Land Surface Changes Research Articles

Combined effects of urbanization and climate variability on water and carbon balances in a rice paddy-dominated basin in southern China

Abstract Urbanization is known to elevate storm runoff, but how it influences carbon cycle and ecosystem productivity through altering the evapotranspiration (ET) process is less clear. We examined the combined effects of urbanization including change in impervious surface area (ISA) and climate variability on the water and carbon balances of the Qinhuai River Basin (QRB) over 2001–2018. QRB represents a typical rice paddy-dominated region that experienced rapid urbanization in southern China. We improved a monthly scale water supply stress index ecosystem service model by integrating local eddy flux measurements and high-resolution remote sensing data. We found a significant downward trend in both ET (−4.6 mm yr−1, p < 0.05) and gross primary productivity (GPP) (−10.4 gC m−2 yr−1, p < 0.05) but a significant upward trend in water yield (Q) (+28.6 mm yr−1, p < 0.05). These ecosystem function changes coincided with a 96% increase in urban areas, 1.9-fold increase in ISA, and a 37% reduction in rice paddy fields. The mean annual watershed GPP decreased from 1048 gC m−2 to 998 gC m−2 while the annual Q increased from 284 mm to 669 mm from 2001 to 2018. Scenario modeling experiments suggested that the negative impacts of loss of rice paddy fields and increase in ISA on ET and GPP overwhelmed the positive impacts of climate warming. The reduction in GPP and increase in Q were largely attributed to the increases in ISA, not necessarily due to changes in land use types (e.g. urban area). The expansion of urban area, increase in ISA and reduction in leaf area index, and increase in precipitation explained the increase in Q. Our research offers insight about the interactions of carbon and water cycles through the critical ET processes under a changing climate and land surface characteristics at a watershed level. Our modeling tool and analysis provides land managers and policy makers information for designing effective ‘Urban Nature-based Solutions’ to mitigate the negative environmental effects of urbanization on carbon and water resources.

Satellite-observed relationships between land cover, burned area, and atmospheric composition over the southern Amazon

Abstract. Land surface changes can have substantial impacts on biosphere–atmosphere interactions. In South America, rainforests abundantly emit biogenic volatile organic compounds (BVOCs), which, when coupled with pyrogenic emissions from deforestation fires, can have substantial impacts on regional air quality. We use novel and long-term satellite records of five trace gases, namely isoprene (C5H8), formaldehyde (HCHO), methanol (CH3OH), carbon monoxide (CO), and nitrogen dioxide (NO2), in addition to aerosol optical depth (AOD), vegetation (land cover and leaf area index), and burned area. We characterise the impacts of biogenic and pyrogenic emissions on atmospheric composition for the period 2001 to 2019 in the southern Amazon, a region of substantial deforestation. The seasonal cycle for all of the atmospheric constituents peaks in the dry season (August–October), and the year-to-year variability in CO, HCHO, NO2, and AOD is strongly linked to the burned area. We find a robust relationship between the broadleaf forest cover and total column C5H8 (R2 = 0.59), while the burned area exhibits an approximate fifth root power law relationship with tropospheric column NO2 (R2 = 0.32) in the dry season. Vegetation and burned area together show a relationship with HCHO (R2 = 0.23). Wet-season AOD and CO follow the forest cover distribution. The land surface variables are very weakly correlated with CH3OH, suggesting that other factors drive its spatial distribution. Overall, we provide a detailed observational quantification of biospheric process influences on southern Amazon regional atmospheric composition, which in future studies can be used to help constrain the underpinning processes in Earth system models.

Utilization of Sentinel Image Multitemporal Data for Landslide Potential Identification in Pujon Sub-District

Landslides are one of the most common disasters in Indonesia. Landslides are often triggered by high rainfall and occur for several days. Unstable soil structure is very easy to experience landslides. The case study of this research is in Pujon Sub-district, Malang Regency. Pujon sub-district has a hilly and highland topography. Its hilly-mountainous relief and steep slopes cause Pujon Sub-district to experience frequent natural disasters, especially landslides. The research was conducted by processing Sentinel 1 image using DInSAR method to obtain data on land surface change and weighting scoring method to obtain information on landslide prone areas. The processing of DInSAR method produces the value of vertical land surface change. The largest increase in land surface with a displacement value of 0.2972 m. And the largest land subsidence is -0.2234 m. Such movement can be an indicator of landslide occurrence. The GIS method produces 3 classes of landslide vulnerability level, namely low, medium and high in each year in Pujon sub-district. The average landslide prone area in the last 5 years is 15081.1331 ha. The processing results of DInSAR method and weighting scoring method can describe the landslide prone areas in Pujon Sub-district which is expected to be useful for landslide mitigation efforts.

Landscape and Soil Erosion Changes Along Different Types of Road Over the Past 30 Years in the Largest Loess Tableland of China

ABSTRACTRoad construction, as a product of modern civilization, can alter landscape patterns and accelerate erosion, especially in the loess tableland area of China with fragile eco‐environment. Different types of road could change land surface and increased soil erosion in different ways, while knowledge about this topic is limited. In this study, our objective is to evaluate the landscape and soil erosion changes along different types of road in the largest loess tableland of China. Results showed that: (1) The land use type became more diverse along the three types of road in 1990–2020, leading to increased landscape fragmentation and spatial heterogeneity. (2) Different types of road had different proportions of soil erosion values. In the expressway and provincial road, the proportion of low soil erosion values (below 100 t ha−1 yr−1) was > 70% in 1990–2020; in the national road, the proportion of low erosion values (below 100 t ha−1 yr−1) was > 50%. (3) In 1990–2020, soil erosion increased with the increase of road‐induced landscape fragmentation along the three types of road. For the expressway, soil erosion value increased from 91.5 to 98.5 t ha−1 yr−1 between 1990 and 2020; for the national road, soil erosion value increased from 174.5 to 184.8 t ha−1 yr−1; and for the provincial road, soil erosion value increased from 93.7 to 97.1 t ha−1 yr−1. (4) Effective soil and water conservation programs could mitigate erosion. Compared with the simulated soil erosion value, the actual soil erosion value decreased along the expressway, national road, and provincial road by 1.21, 3.67, and 2.96 t ha−1 yr−1, respectively, between 2005 and 2020. This study emphasizes the importance of road in aggravating erosion and the effectiveness of soil and water conservation programs in mitigating soil erosion within the loess area.

Constraining the origin of the Norwegian strandflat – The influence of isostatic and dynamic surface changes

The Norwegian strandflat is a prominent low-relief bedrock surface found near sea level along most of the west coast of Norway. Its origin has been discussed throughout the last 130 years but is yet to be resolved. Some studies suggest that the strandflat represent a tropical weathering front of Mesozoic age that has since been buried and re-exhumed, while others relate its origin to Pleistocene periglacial and glacial processes and/or wave-induced weathering and erosion. Previous interpretations of the strandflat have considered postglacial isostatic uplift, but the impacts of isostatic changes due to glacial erosion and deposition, as well as dynamic surface changes driven by mantle convection, have been largely overlooked. Here we examine how geomorphological-driven isostatic changes and dynamic surface changes have influenced the land-surface elevation along the Norwegian coast during late Pliocene-Quaternary (the last ca. 3 million years). We employ quantitative estimates of glacial erosion and deposition to assess the flexural isostatic response from the resulting load changes. Our analyses show that patterns of geomorphic isostatic adjustments and dynamic surface changes are generally not reflected in the present elevation of the strandflat. Only the loading effect from the deposition of the North Sea Fan can clearly be correlated with the submerged strandflat found near Stad (~62 °N). Our results imply that if the strandflat formed synchronously along the Norwegian coast as a flat surface at sea level, the strandflat we observe today must have developed after the majority of late Pliocene-Quaternary glacial erosion took place, but prior to the main deposition of the North Sea Fan. This would place strandflat formation within the last few glacial cycles, but before the Last Glacial Maximum (LGM). This inferred pre-LGM age of the strandflat is generally consistent with cosmogenic nuclide exposure ages and observed striations on the strandflat. Finally, we examine ice cover and land-surface changes relative to sea level during the last 80,000 years and find no extended periods favorable for synchronous strandflat formation across all regions along the Norwegian coast. This implies that either the strandflat is diachronous, or that the processes of formation have either been extremely fast under certain conditions or are independent of sea level, for instance related to glacial erosion.

Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

Human-induced warming accelerates local evapotranspiration and precipitation recycling over the Tibetan Plateau

The Tibetan Plateau faces changing precipitation and environmental conditions affecting alpine ecosystems and downstream freshwater sustainability. While aerosol influence has been highlighted, how human-induced greenhouse warming impacts the plateau’s moisture recycling remains unclear. Here we show that the Tibetan Plateau’s recent precipitation changes result from enhanced precipitation recycling and moisture convergence that offset the decline in monsoon- and westerly-associated moisture transport based on 40-year Lagrangian simulations and water budget analyses. Local evapotranspiration is observed to increase faster in percentage than precipitation, a trend expected to continue in future warming scenarios according to climate projections. Greenhouse gas emission causes widespread wetting while weakening the southerly monsoons across the Himalayas, heightening the sensitivity of precipitation to evapotranspiration and thereby local land surface changes. This trend exacerbates vulnerability in the water cycle of high mountain Asia, calling for proactive management to address potential risks and ensure future water and food security in Asia.

Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach.

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city.

Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany

Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany

Population Pressure and Changing Land Use Land Cover in Morigaon District, Assam, India - A Geospatial Analysis

Objective: This study intends to prioritize the underlying mechanisms underpinning the spatial-temporal variations in Land Use and Land Cover (LULC) change in the Morigaon district, located in the middle part of Brahmaputra valley of Assam, India covering an area of 1498 km2.At the same time, this study focuses on the impact of population pressure on changing LULC in the study area. Methods: This research adopted geospatial approach for analysing land use land cover change in the study area. Satellite images from the years 1992 to 2022 have been utilized to identify the land transformation. The analysis of the land transformation has taken into account the five main LULC classifications of agriculture, forest, water bodies, built-up lands, and sand deposits. It has been discovered that the LULC classes identified in this study, which comprise both natural and anthropogenic elements, have a very strong link with the population growth in the Morigaon district. To show the relationship between population expansion and LULC indicators Pearson correlation method is used. Findings: Water bodies, forests and agriculture have significantly decreased by 0.13%, 4.66% and 1.64% respectively due to substantial anthropogenic activity during the past 30 years. In contrast, sand deposits and built-up lands increased by 3.13% and 3.30%. To show the relationship between population expansion and LULC indicators Pearson correlation method is used. Very significant relationships have been found between population and built-up lands with rho value 0.95. The relationship between population with agriculture, forest, water bodies and sand deposits have been seen with rho value -0.51, -0.89, -0.64 and 0.97 respectively. The loss of the geographic extent of natural classes like water bodies, forests, signifies that the geometrical progression in the human population has greatly amplified anthropogenic activities. Novelty: The novelty lies in adopting geospatial techniques that would certainly enhance land resource conservation. Environmental concerns have been increasing due to significant land surface change, which is predominantly the result of human-induced activities. In the studied area, socioeconomic, demographic, and proximity factors have a significant impact on the LULC change processes. The findings can be utilized to forecast potential LULC scenarios and offer direction for land management strategy. Keywords: LULC, Satellite Images, Anthropogenic, Pearson Correlation, Proximity Factor

The Decline in Summer Fallow in the Northern Great Plains Cooled Near‐Surface Climate but had Minimal Impacts on Precipitation

AbstractLand management can moderate or intensify the impacts of a warming atmosphere. Since the early 1980s, nearly 116,000 km2 of cropland that was once held in fallow during the summer is now planted in the northern North American Great Plains. To simulate the impacts of this substantial land cover change on regional climate processes, convection‐permitting model experiments using the Weather Research and Forecasting model were performed to simulate modern and historical amounts of summer fallow. The control simulation was extensively validated using multiple observational data products as well as eddy covariance tower observations. Results of these simulations show that the transition from summer fallow to modern land cover led to ∼1.5°C cooler temperatures and decreased vapor pressure deficit by ∼0.15 kPa during the growing season across the study region, which is consistent with observed cooling trends. The cooler and wetter land surface with vegetation led to a shallower planetary boundary layer and lower lifted condensation level, creating conditions more conducive to convective cloud formation and precipitation. Our model simulations however show little widespread evidence of land surface changes effects on precipitation. The observed precipitation increase in this region is more likely related to increased moisture transport by way of the Great Plains Low Level Jet as revealed by the ERA5 reanalysis. Our results demonstrate that land cover change is consistent with observed regional cooling in the northern North American Great Plains but changes in precipitation cannot be explained by land management alone.

Pemetaan Perubahan Permukaan Tanah di Kawasan Pulau Nias dengan Menggunakan Data Sentinel - 1A IW

The determination of changes in the land surface is an event caused by changes in the volume of the rock layer below. and geological processes such as tectonic and volcanic activity, removal of material from the subsurface such as mining and natural causes such as the occurrence of holes in limestone. The purpose of this study is to determine the magnitude of land surface changes that occur in the Nias Island area caused by earthquakes. The data used in the processing stage with the DinSAR method in 2022 recording, with specifications of dual polarization VV + VH, beam mode IW (interferometric wide) and flight direction ascending. Sentinel 1 data used in this study are 4 pairs of scans, where each pair of scans consists of one scans being the master and another scans being the slave. The master image is the SAR image before the earthquake, while the slave image is after the earthquake. The magnitude of land surface changes that occurred on Nias Island, based on the 2022 earthquake, the largest uplift was the earthquake on December 30, 2022, with a range of values (1.012042) mm with an area of 72,641,981 Hectares. The largest subsidence occurred in the earthquake on December 30, 2022, with a range of values ranging from (-1.373125) mm to (-1.235427) mm with an area of 61347,891 Hectares.

DASUNet: a deeply supervised change detection network integrating full-scale features

The change detection (CD) technology has greatly improved the ability to interpret land surface changes. Deep learning (DL) methods have been widely used in the field of CD due to its high detection accuracy and application range. DL-based CD methods usually cannot fuse the extracted feature information at full scale, leaving out effective information, and commonly use transfer learning methods, which rely on the original dataset and training weights. To address the above issues, we propose a deeply supervised (DS) change detection network (DASUNet) that fuses full-scale features, which adopts a Siamese architecture, fuses full-scale feature information, and realizes end-to-end training. In order to obtain higher feature information, the network uses atrous spatial pyramid pooling (ASPP) module in the coding stage. In addition, the DS module is used in the decoding stage to exploit feature information at each scale in the final prediction. The experimental comparison shows that the proposed network has the current state-of-the-art performance on the CDD and the WHU-CD, reaching 94.32% and 90.37% on F1, respectively.

Deforestation‐Driven Increases in Shallow Clouds Are Greatest in Drier, Low‐Aerosol Regions of Southeast Asia

AbstractAnthropogenic activity drives extensive tropical deforestation, particularly in Southeast Asia where 16% of total forest cover was lost between 2000 and 2020. While land surface changes significantly affect the atmosphere, their net impact on convective clouds is not well‐constrained. Here, we use satellite data to demonstrate long‐term deforestation in Southeast Asia robustly alters cloud properties and provide the first observational evidence that the magnitude of this response depends on the atmospheric environment. Deforestation drives a shift toward more widespread, shallower clouds during the daytime, with amplified effects in dry inland areas compared with moist coastal regions. Aerosols only weakly modulate the cloud fraction response, but offset the cloud top height response to deforestation, suggesting the influence of aerosol indirect effects. We conclude that the local signature of forest loss is not uniform, and regional differences in climatology must be considered when assessing deforestation impacts on clouds and the climate system.

SatelliteChangeNet: Deep Learning approach for Detection & Prediction

In geoscience, Detection is a useful method for analyzing land surface changes using data from Earth observation and for uncovering links between human activities and environmental phenomena. Detection in remote sensing is a rapidly evolving area of interest that is relevant for a number of fields. Recent years have seen a large number of publications and progress, even though the challenge is far from solved. This review focuses on deep learning applied to the task of Detection in multispectral remote-sensing images. In this work, SatelliteChangeNet addresses the growing need for an accurate and effective method to monitor and predict changes in satellite imagery, which is important for many purposes such as environmental monitoring, urban planning, and agriculture and disaster management. The changes always aim to show the struggle with the body and its different structures and lead to the search for a deep learning process. The program focuses on the use of satellite data for exploration of new areas, urban development analysis, environmental management damage, and change and prediction with important applications in agriculture. Water resources and farmland provide a lot of information about our planet. Analyzing these changes over time is important for understanding land use, environmental change, and natural hazards

Attribution Analysis of Discharge Variations in the Inner Mongolia Basin of the Yellow River

Climate change and human activities are the main causes of discharge variation. Therefore, calculating the contribution of climate change and human activities to discharge variation in the Inner Mongolia Basin of the Yellow River can provide a reference basis for alleviating water resources stress in the Inner Mongolia Basin of the Yellow River. Firstly, based on the water balance theory and the Budyko hypothesis-based evapotranspiration formula, this study constructed a coupled water and heat balance equation and calibrated and validated the model using observed annual discharge depth data from the Toudao Guai hydrological station. The results show that the water-heat coupling model has good simulation accuracy at the annual scale, with NSE=0.76, RMSE=10.45, and RRMSE=19.5%, and overall, the simulated discharge results are close to the observed values. Secondly, this study also used the Mann-Kendall test method to determine the abrupt change points of discharge and analyzed the contribution rates of various meteorological factors. The results indicate that precipitation accounts for 88.79% of the discharge variation in the Inner Mongolia segment of the Yellow River basin, followed by land surface changes at 13.6%, while potential evaporation has the smallest contribution to discharge variation.

Runoff variation in midstream Hei River, northwest China: Characteristics and driving factors analysis

Study regionThe midstream of the Hei River Basin (HRB). Study focusThe study investigated the changes in hydrological elements at different time scales in the basin from 1982 to 2020. We used the Budyko coupled balance equation to quantitatively assess the contributions of climate change and human activity-induced land surface changes to runoff during different periods. In addition, the driving factors of flow consumption within the basin were analyzed using wavelet coherence and generalized additive model (GAM). New hydrological insights for the regionPrior to 2001, there was a significant decrease in annual-scale runoff in the midstream. After that, a partial recovery was observed due to the influence of the Ecological Water Transfer Project (EWTP), while the NDVI trend exhibited an opposite pattern. The study indicates that human-induced modification of the underlying surface is the primary driving factor for the decrease in runoff, with land surface factors contributing 84.69% and 65.27% to the runoff reduction during two periods of change, respectively. Further research on the driving forces of runoff consumption reveals that vegetation cover and climate factors jointly regulate the variations in runoff consumption in the midstream, with multifactor effects surpassing those of individual factors, and NDVI emerges as the predominant controlling factor for runoff consumption. Moreover, all anthropogenic factors exhibit a high explanatory power for runoff consumption.

Changes in global land surface frozen ground and freeze‒thaw processes during 1950–2020 based on ERA5-Land data

Frozen ground (FG) plays an important role in global and regional climates and environments through changes in land freeze‒thaw processes, which have been conducted mainly in different regions. However, the changes in land surface freeze‒thaw processes under climate change on a global scale are still unclear. Based on ERA5-Land hourly land skin temperature data, this study evaluated changes in the global FG area, global land surface first freeze date (FFD), last freeze date (LFD) and frost-free period (FFP) from 1950 to 2020. The results show that the current FG areas (1991–2020 mean) in the Northern Hemisphere (NH), Southern Hemisphere (SH), and globe are 68.50 × 106, 9.03 × 106, and 77.53 × 106 km2, which account for 72.4%, 26.8%, and 60.4% of the exposed land (excluding glaciers, ice sheets, and water bodies) in the NH, SH and the globe, respectively; further, relative to 1951–1980, the FG area decreased by 1.9%, 8.8%, and 2.8%, respectively. Seasonally FG at lower latitudes degrades to intermittently FG, and intermittently FG degrades to non-frozen ground, which caused the global FG boundary to retreat to higher latitudes from 1950 to 2020. The annual FG areas in the NH, SH, and globe all show significant decreasing trends (p < 0.05) from 1950 to 2020 at −0.32 × 106, −0.22 × 106, and −0.54 × 106 km2 per decade, respectively. The FFP prolongation in the NH is mainly influenced by LFD advance, while in the SH it is mainly controlled by FFD delay. The prolongation trend of FFP in the NH (1.34 d per decade) is larger than that in the SH (1.15 d per decade).

A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies

Abstract. Our understanding and predictive capability of streamflow processes largely rely on high-quality datasets that depict a river's upstream basin characteristics. Recent proliferation of large sample hydrology (LSH) datasets has promoted model parameter estimation and data-driven analyses of hydrological processes worldwide, yet existing LSH is still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of anthropogenic activities. To bridge the gap, we contribute the synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) to complement existing LSH datasets, which covers 21 568 watersheds from 13 agencies for as long as 43 years based on discharge observations scraped from the internet. In addition to annual and monthly streamflow indices, each basin's daily meteorological variables (i.e., precipitation, 2 m air temperature, longwave/shortwave radiation, wind speed, actual and potential evapotranspiration), daily–weekly water storage terms (i.e., snow water equivalence, soil moisture, groundwater percentage), and yearly dynamic descriptors of the land surface characteristics (i.e., urban/cropland/forest fractions, leaf area index, reservoir storage and degree of regulation) are also provided by combining openly available remote sensing and reanalysis datasets. The uncertainties in all meteorological variables are estimated with independent data sources. Our analyses reveal the following insights: (i) the meteorological data uncertainties vary across variables and geographical regions, and the revealed pattern should be accounted for by LSH users; (ii) ∼6 % watersheds shifted between human-managed and natural states during 2001–2015, e.g., basins with environmental recovery projects in northeast China, which may be useful for hydrologic analysis that takes the changing land surface characteristics into account; and (iii) GSHA watersheds showed a more widespread declining trend in runoff coefficient than an increasing trend, pointing towards critical water availability issues. Overall, GSHA is expected to serve hydrological model parameter estimation and data-driven analyses as it continues to improve. GSHA v1.1 can be accessed at https://doi.org/10.5281/zenodo.8090704 and https://doi.org/10.5281/zenodo.10433905 (Yin et al., 2023a, b).

Developing a Multi-Scale Convolutional Neural Network for Spatiotemporal Fusion to Generate MODIS-like Data Using AVHRR and Landsat Images

Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce moderate-spatial-resolution data, like Moderate-Resolution Imaging Spectroradiometer (MODIS), which has moderate spatial detail and frequent temporal coverage. This limitation arises from the challenge of combining coarse- and fine-spatial-resolution data, due to their large spatial resolution gap. This study presents a novel model, named multi-scale convolutional neural network for spatiotemporal fusion (MSCSTF), to generate MODIS-like data by addressing the large spatial-scale gap in blending the Advanced Very-High-Resolution Radiometer (AVHRR) and Landsat images. To mitigate the considerable biases between AVHRR and Landsat with MODIS images, an image correction module is included into the model using deep supervision. The outcomes show that the modeled MODIS-like images are consistent with the observed ones in five tested areas, as evidenced by the root mean square errors (RMSE) of 0.030, 0.022, 0.075, 0.036, and 0.045, respectively. The model makes reasonable predictions on reconstructing retrospective MODIS-like data when evaluating against Landsat data. The proposed MSCSTF model outperforms six other comparative models in accuracy, with regional average RMSE values being lower by 0.005, 0.007, 0.073, 0.062, 0.070, and 0.060, respectively, compared to the counterparts in the other models. The developed method does not rely on MODIS images as input, and it has the potential to reconstruct MODIS-like data prior to 2000 for retrospective studies and applications.