NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

An accurate paddy rice map is crucial for ensuring food security, particularly for Southeast and Northeast Asia. MODIS satellite data are useful for mapping paddy rice at continental scales but have a mixed-pixel problem caused by the coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time series Sentinel-1 and MODIS data. We demonstrated the method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with available agricultural statistics at subnational levels and existing rice maps for some countries. The results demonstrated that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranged from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2=0.93) and Northeast China (R2=0.99). The spatial distribution of the 2017–2019 composite paddy rice map was consistent with that of the rice map from the International Rice Research Institute. The paddy rice planting area may have been underestimated in the region in which the flooding signal was not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.5281/zenodo.5645344 (Han et al., 2021a). Small examples can be found from the following DOI: https://doi.org/10.17632/cnc3tkbwcm.1 (Han et al., 2021b).

Nitrogen deposition in the UK at 1 km resolution from 1990 to 2017

An atmospheric chemistry transport model (FRAME) is used here to calculate annual UK N deposition for the years 1990–2017, at a 1 km × 1 km resolution. Reactive nitrogen (N) deposition is a threat that can lead to adverse effects on the environment and human health. In Europe, substantial reductions in N deposition from nitrogen oxide emissions have been achieved in recent decades. This paper quantifies reductions in UK N deposition following the N emissions peak in 1990. In the UK, estimates of N deposition are typically available at a coarse spatial resolution (typically 5 km × 5 km grid resolution), and it is often difficult to compare estimates between years due to methodological changes in emission estimates. Through efforts to reduce emissions of N from industry, traffic, and agriculture, this study predicts that UK N deposition has reduced from 465 kt N in 1990 to 278 kt N in 2017. However, as part of this overall reduction, there are non-uniform changes for wet and dry deposition of reduced N (NHx) and oxidised N (NOy). In 2017, it is estimated 59 % of all N deposition is in the form of reduced N, a change from 35 % in 1990. This dataset uses 28 years of emissions data from 1990 to 2017 to produce the first long-term dataset of 28 years of N deposition at 1 km × 1 km resolution in the UK. Full data are available at https://doi.org/10.5285/9b203324-6b37-4e91-b028-e073b197fb9f (Tomlinson et al., 2020).

An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.

Remote Sensing of Pasture Degradation in the Highlands of the Kyrgyz Republic: Finer-Scale Analysis Reveals Complicating Factors

Degradation in the highland pastures of the Kyrgyz Republic, a small country in Central Asia, has been reported in several studies relying on coarse spatial resolution imagery, primarily MODIS. We used the results of land surface phenology modeling at higher spatial resolution to characterize spatial and temporal patterns of phenometrics indicative of the seasonal peak in herbaceous vegetation. In particular, we explored whether proximity to villages was associated with substantial decreases in the seasonal peak values. We found that terrain features—elevation and aspect—modulated the strength of the influence of village proximity on the phenometrics. Moreover, using contrasting hotter/drier and cooler/wetter years, we discovered that the growing season weather can interact with aspect to attenuate the negative influences of dry conditions on seasonal peak values. As these multiple contingent and interactive factors that shape the land surface phenology of the highland pastures may be blurred and obscured in coarser spatial resolution imagery, we discuss some limitations with prior and recent studies of pasture degradation.

Regional Variation in Communities of Demersal Fishes and Scavengers Across the CCZ and Pacific Ocean

We synthesize and analyze data from visual transecting approaches and baited camera studies to evaluate fish and invertebrate scavenger communities across the Clarion-Clipperton Zone (CCZ), an area of intense deep-sea mining interest, and neighboring areas of the abyssal Pacific. In abyssal regions including the CCZ most of the top predators are large mobile fishes and crustaceans, and the majority of these are also opportunistic scavengers. Top predators can exert important ecosystem influences and they can be susceptible to sustained anthropogenic disturbances, necessitating their study in the CCZ mining region. In total 157 baited camera deployments from 3 mining exploration license areas, 4 APEIs (Areas of Particular Environmental Interest – one type of no mining zone) and 4 other areas in the Pacific (Hawaii, California, New Zealand and Guam) and 122 visual transects from 7 exploration license areas, 4 no mining zones, and the Peru Basin (DISCOL area) were examined. Many taxa were observed in both sampling techniques but visual transects viewed few fishes overall. Fish and scavenger communities and diversity varied across the CCZ, significantly for baited camera data with a parallel but insignificant pattern for visual transects suggesting that even for these highly mobile species, not all regions of the CCZ are equivalent and the CCZ cannot be managed as one homogenous region. Further CCZ communities were different than communities elsewhere in the abyssal Pacific. The regional variations in community composition are largely the result of varying abundances of species rather than species presence/absence given that most, but not all, of the fishes and scavengers observed have very large ranges. On a more local scale, seamounts had a significantly different scavenger community than neighboring abyssal plains and thus contribute to regional diversity. Visual transect data revealed a similar but insignificant pattern due to low sample sizes. Given the coarse spatial resolution of sampling of fish and scavenger communities in the CCZ, it is not possible to evaluate if no mining zones (APEIs) adequately represent these communities nor where, or if, any biogeographic boundaries exist in the CCZ region. It is possible to conclude that a network of APEIs that covers the spectrum of available habitats at regional and more local scales will be key to conserving fish and scavenger biodiversity.

AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

An accurate paddy rice map is crucial for food security, particularly for Southeast and Northeast Asia. The MODIS satellite data is useful for mapping paddy rice at continental scales but has a problem of mixed-pixel due to coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time-series Sentinel-1 and MODIS data. We demonstrated the method in generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (AsiaRiceMap10m). The resultant paddy rice maps were compared with available agricultural statistics at subnational levels and existing rice maps for some countries. The results show that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranges from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2 = 0.93) and Northeast China (R2 = 0.99). The spatial distribution of the 2017–2019 composite paddy rice map is consistent with the rice map from the International Rice Research Institute. The paddy rice planting area may be underestimated in the region where the flooding signal is not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.17632/j34b3jsvr9.1 (Han et al., 2021a). Find small examples here (https://doi.org/10.17632/cnc3tkbwcm.1) (Han et al., 2021b).

Using Twitter to Track Immigration Sentiment During Early Stages of the COVID-19 Pandemic

In 2020, the world faced an unprecedented challenge to tackle and understand the spread and impacts of COVID-19. Large-scale coordinated efforts have been dedicated to understand the global health and economic implicationsof the pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations, particularlymigrants and individuals of Asian descent, has largely been neglected. Understanding public attitudes towardsmigration is essential to counter discrimination against immigrants and promote social cohesion. Traditional datasources to monitor public opinion – ethnographies, interviews, and surveys – are often limited due to smallsamples, high cost, low temporal frequency, slow collection, release and coarse spatial resolution. New forms ofdata, particularly from social media, can help overcome these limitations. While some bias exists, social mediadata are produced at an unprecedented temporal frequency, geographical granularity, are collected globally andaccessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this paperaims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemicin Germany, Italy, Spain, the United Kingdom and the United States. Results show an increase of migration-relatedTweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence ofa significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset bycomparable increases in positive messages. Additionally, we presented evidence of growing social polarisationconcerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

Nitrogen deposition in the UK at 1 km resolution from 1990 to 2017

An atmospheric chemistry transport model (FRAME) is used here to calculate the UK N deposition for the years 1990–2017. Reactive nitrogen (N) deposition is a threat that can lead to adverse effects on the environment and human health. In Europe, substantial reductions in N deposition from nitrogen oxide emissions have been achieved in recent decades, this paper quantifies reductions in UK N deposition following the N emissions peak in 1990. In the UK, estimates of N deposition are typically available at a coarse spatial resolution (typically 5 km × 5 km grid resolution) and it is often difficult to compare estimates between years due to methodological changes in emission estimates. Through efforts to reduce emissions of N from industry, traffic, and agriculture, this study predicts that UK N deposition has reduced from 465 kt N in 1990 to 278 kt N in 2017. However, as part of this overall reduction, there are non-uniform changes for wet and dry deposition of reduced N (NHx) and oxidised N (NOy). In 2017, it is estimated 59 % of all N deposition is in the form of reduced N, a change from 35 % in 1990. This dataset uses 28 years of emissions data from 1990 to 2017 to produce the first long-term dataset of 28 years of N deposition at 1 km × 1 km resolution in the UK.

Thermodynamics of a fast-moving Greenlandic outlet glacier revealed by fiber-optic distributed temperature sensing

Measurements of ice temperature provide crucial constraints on ice viscosity and the thermodynamic processes occurring within a glacier. However, such measurements are presently limited by a small number of relatively coarse-spatial-resolution borehole records, especially for ice sheets. Here, we advance our understanding of glacier thermodynamics with an exceptionally high-vertical-resolution (~0.65 m), distributed-fiber-optic temperature-sensing profile from a 1043-m borehole drilled to the base of Sermeq Kujalleq (Store Glacier), Greenland. We report substantial but isolated strain heating within interglacial-phase ice at 208 to 242 m depth together with strongly heterogeneous ice deformation in glacial-phase ice below 889 m. We also observe a high-strain interface between glacial- and interglacial-phase ice and a 73-m-thick temperate basal layer, interpreted as locally formed and important for the glacier’s fast motion. These findings demonstrate notable spatial heterogeneity, both vertically and at the catchment scale, in the conditions facilitating the fast motion of marine-terminating glaciers in Greenland.

Validation of snow extent time series derived from AVHRR GAC data at Himalaya-Hindukush

Long-term monitoring of snow cover is crucial for climate and hydrology studies. To meet the increasing demand for a long-term, consistent snow product, an exceptional snow cover climatology was generated dating back to the 1980s using AVHRR GAC data. However, the retrieval of snow extent is not straightforward due to artifacts introduced during data processing, which are partly caused by the coarse spatial resolution of AVHRR GAC data, but also heterogeneous land cover/topography. Therefore, the accuracy and consistency of this long-term AVHRR GAC snow cover climatology needs to be carefully evaluated prior to its application. Here, we extensively validate the AVHRR GAC snow cover extent dataset for the Hindu Kush Himalaya (HKH) region. The mountainous HKH region is of high importance for climate change, impact and adaptation studies. Additionally, the influences of snow depth, land cover type, elevation, slope, aspect, and topographical variability, as well as the sensor-to-sensor consistency have been explored using a snow dataset based on long-term in situ stations and high-resolution Landsat TM data. Moreover, the performance of the AVHRR GAC snow cover dataset was also compared to that of MODIS (MOD10A1 V006). Our analysis shows an overall accuracy of 94 % in comparison with in situ station data. Using a ±3 days temporal filter caused a slight decrease in accuracy (from 94 to 92 %), which is still comparable to MOD10A1 V006 (93.6 %). Validation against Landsat5 TM data over region of P140-R40/41 indicated overall RMSEs of about 13 % and 16 % and overall Biases of about −1 % and −2 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH due to its good quality, unique temporal coverage (1982–2018), and inter-sensor/satellite consistency.