could establish a theoretical foundation for the targeted control of the black spot disease in pear fruit.

Most studies on building height estimation using remote sensing imagery mainly focus on urban buildings with relatively regular shape and relatively flat terrain, and pay little attention to large buildings with complex terrain like dams. A new dam height measurement method was proposed in this paper, which used shadow measurement data and metadata from multiple image sources. The method not only considers the geometric relationship between the sun, the satellite and the dam, but also considers the influence of the satellite zenith and introduces the correction factor, which brings the higher precision. Two dams with a maximum height of more than 200 m were studied, and the dam height is estimated by considering the topography around the dam and the shape of the dam. The experimental results show that the Mean Relative Error (MRE) of the estimated dam height using our proposed method are 3.1% and 4.7% for our two study areas, while the MRE of the traditional models is more than 13%. By doing so, we are able to calculate dam height and obtain information on temporal changes in dam height during the construction process, even in situations without a Digital Surface Model (DSM). Therefore, the proposed method will be propitious to the dynamic supervision of the construction process of the dams effectively.

As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizing satellite remote sensing for lock extraction can significantly reduce manual workload and costs, assist in the daily dynamic maintenance of lock hubs, and provide more comprehensive data support for the construction and management of water transport infrastructure. In this context, this paper proposes a new method for ship lock object extraction. Leveraging fuzzy theory and prior knowledge of locks, the extraction of lock objects is achieved from Gaofen-1 (GF-1) high-resolution remote sensing images. The experimental results demonstrate that the proposed algorithm can effectively extract small lock objects in remote sensing images, achieving an average extraction accuracy of 80.9% in the study area.

Currently, there is an absence of an exhaustive elucidation regarding the long-term spatiotemporal characteristics of meteorological droughts in China. Previous studies have predominantly fixated on the lower-dimensional (temporal or spatial) facets of drought events, overlooking critical spatiotemporal details. This paper proposes an integrated processing framework that employs a three-dimensional clustering algorithm to discern drought events and quantifies the spatiotemporal patterns and dynamic attributes of China’s meteorological droughts during 1950–2021 from a three-dimensional perspective encompassing longitude, latitude, and time, capturing comprehensive spatiotemporal information. Additionally, the cross wavelet transform is harnessed to elucidate the impact of teleconnection factors on meteorological drought. The findings underscore that prolonged and extensive meteorological drought events, characterized by heightened severity, primarily manifest during the 1950 s, 1960 s, and 2010 s in temporal, predominantly within the central and western China spatially. From 1950 onwards, the severity, duration, and affected area of meteorological droughts in China exhibit a discernible pattern of initial decline followed by escalation. Into the 2010 s, meteorological droughts became more severe and active. China's meteorological drought displays two relatively prevailing spatial migration patterns: an overarching eastward and westward migration. In the former, the most frequent migration occurs to the east and northeast during its monthly evolution, while the latter involves migration primarily towards the west, southwest, and northwest. The impact of teleconnection factors on meteorological drought shows a spatiotemporal heterogeneity, with ENSO exhibiting a relatively substantial and intricate influencer. This research unearths the latest long-term spatiotemporal dynamics of meteorological drought in China, which contributes to an improved comprehension of drought patterns and behaviors.

Urban land use/land cover (LULC) classification has long been a hotspot for remote sensing applications. With high spatio-temporal resolution and multispectral, the recently launched GF-6 satellite provides ideal open imagery for LULC mapping. In this study, we utilized multitemporal GF-6 images to generate six types of land features, including spectral bands, texture features, built-up, waterbody, vegetation, and red-edge indices. The minimum Redundancy Maximum Relevance (mRMR) algorithm was employed to optimize feature selection. Subsequently, Random Forest (RF) and Extreme Gradient Boosting (XGBT) were assessed using different feature selections. Besides, various feature configurations were designed for LULC classification and comparison. The results indicate that the mRMR-based RF method achieved the highest overall accuracy of 91.37%. The temporal red-edge indices were important features for urban LULC classification and contributed mainly to grassland and cropland. These results supplement existing classification methods and assist in improving LULC mapping in urban areas with complex landscapes.

Large-scale and dynamic surface water mapping is crucial for understanding the impact of global climate change and human activities on the distribution of surface water resources. Remote sensing imagery has become the primary data source for surface water mapping due to its high spatiotemporal resolution and wide coverage. However, the reliability of current water products during flood seasons is limited due to the influence of clouds on optical remote sensing images. Moreover, annual and seasonal surface water mapping cannot capture intra-month variations of water bodies. To address these challenges, we proposed a high spatiotemporal surface water mapping framework on Google Earth Engine that combines multi-source remote sensing data. Our framework can generate 10 m spatial resolution surface water maps at a 15-day time step. We classified water bodies using Sentinel-2 images and a classification tree algorithm, and then used Sentinel-1 data to compensate for cloudy and missing data areas in Sentinel-2 images, resulting in seamless cloud-unaffected surface water maps. We evaluated the effectiveness of our proposed framework in six floodplains around the world, and experimental results demonstrate that the water maps generated by our framework outperform existing public datasets and our framework has great potential for hydrological applications. Our proposed framework can capture the details of surface water dynamics with higher spatial and temporal resolution and is free from cloud influence, which is necessary for water resources management, flood monitoring, and disaster response.

Study region: Ten major river basins in China. Study focus: The spatial and temporal distribution of terrestrial water storage (TWS) in China is significantly affected by global climate change and increased human activity. In this study, we analyzed the spatial and temporal variability of TWS in ten Chinese basins based on data of the Gravity Recovery and Climate Experiment (GRACE). To discuss the driving force of TWS, we assessed the relative contributions and time lags of precipitation (P), evapotranspiration (ET), and runoff (R) to TWS using Spearman correlation analysis and partial least squares regression (PLS). New hydrological insights for the region: Results indicate that: (1) TWS generally shows a reduction trend at a rate of 1.58 ± 0.067 mm/a from 2003 to 2020, with the most significant decreasing trend in September (−2.77 ± 0.070 mm/a). (2) The relative contributions of P, ET, and R to the TWS in China are 29.89 ± 6.98%, 31.03 ± 6.92% and 39.09 ± 10.82%, respectively, and are more prominent in low-latitude regions than in high-latitude regions. (3) The best-correlated lag times were 1–2 months between the P and TWS responses. (4) Melting snow, ice and groundwater extraction may have weakened the response of the northern TWS to the climate. The results provide insight into the spatial and temporal variation in water storage, which can provide a basis for the scientific and effective management of water resources.

Kiwifruit fruit stored at low temperatures are susceptible to chilling injury, leading to rapid softening, which therefore affects storage and marketing. The effect of 150 nM mL−1 of exogenous phytosulfokine α (PSKα) on reactive oxygen species (ROS) metabolism, Ca2+ signaling, and signal-transducing MAPK in kiwifruit, stored at 0 °C for 60 days, was investigated. The results demonstrated that PSKα treatment effectively alleviated chilling injury in kiwifruit, with a 15% reduction in damage compared to the control on day 60. In addition, PSKα enhanced the activities and gene expression levels of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), glutathione reductase (GR), Ca2+−ATPase, and mitogen−activated protein kinase (MAPK). In contrast, the activities and gene expression levels of NADPH oxidase (NOX) were inhibited, leading to a lower accumulation of O2− and H2O2, which were 47.2% and 42.2% lower than those in the control at the end of storage, respectively. Furthermore, PSKα treatment enhanced the calmodulin (CaM) content of kiwifruit, which was 1.41 times that of the control on day 50. These results indicate that PSKα can mitigate chilling injury and softening of kiwifruit by inhibiting the accumulation of ROS, increasing antioxidant capacity by inducing antioxidant enzymes, activating Ca2+ signaling, and responding to MAPK protein kinase. The present results provide evidence that exogenous PSKα may be taken for a hopeful treatment in alleviating chilling injury and maintaining the quality of kiwifruit.

The evaluation of satellite soil moisture is a big challenge owing to the large spatial mismatch between pixel-based satellite soil moisture products and point-based in situ measurements. Upscaling in situ measurements to obtain the “true value” of soil moisture content at the satellite grid/footprint scale can make up for the scale difference and improve the validation. Many existing upscaling methods have strict requirements regarding the spatial distribution and quantity of soil moisture sensors. However, in reality, soil-moisture-monitoring networks are commonly sparse with low sensor density, which increases the difficulty of obtaining accurate upscaled soil moisture data and limits the validation of satellite products. For this reason, this paper proposes a scheme to upscale in situ measurements using five machine learning methods along with Landsat 8 datasets and DEM data to validate the accuracy of a SMAP-enhanced passive soil moisture product for a sparse network on the Qinghai–Tibet Plateau. The proposed scheme realizes the upscaling of in situ soil moisture data to the pixel scale (30 m × 30 m) and then to the coarse grid scale (9 km × 9 km) by using multi-source remote sensing data as the bridge of scale conversion. The long-time SMAP SM products since April 2015 on the Qinghai–Tibet Plateau were validated based on upscaled soil moisture data. The results show that (1) random forest regression performs the best, and the upscaled soil moisture data reflect the region-average soil moisture conditions that can be used for evaluating SMAP data; (2) the SMAP product meets its scientific measurement requirements; and (3) the SMAP product generally underestimates the soil moisture in the study area.

NDVI (Normalized difference vegetation index) is a critical variable for monitoring climate change, studying ecological balance, and exploring the pattern of regional phenology. Traditional neural network models only consider image features in time series prediction, while historical data and its changes play an important role in time series forecasting. For this study, we proposed convolutional neural networks (CNN) combined feature engineering forecasting model (SF-CNN), which integrated both the advantages of image characteristics learned from CNN and statistic characteristics calculated by historical data related to the forecast period to improve the accuracy of NDVI predictions in the next 3 months with 30-day interval at multiple complex areas. To intuitively show the performance of SF-CNN, it was compared with CNN using the same parameters. Results mainly showed that (1) in terms of visual analysis, the texture, pattern, and structure of predicted NDVI using SF-CNN are similar to the observed NDVI, and SF-CNN exhibits strong generalization ability; (2) in terms of quantitative assessment, SF-CNN generally outperforms CNN, and it can improve the reliability and robustness for predicting NDVI through simple statistical characteristics while reducing the uncertainties; (3) SF-CNN can learn seasonal and sudden changes in four different and complex study areas with considerable accuracy and without extra data.

Soil moisture (SM) is an indispensable variable in drought monitoring and weather forecast. L-band is found to be the most suitable band for retrieving surface SM. Here, we evaluate two L-band passive microwave SM products Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) in Inner Mongolia. The collected in-situ data from 24 measured sites and the triple collocation (TC) analysis method (using Global Land Data Assimilation System (GLDAS) Noah and the Advanced Scatterometer (ASCAT) SM data as a reference dataset) are used to evaluate SMAP and SMOS SM products. In the validation based on in-situ SM, SMAP shows the highest correlation with the in-situ SM, followed by GLDAS and SMOS. The root mean square error (RMSE) and bias value of SMAP is the lowest. The validation results of each in-situ station show that the median correlation coefficient of ASCAT is the highest, followed by SMAP, GLDAS, and SMOS. The median RMSE and bias of SMAP are the lowest, while SMOS performs the worst among the three products. TC analysis results show that SMAP has better performance with the majority of the random errors distributed between 0.02 and 0.03 m3/m3. The average random error of SMAP is small, while SMOS has relatively large random errors. Simultaneously, SMAP performs better than GLDAS and SMOS in grassland and low vegetation coverage areas. The research results and analysis of this paper are expected to provide some insights for improving the quality of SM products.

Study region: Han River mainstream, China. Study focus: Human activities and climate change are synergistically impacting on the spatiotemporal features of Han River surface water, which affects the ecological environment evolution and economic development of the basin. However, the long-term changes of the area and spatial characteristics of Han River surface water have not been well quantified for lack of sufficient data and data processing powers. The study produced the annual surface water products of Han River mainstream from 1986 to 2020 based on Google Earth Engine and Landsat images, and then analyzed the long-term spatiotemporal dynamics of surface water and the influence of climatic factor and water conservancy projects construction. New hydrological insights for the region: During the past 35 years, the surface water area of Han River mainstream has shown a fluctuating upward trend overall, and the dynamic changes have obvious regional and phased differences. Precipitation is the main climatic factor affecting the surface water area changes. With precipitation changes and the implementation of water conservancy projects in Han River, the surface water area has increased significantly and many reaches have gradually widened in upstream, while the surface water area has slowly decreased and many reaches have gradually narrowed in mid-downstream. The latest information about the spatiotemporal dynamics of Han River surface water provided by the research can be used for the optimal allocation and management of the river’s water resources.

In the era of rapid data expansion and computer technology development, discrete storage, multiband push and fuzzy query remote sensing data management methods are no longer suitable for the data analysis needs of users, including the needs for long time series, global regions and multidata fusion. After analyzing traditional data management techniques, this paper discusses the existing achievements and development trends of current technologies. This paper aims to solve the problem of data sharing difficulties and organizational inconsistency caused by the use of different formats for the same spatial object. Based on a discrete global grid, this paper studies the blocky division method and coding specification of Google S2 and then accomplishes the layered storage of remote sensing data in HBase. Finally, Kylin is used to build a cube model to discuss the information mining analysis changes in the new data management model. Experiments show that the blocky and layered management schema (BLMS) can realize the unified management of global remote sensing data with multisource, heterogeneous, multiscale, and long-term characteristics and provide accurate data services on demand.

When using HBase to store tiles of remote sensing images, the spatial position of a tile is often used as the first part of the tile's rowkey so that tiles with high spatial correlations are stored close together to improve query efficiency. We refer to this storage method as the Geo-First model. However, Geo-First models have two problems: the load between nodes is unbalanced, and the accumulation of time-series remote sensing images has a negative impact on storage and query efficiency. Considering these two problems, we proposed a method for storing remote sensing images based on Google S2 and HBase. In our method, two strategies are adopted to eliminate these problems: the balanced placement strategy (BPS) and the periodic storage strategy (PSS). We evaluated our method by focusing on the effectiveness of BPS and PSS. The results show that our method achieves higher tile storage and query efficiency than three Geo-First models based on latitude and longitude, Geohash code, and Google S2 code. BPS effectively balances the load between nodes, while PSS alleviates the negative impact of the accumulation of time-series remote sensing images. Both BPS and PSS greatly improve tile storage and query efficiency.

The corrupted information in training samples is an important factor affecting the accuracy and generalizability of the machine learning models. Due to the extremely high memory capacity of deep learning models, the interference of excessive corrupted information makes the model prone to bad generalization behavior. This paper proposes a method of estimating training sample quality using the value calculated by the loss function in the process of gradient descent optimization. The method includes a model accuracy variation degree algorithm and a sample quality analysis algorithm. The model accuracy variation degree algorithm provides a basis for determining the intervention time of the sample quality analysis algorithm by calculating the intensity of the model accuracy variation change. The data error evaluation algorithm analyzes the distribution characteristics of the training error and estimates the error degree of the training samples to control the quality of the input samples. This study includes a water segmentation experiment performed on GF1 remote sensing images, which demonstrates that the optimization method can significantly improve the model accuracy and training stability.

Lakes and reservoirs are important parts of ecosystems and social developments. Separating scientific research on lakes and reservoirs is not conducive to measure water regulation services or defend against natural disasters. Accurate surface water information is of great significance to lake and reservoir management. To address this problem, we proposed a new automated lake and reservoir extraction process (LREP), which applied the modified two-mode (MTM) method to extract water from partial images by calculating thresholds in the normalized difference water index (NDWI) and the near-infrared (NIR) band; then, we estimated the segmentation thresholds from a set of NDWI thresholds obtained for the remaining images. Subsequently, a series of feature factors was used to remove rivers, shadows, and ponds. Finally, a new shape parameter called complexity was introduced to describe the edge characteristics of water bodies, distinguishing lakes and reservoirs from paddy fields and remaining ponds. We verified the algorithm using 154 Gaofen-2 images covering three cities: Liaoyuan, Xianning, and Zigong. The results indicated that the new LREP performed well with the overall accuracies of 99.38%, 99.27%, and 98.72% and the kappa coefficients of 0.92, 0.96, and 0.92 in Liaoyuan, Xianning, and Zigong, respectively. This method has the potential to monitor a variety of lakes and reservoirs, including lost lakes, new reservoirs, and surface water quality.

In the task of using deep learning semantic segmentation model to extract water from high-resolution remote sensing images, multiscale feature sensing and extraction have become critical factors that affect the accuracy of image classification tasks. A single-scale training mode will cause one-sided extraction results, which can lead to “reverse” errors and imprecise detail expression. Therefore, fusing multiscale features for pixel-level classification is the key to achieving accurate image segmentation. Based on this concept, this paper proposes a deep learning scheme to achieve fine extraction of image water bodies. The process includes multiscale feature perception splitting of images, a restructured deep learning network model, multiscale joint prediction, and postprocessing optimization performed by a fully connected conditional random field (CRF). According to the scale space concept of remote sensing, we apply hierarchical multiscale splitting processing to images. Then, we improve the structure of the image semantic segmentation model DeepLabV3+, an advanced image semantic segmentation model, and adjust the feature output layer of the model to multiscale features after weighted fusion. At the back end of the deep learning model, the water boundary details are optimized with the fully connected CRF. The proposed multiscale training method is well adapted to feature extraction for the different scale images in the model. In the multiscale output fusion, assigning different weights to the output features of each scale controls the influence of the various scale features on the water extraction results. We carried out a large number of water extraction experiments on GF1 remote sensing images. The results show that the method significantly improves the accuracy of water extraction and demonstrates the effectiveness of the method.

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived SIF for drought monitoring has not been widely investigated due to a lack of data. The lack of high spatiotemporal resolution satellite SIF hinders the resolution enhancement of SIF derived by downscaling or reconstruction algorithms. The TROPOspheric Monitoring Instrument (TROPOMI) SIF provides an alternative with finer spatiotemporal resolution. We present an operational downscaling method to generate 500 m 16-day SIF (TSIF) using Neural Networks over a local spatiotemporal window. The results showed that our method is very robust against overfitting, and TSIF has a strong spatiotemporal consistency with TROPOMI SIF (TROPOSIF) with R2=0.956 and RMSE=0.054 mWm−2sr−1nm−1. Comparison with another SIF product (CASIF) showed a spatiotemporal consistency with TSIF. Comparison with tower gross primary productivity (GPP) from AmeriFlux in California showed a strong correlation with R2 for multiple ecosystems ranging from 0.58 to 0.88. We explored the capacity of TSIF for monitoring a drought event in Henan, China, showing that TSIF is more sensitive to drought and precipitation compared to the Enhanced Vegetation Index. Our TSIF is a very promising indicator for regional drought monitoring.

Lake bottom topography is a basic parameter that reflects the elevation of all lake bottom geographical locations. In this study, a novel method was proposed for mapping lake bottom topography by combining the water occurrence map from the Global Surface Water (GSW) dataset with long-term measured water levels. This method took advantage of the following feature: the rapid change in water level of a lake’s dynamic inundation area leads to a different water occurrence frequency and, therefore, put forward the concept of lake water level frequency, which refers to the frequency at which the water level is higher than or equal to a specified elevation. As water occurs more frequently in lake bottoms with lower elevations and less frequently in lake bottoms with higher elevations, we assume that lake water level frequency is identical to the water occurrence frequency over a long time. The water level frequency curve of all the measured water level data was generated through the P-III distribution function, and the elevation values from the water level frequency curve were assigned to pixels with the same frequency in the water occurrence map in order to generate the lake bottom topographic map. A case study was conducted on Poyang Lake in China to demonstrate the performance of the method. The derived bottom topographic map of Poyang Lake was verified by four measured sections. The results showed that the proposed method was feasible and could well reflect the bottom topography of Poyang Lake. The absolute error was mostly less than 0.5 m, the mean relative error was 7.4%, and the root mean square error was 0.99 m. The proposed method enriches the mapping means of lake bottom topography and has the potential to become a useful tool with a broad application prospect.

In this paper, we propose a significance test-based change detection method that can automatically discriminate between changed and unchanged pixels in the difference image. The method adaptively considers the local contextual information, which is contained in the neighborhoods of each pixel, to derive the decision threshold. In our method, a significance test algorithm based on maximuming a posteriori estimate is constructed; then, a weight to each pixel in the block is imposed to increase the change detection accuracy. In our proposed method, the distribution of the difference image satisfying Laplace model also leads to good precision. For the experimental component, two types of images were tested. And experimental results proved the effectiveness of the significance test method when compared with four state-of-the-art change detection methods.

Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.

Deep learning technology has achieved great success in the field of remote sensing processing. However, the lack of tools for making deep learning samples with remote sensing images is a problem, so researchers have to rely on a small amount of existing public data sets that may influence the learning effect. Therefore, we developed an add-in (LabelRS) based on ArcGIS to help researchers make their own deep learning samples in a simple way. In this work, we proposed a feature merging strategy that enables LabelRS to automatically adapt to both sparsely distributed and densely distributed scenarios. LabelRS solves the problem of size diversity of the targets in remote sensing images through sliding windows. We have designed and built in multiple band stretching, image resampling, and gray level transformation algorithms for LabelRS to deal with the high spectral remote sensing images. In addition, the attached geographic information helps to achieve seamless conversion between natural samples, and geographic samples. To evaluate the reliability of LabelRS, we used its three sub-tools to make semantic segmentation, object detection and image classification samples, respectively. The experimental results show that LabelRS can produce deep learning samples with remote sensing images automatically and efficiently.

Urban flooding is one of the most costly and destructive natural hazards worldwide. Remote-sensing images with high temporal resolutions have been extensively applied to timely inundation monitoring, assessing and mapping, but are limited by their low spatial resolution. Sub-pixel mapping has drawn great attention among researchers worldwide and has demonstrated a promising potential of high-accuracy mapping of inundation. Aimed to boost sub-pixel urban inundation mapping (SUIM) from remote-sensing imagery, a new algorithm based on spatial attraction models and Elman neural networks (SAMENN) was developed and examined in this paper. The Elman neural networks (ENN)-based SUIM module was developed firstly. Then a normalized edge intensity index of mixed pixels was generated. Finally the algorithm of SAMENN-SUIM was constructed and implemented. Landsat 8 images of two cities of China, which experienced heavy floods, were used in the experiments. Compared to three traditional SUIM methods, SAMENN-SUIM attained higher mapping accuracy according not only to visual evaluations but also quantitative assessments. The effects of normalized edge intensity index threshold and neuron number of the hidden layer on accuracy of the SAMENN-SUIM algorithm were analyzed and discussed. The newly developed algorithm in this study made a positive contribution to advancing urban inundation mapping from remote-sensing images with medium-low spatial resolutions, and hence can favor urban flood monitoring and risk assessment.

For oversized cargo transportation, traditional transportation schemes only consider road length, road width, the transportation cost as weight values in analysis and calculation of route selection. However, for oversized trucks, turning direction at road intersections is also a factor worth considering. By introducing the classical algorithm of Dijkstra into the model of road network, this research considers the size of turning angle at intersections as the weight value of the edge in the auxiliary network based on the weight values of road corners, upon which the shortest path analysis is performed. Then, an optimal path with minimum time cost was eventually obtained. The proposed algorithm was analyzed and compared with the traditional shortest path algorithm and it reported that our method could reduce the time for oversized trucks to pass through intersections. In addition, the proposed algorithm could be adapted to the complex and diverse road networks and provide a reliable scheme for route selection of oversized trucks.

Detailed and accurate information on the spatial variation of soil over low-relief areas is a critical component of environmental studies and agricultural management. Early studies show that the pattern of soil dynamics provides comprehensive information about soil and can be used as a new environmental covariate to indicate spatial variation in soil in low relief areas. In practice, however, data gaps caused by cloud cover can lead to incomplete patterns over a large area. Missing data reduce the accuracy of soil information and make it hard to compare two patterns from different locations. In this study, we introduced a new method to fill data gaps based on historical data. A strong correlation between MODIS band 7 and cumulated reference evapotranspiration has been confirmed by theoretical derivation and by the real data. Based on this correlation, data gaps in MODIS band 7 can be predicted by daily evaporation data. Furthermore, correlations among bands are used to predict soil reflectance in MODIS bands 1–6 from MODIS band 7. A location in northeastern Illinois with a large area of low relief farmland was selected to examine this idea. The results show a good exponential relationship between MODIS band 7 and CET00.5 in most locations of the study area (with average R2 = 0.55, p < 0.001, and average NRMSE 10.40%). A five-fold cross validation shows that the approach proposed in this study captures the regular pattern of soil surface reflectance change in bands 6 and 7 during the soil drying process, with a Normalized Root Mean Square Error (NRMSE) of prediction of 13.04% and 10.40%, respectively. Average NRMSE of bands 1–5 is less than 20%. This suggests that the proposed approach is effective for filling the data gaps from cloud cover and that the method reduces the data collection requirement for understanding the dynamic feedback pattern of soil, making it easier to apply to larger areas for soil mapping.

A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1−RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1−RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active−passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical−vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions.

Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.

China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring.

The vegetation supply water index (VSWI = NDVI/LST) is an effective metric estimating soil moisture in areas with moderate to dense vegetation cover. However, the normalized difference vegetation index (NDVI) exhibits a long water stress lag and the land surface temperature (LST), sensitive to water stress, does not contribute considerably to surface soil moisture monitoring due to the constraints of the mathematical characteristics of VSWI: LST influences VSWI less when LST value is sufficiently high. This paper mathematically analyzes the characteristics of VSWI and proposes a new operational dryness index (surface water content temperature index, SWCTI) for the assessment of surface soil moisture status. SWCTI uses the surface water content index (SWCI), which provides a more accurate estimation of surface soil moisture than that of NDVI, as the numerator and the modified surface temperature, which has a greater influence on SWCTI than that of LST, as the denominator. The validation work includes comparison of SWCTI with in situ soil moisture and other remote sensing indices. The results show SWCTI demonstrates the highest correlation with in situ soil moisture; the highest correlation R = 0.801 is found between SWCTI and the 0–5 cm soil moisture in a sandy loam. SWCTI is a functional and effective method that has a great potential in surface soil moisture monitoring.

With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method based on effective coverage (EC) information, which is used to replace the artificial screening stage in traditional satellite image retrieval tasks. In this method, first, we use a convolutional neural network to extract the EC of each satellite image; then, we use an effective coverage grid set (ECGS) to represent the ECs of all satellite images in the library; finally, the satellite image accurate retrieval algorithm is proposed to complete the process of screening images. The performance evaluation of the method is implemented in three regions: Wuhan, Yanling, and Tangjiashan Lake. The large number of experiments shows that our proposed method can automatically retrieve high-resolution satellite images and significantly improve efficiency.

In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR) were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R ¯ 2 = 0.9875 and a root mean square error (RMSE) of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive (SMAP) active radar soil moisture products and the Global Land data assimilation system (GLDAS) 0–10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale.

Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.