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With the advancement of global warming, the Arctic sea routes (ASRs) may open for the entire year. The ASRs will be far more competitive than they are now, and they will be the major international sea routes in the future. To date, most studies have researched the economic feasibility in the short term from a company’s perspective. To help to plan the shipping market in the future, we developed a three-stage model to simulate the trade demand of the ASRs for the long term. This model firstly considers the seasonal sea ice dynamics in the future and plans new paths for vessels shipping through the Arctic. Additionally, an improved trade prediction model was developed to adapt to the long-term forecasts. After verification, the accuracy of the model was found to be very high (R2 = 0.937). In comparison with another transportation cost model and a trade prediction model, our model was more reasonable. This study simulated the trade volumes of China, Europe (EU), and North America (NA) in 2100 with the ASRs open. The results show that the percentage of port trade can be up to 26% in representative concentration pathway (RCP)2.6, and the percentage of port trade can be up to 52% in RCP8.5.

Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia.

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

A band selection model was described for efficient and accurate remotely-sensed vegetation mapping in cloudy mixed-vegetation areas, demonstrated with an application on mapping mangroves in Southeast Asia (SE Asia). We show how to use multi-source satellite imagery and Cloud Computing Platforms to improve mapping and computational efficiency in complex environments. A key element of the method relies upon field surveys to establish a detailed sample database that includes easily-confused land cover. The Maximal Separability and Information (MSI) model was developed to select key bands for target land cover classification from multiple satellite imagery based on two principles: 1. maximize separability of the target cover from other land cover; and 2. maximize and prioritize information from band combinations. Application of the MSI model to map mangroves in SE Asia using three optical and SAR data systems (Landsat OLI, Sentinel-2 and Sentinel-1) showed: 1. Sentinel-2 is better at classifying mangrove than Landsat and Sentinel-1; and 2. SWIR, NIR and Red bands (with SWIR in particular) are effective in separating mangrove from other vegetation. The MSI-mapped mangroves showed lower computation cost compared to using all bands from individual satellites, and higher accuracy (above 90%) when applied to SE Asia. It was robust in tolerating smaller sample sizes, thereby demonstrating computational feasibility and substantial improvements with the MSI model for large-scale land cover mapping in complex environments.

With more and more crowdsourcing geo-tagged field photos available online, they are becoming a potentially valuable source of information for environmental studies. However, the labelling and recognition of these photos are time-consuming. To utilise such information, a land cover type recognition model for field photos was proposed based on the deep learning technique. This model combines a pre-trained convolutional neural network (CNN) as the image feature extractor and the multinomial logistic regression model as the feature classifier. The pre-trained CNN model Inception-v3 was used in this study. The labelled field photos from the Global Geo-Referenced Field Photo Library ( http://eomf.ou.edu/photos ) were chosen for model training and validation. The results indicated that our recognition model achieved an acceptable accuracy (48.40% for top-1 prediction and 76.24% for top-3 prediction) of land cover classification. With accurate self-assessment of confidence, the model can be applied to classify numerous online geo-tagged field photos for environmental information extraction.

Urban expansion plays a dominant role in the urban heat island (UHI) formation and is thus the essence and fundamental characteristic of the urban fabric. In this study, the responses of UHI to the urban expansion in the past decades were simulated using the coupled weather research forecast/urban canopy model (WRF/UCM) system from the 1980s to 2005 and in the future in 2050 embedded with the fine spatial resolution land use/land cover (LULC) datasets over the Beijing–Tianjin–Hebei (BTH) metropolitan area. With the urban expansion, the validations suggested that the designed models in this research can well simulate the generation and development of UHI. Due to urban expansion, the minimum temperature would rise by about 5 K in the newly developed areas. The temperature over the old urban areas would also increase (

In order to use the global available eddy-covariance (EC) flux dataset and remote-sensing measurements to provide estimates of gross primary productivity (GPP) at landscape (101–102 km2), regional (103–106 km2) and global land surface scales, we developed a satellite-based GPP algorithm using LANDSAT data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index – EVI, Land Surface Water Index – LSWI). The upscalling framework involves flux footprint climatology modelling and data-model fusion. This approach was first applied to an evergreen coniferous stand in the subtropical monsoon climatic zone of south China. The EC measurements at Qian Yan Zhou tower site (26°44´48" N, 115°04´13" E), which belongs to the China flux network and the LANDSAT and MODIS imagery data for this region in 2004 were used in this study. A consecutive series of LANDSAT-like images of the surface reflectance at an 8-day interval were predicted by blending the LANDSAT and MODIS images using an existing algorithm (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model). The seasonal dynamics of GPP were then predicted by the satellite-based algorithm. MODIS products explained 60% of observed variations of GPP and underestimated the measured annual GPP (= 1879 g C m−2) by 25–30%; while the satellite-based algorithm with default static parameters explained 88% of observed variations of GPP but overestimated GPP during the growing seasonal by about 20–25%. The optimization of the satellite-based algorithm using a data-model fusion technique with the assistance of EC flux tower footprint modelling reduced the biases in daily GPP estimations from about 2.24 g C m−2 day−1 (non-optimized, ~43.5% of mean measured daily value) to 1.18 g C m−2 day−1 (optimized, ~22.9% of mean measured daily value). The remotely sensed GPP using the optimized algorithm can explain 92% of the seasonal variations of EC observed GPP. These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modelling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle.

At high latitudes, lake and river ecosystems are predominant and these ecosystems are undergoing significant changes due to climate change. Although many scientists have studied lakes and rivers in the Arctic region, the inland water dynamics in this region at the continental scale remain unknown. In this study, the dynamics of the Arctic water were analyzed at the continental scale using Landsat ortho-rectified surface reflectance products of fine spatial and temporal resolutions for the period of 2000&ndash, 2016, using the random forests method. The results of this study produced the following revelations: (i) the water area is decreasing year by year in the long term, (ii) the water loss and gain always show the same dynamic pattern spatially and temporally, (iii) the spatial distribution of the water budget is strongly linked to permafrost, which implies that permafrost determines the distribution pattern of the water dynamics more than climatic factors, and (iv) the dynamics of the water show a certain rule with surface temperature, but the pattern of the dynamics cannot be explained by temperature alone.

The exodus of people from rural areas to cities brings many detrimental environmental, social and cultural consequences. Monitoring spatiotemporal change by referencing the historical timeline or incidence has become an important way to analyze urbanization. This study has attempted to attain the cross-sectional analysis of Kathmandu valley that has been plagued by rampant urbanization over the last three decades. The research utilizes Landsat images of Kathmandu valley from 1976 to 2015 for the transition analysis of land use, land cover and urban sprawl for the last four decades. Results showed that the urban coverage of Kathmandu valley has tremendously increased from 20.19 km2 in 1976 to 39.47 km2 in 1989 to 78.96 km2 in 2002 to 139.57 km2 in 2015, at the cost of cultivated lands, with an average annual urban growth rate of 7.34%, 7.70% and 5.90% in each temporal interval, respectively. In addition, the urban expansion orientation analysis concludes the significant urban concentration in the eastern part, moderately medium in the southwest and relatively less in the western and northwest part of the valley. Urbanization was solely accountable for the exploitation of extant forests, fertile and arable lands and indigenous and cultural landscapes. Unattended fallow lands in suburban areas have compounded the problem by welcoming invasive alien species. Overlaying the highly affected geological formations within the major city centers displays that unless the trend of rapid, unplanned urbanization is discontinued, the future of Kathmandu is at the high risk. Since land use management is a fundamental part of development, we advocate for the appropriate land use planning and policies for sustainable and secure future development.

A general greening trend in the Arctic tundra biome has been indicated by satellite remote sensing data over recent decades. However, since 2011, there have been signs of browning trends in many parts of the region. Previous research on tundra greenness across the Arctic region has relied on the satellite-derived normalized difference vegetation index (NDVI). In this research, we initially used spatially downscaled solar-induced fluorescence (SIF) data to analyze the spatiotemporal variation of Arctic tundra greenness (2007&ndash, 2013). The results derived from the SIF data were also compared with those from two NDVIs (the Global Inventory Modeling and Mapping Studies NDVI3g and MOD13Q1 NDVI), and the eddy-covariance (EC) observed gross primary production (GPP). It was found that most parts of the Arctic tundra below 75&deg, N were browning (&ndash, 0.0098 mW/m2/sr/nm/year, where sr is steradian and nm is nanometer) using SIF, whereas spatially and temporally heterogeneous trends (greening or browning) were obtained based on the two NDVI products. This research has further demonstrated that SIF data can provide an alternative direct proxy for Arctic tundra greenness.

Remote sensing of sun-induced chlorophyll fluorescence (SIF) has recently attracted major interest as the SIF is directly linked to the photosynthesis. Nevertheless, SIF retrievals from the observed apparent radiance via the most common fraunhofer line discrimination (FLD) method are significantly affected by the directional reflectance. This study investigates the effects of directional reflectance on SIF estimates by in-situ multiangular spectral measurements of lawn grass. Results show that the SIFs in both O2-B and O2-A atmospheric absorption bands vary greatly with view angles. Consequently, a new retrieval strategy based on kernel-driven BRDF (Bidirectional Reflectance Distribution Function) models was proposed. We initially demonstrate that the presented approach has the potential to eliminate the directional effects, producing the isotropic SIFs that are considered more closely related to vegetation physiological variations, and it would better comply with the FLD implied assumption of lambertian surface reflection. This study may further promote the understanding of the emission mechanism of remotely sensed SIFs.

The light-use efficiency (LUE) is one of critical parameters in the terrestrial ecosystem production studies. Although some published paper on the LUE in cropland had used hyperspectral data, lack of study in literature investigated the cropland LUE using the surface reflectance with high spatiotemporal and high spectral data with multiple angles. Based on the observed reflectance and flux data, a Bidirectional Reflectance Distribution Function (BRDF) of vegetation index (Photochemical Reflectance Index, PRI) at continuous time series was established by integrating of a semi-empirical kernel-driven BRDF model (RossThick-LiSparse), a footprint model (the Simple Analytical Footprint model on Eulerian coordinates for scalar Flux, SAFE-f) and a LUE model. Besides, based on the sky-condition (direct/diffused radiation) data, the relationships between the PRI and sunlit/shaded LUE under corresponding sky conditions were established.

As urban thermal environments are being caused by global climatic changes and urbanization is not uniform on diurnal, seasonal, or annual scales, the spatiotemporal patterns of surface urban heat islands (SUHI) similarly vary between cities across regions. This research assessed the spatiotemporal variations in SUHI intensities (SUHII), and then revealed their spatiotemporal patterns and relationships that existed within 32 major cities in China using spatialization technologies, such as the self-organizing map (SOM) method and statistical methods. Results showed that the spatial patterns of the SUHII patterns in China were significantly affected by the climatic types, whereas human heat discharge also disturbed the patterns to a certain extent. Specifically, the daytime SUHIIs in China had much higher seasonal variations in North China than in South China. The nighttime SUHIIs were much weaker and more stable than the daytime SUHIIs, and had far more obvious spatial patterns with much higher values in North China than in South China. As for the temporal regimes, the temporal variation in the SUHIIs in one city was more related to the development of the urbanization. To be specific, not all cities were experiencing increasingly worse urban thermal environments with urbanization as reported by previous studies. This research not only proposes a spatiotemporal framework to study the SUHIIs patterns and their relationships, but also provides an in-depth and comprehensive understanding of SUHIIs in China.

Research on the land use of the coastal zone in the sea–land direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and seaward land use indirectly. However, in the existing research, few have paid attention to the land use in sea–land direction, let alone the sequential relationship between land-use types. The sequential relationship would be useful in land use planning and rehabilitation of the landscape in the sea–land direction, and the association between land-use types, particularly the inland land use and seaward land use, is not discussed. Therefore, This study presents a model named ARCLUSSM (Association Rules-based Coastal Land use Spatial Sequence Model) to mine the sequential pattern of land use with interesting associations in the sea–land direction of the coastal zone. As a case study, the typical coastal zone of Bohai Bay and the Yellow River delta in China was used. The results are as follows: firstly, 27 interesting association patterns of land use in the sea–land direction of the coastal zone were mined easily. Both sequential relationship and distance between land-use types for 27 patterns among six land-use types were mined definitely, and the sequence of the six land-use types tended to be tidal flat > shrimp pond > reservoir/artificial pond > settlement > river > dry land in sea–land direction. These patterns would offer specific support for land-use planning and rehabilitation of the coastal zone. There were 19 association patterns between seaward and landward land-use types. These patterns showed strong associations between seaward and landward land-use types. It indicated that the landward land use might have some impacts on the seaward land use, or in the other direction, which may help to reveal the interactions between inland land use and the ocean. Thus, the ARCLUSSM was an efficient tool to mine the sequential relationship and distance between land-use types with interesting association rules in the sea–land direction, which would offer practicable advice to appropriate coastal zone management and planning, and might reveal the interactions between inland land use and the ocean.

Due to the budget and technical limitations, remote sensing sensor designs trade spatial resolution, swath width and spectral resolution. Consequently, no sensor can provide high spatial resolution, high temporal resolution and high spectral resolution simultaneously. However, the ability of Earth observation at fine resolution is urgently needed for global change science. One possible solution is to "blend" the reflectance from a variety of satellite data sources, including those providing high spatial resolution and less frequent coverage (e.g., Landsat Thematic Mapper, TM), daily global data (e.g., Moderate Resolution Imaging Spectroradiometer, MODIS), and high spectral resolution and infrequent revisit cycle (e.g., Hyperion). However, the previous algorithms for blending multi-source remotely sensed data have some shortcomings, especially with regard to hyperspectral information. This study has developed a SPAtial-Temporal-Spectral blending model (SPATS) that can simulate surface reflectance with high spatial-temporal-spectral resolution. SPATS is based on an existing spatial-temporal image blending model and a spatial-spectral image blending model. The performance of SPATS was tested with both simulated and observed satellite data, using Landsat TM, Hyperion and MODIS data, as well as heterogeneous landscapes as examples. The results show that the high spatial-temporal-spectral resolution reflectance data can be applied to investigations of global landscapes that are changing at different temporal scales.

Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.