Satellite-derived Datasets Research Articles

The variability of particulate organic carbon in the northern South China Sea during the 2009–2010 El Niño

The influences of El Niño on the changes of sea surface temperature (SST) and chlorophyll have been well studied, but its impact on changes of particulate organic carbon (POC) remains unclear. In this study, we analyzed the time series of POC and found that from January 2003 to December 2011, the spatially-averaged POC increased at a rate of 0.24 mg/m3 per year. However, during the 2009–2010 El Niño, the spatially-averaged POC increased at a rate of 1.78 mg/m3 per month. This indicated that the increase of POC during 2009–2010 may be related to El Niño. To investigate the relationship between POC and El Niño, we introduced the phase shift time between the POC and the Multivariate ENSO Index (MEI) to characterize the response of POC to El Niño, and calculated the correlation between the time series of the MEI and POC anomalies during El Niño to determine the phase shift time. The results of cross-correlation analysis indicated that changes of POC were delayed from the onset of El Niño by 4 months. Furthermore, a detailed analysis was presented based on a range of in situ observations and satellite-derived datasets available during the 2009–2010 El Niño. The correlation coefficients between POC and chlorophyll, as well as between POC and SST, are 0.9263 and −0.8315, respectively, and the constructed model indicated that chlorophyll and SST had a significant impact on POC. The spatial distribution of POC anomaly maximum occurred in the spring of 2010, because in the winter of 2009, the Rossby-type anomalies were reflected as upwelling Kelvin wave. However, the vertical POC anomaly maximum occurred in the spring of 2011, because the 2009–2010 El Niño experienced a fast phase transition from warm El Niño to cold La Niña, which was associated with oceanic upwelling.

Statistical assessment of uncertainty and performance of multiple satellite-based precipitation products in capturing extreme precipitation events across Punjab Province, Pakistan

ABSTRACT The changing climate poses a significant danger to both environment and humans. Precipitation stands out as a critical factor for impacting hydrology, ecology, agriculture, and vegetation as well as crucial for maintaining the atmospheric equilibrium. This research examined four satellite-based precipitation products spanning from 2010 to 2018, focusing on assessing the uncertainty associated with these products in Punjab Province, Pakistan. Additionally, a comparative analysis of multi-satellite-derived precipitation data was conducted for the region. Various evaluation metrics, including CC, RMSE, Bias, rBias, and POD, were employed to gauge the performance of these datasets throughout the study area. Our research yielded the following findings: (1) datasets CHIRP and SM2RAIN exhibited the capability of capturing temporal changes observed in precipitation throughout the study area. (2) All satellite-derived datasets displayed superior performance on a monthly basis compared to daily timeframes. (3) On a seasonal scale, CHIRP and SM2RAIN exhibited superior precipitation detection capabilities compared to PERSIANN-CCS and PERSIANN-CDR. (4) CHIRP and SM2RAIN exhibited superior performance across all seasons when compared to PERSIANN-CCS and PERSIANN-CDR. Nonetheless, all products exhibited reasonably accurate detection of light to moderate precipitation events. This study serves to establish a foundation for effective monitoring, mitigation, and decision-making processes.

Estimating global annual gross primary production based on satellite-derived phenology and maximal carbon uptake capacity

The high uncertainty regarding global gross primary production (GPP) remains unresolved. This study explored the relationships between phenology, physiology, and annual GPP to provide viable alternatives for accurate estimation. A statistical model of integrated phenology and physiology (SMIPP) was developed using GPP data from 145 FLUXNET sites to estimate the annual GPP for various vegetation types. By employing the SMIPP model driven by satellite-derived datasets of the global carbon uptake period (CUP) and maximal carbon uptake capacity (GPPmax), the global annual GPP was estimated for the period from 2001 to 2018. The results demonstrated that the SMIPP model accurately predicted annual GPP, with relative root mean square error values ranging from 11.20 to 19.29% for forest types and 20.49–35.71% for non-forest types. However, wetlands, shrublands, and evergreen forests exhibited relatively low accuracies. The average, trend, and interannual variation of global GPP during 2001–2018 were 132.6 Pg C yr−1, 0.25 Pg C yr−2, and 1.57 Pg C yr−1, respectively. They were within the ranges estimated in other global GPP products. Sensitivity analysis revealed that GPPmax had comparable effects to CUP in high-latitude regions but significantly greater impacts at the global scale, with sensitivity coefficients of 0.85 ± 0.23 for GPPmax and 0.46 ± 0.28 for CUP. This study provides a simple and practical method for estimating global annual GPP and highlights the influence of GPPmax and CUP on global-scale annual GPP.

Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin

Surface gauge measurements have been commonly employed to analyze the precipitation's high spatial and temporal variability. However, incomplete area coverage and deficiencies over most tropical and complex topography mean significant limitations of this measurement type. Satellite-derived datasets, combined with the integration of in-situ observations with satellite data, are an alternative to address these limitations by offering a more spatially homogeneous and temporally comprehensive coverage for scarce data areas of the globe. Nevertheless, applying a bias correction technique on the precipitation datasets is still necessary before they are used for research due to their considerable bias. Here, we analyze the performance of CHIRPS, WorldClim, and TerraClimate datasets compared to data from 30 rain gauge stations over the South-West of Colombia, specifically in the Upper Cauca River Basin-UCRB between 1981 and 2018. Additionally, we applied the Quantile Mapping correction to all gridded precipitation products, and subsequently, the corrected rainfall is compared to the observed data on the monthly, seasonal, and annual scale. Our results show that the CHIRPS dataset better captures the seasonal and monthly variability. CHIRPS presents the best performance during less rainy seasons and at low elevation zones (900–2000 m above sea level-m.a.s.l.), followed by TerraClimate. Utilizing the bias correction methodology, we generated a new, corrected, and more reliable monthly precipitation time series for each location from all gridded precipitation products. Additionally, we found that the correction of the CHIRPS dataset presented the best performance across all spatiotemporal scales in the UCRB. Therefore, this study provides an accurate precipitation database for a complex topographic tropical region with limited data availability.

Linking marsh sustainability to event-based sedimentary processes: Impulsive river floods initiated lateral erosion of deltaic marshes

Salt marshes providing valuable services in buffering flooding risks are regarded as cost-effective coastal defense solutions. Given that eroding marsh cliffs will threaten the sustainability of these protective functions, there is a need for mechanistic understanding of cliff formation. Based on short-term field measurements together with satellite-derived dataset and a morphodynamic model, we determined the response of marsh lateral dynamics to event-based sedimentary processes in the Yellow River Delta, China. It was found that the Eastern Marsh with a cliff of ∼1.2 m in height significantly differed from the gentle Western Marsh in accretion pattern and lateral dynamics. Attributed to an artificial flood lasting ∼20 days in 2022, the Eastern Marsh platform experienced average vertical accretion of 23.3 mm, ∼5 times higher than the Western Marsh barely impacted by fluvial sediment supply. Although the accretion pattern ensured the overall vertical adaptability, Eastern Marsh has translated from a phase of rapid expansion to lateral retreat of ∼60 m/yr since 2018, whereas Western Marsh was relatively stable in recent decade. With a validated delta-marsh model, we showed that the observed disparities were attributed to different marsh edge morphodynamic responses to river flood sediment supplies. When Spartina alterniflora initially colonized, the Eastern Marsh platform was lower and the trapping efficiency could rise by ∼3 times than the present. Integrating with ten-fold increases in sediment loads, it was estimated that the large artificial floods in 2018 could contribute to a vertical growth approximating 30 cm at Eastern Marsh. Such episodic but rapid marsh accretion played a crucial role in initiating the development of marsh cliff. Driven by positive feedbacks between platform elevations and cumulative wave thrust, a series of event-based marsh sedimentation will promote long-term marsh loss caused by lateral retreat at sediment-starved systems. This study provides insights in predicting marsh sustainability and long-term evolutions under episodic sedimentary events.

Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites

A study was carried out to investigate the effects of wildfires on lake water quality using a source dataset of 2024 lakes worldwide, covering different lake types and ecological settings. Satellite-derived datasets (Lakes_cci and Fire_cci) were used and a Source Pathway Receptor approach applied which was conceptually represented by fires (burned area) as a source, precipitation/drought representing transport dynamics, and lakes as the ultimate receptor. This identified 106 lakes worldwide that are likely prone to be impacted by wildfires via a terrestrial pathway. Satellite-derived chlorophyll-a (Chl-a) and turbidity variables were used as indicators to detect changes in lake water quality potentially induced by wildfires over a four-year period. The lakes with the largest catchment areas burned and characterized by regular annual fires were located in Africa. Evidence for a strong influence of wildfires was not found across the dataset examined, although clearer responses were seen for some individual lakes. However, among the hydro-morphological characteristics examined, lake depth was found to be significant in determining Chl-a concentration peaks which were higher in shallow and lower in deep lakes. Lake turbidity responses indicated a dependence on lake catchment and weather conditions. While wildfires are likely to contribute to the nutrient load of lakes as found in previous studies, it is possible that in many cases it is not a dominant pressure and that its manifestation as a signal in lake Chl-a or turbidity values depends to a large part on lake typology and catchment characteristics. Assessment of lake water quality changes six months after a fire showed that Chl-a concentrations either increased, decreased, or showed no changes in a similar number of lakes, indicating that a lake specific ecological and hydro-morphological context is important for understanding lake responses to wildfires.

Cooler Arctic surface temperatures simulated by climate models are closer to satellite-based data than the ERA5 reanalysis

Many climate models simulate near-surface air temperatures that are too low in the Arctic compared to the observation-based ERA5 reanalysis data, a bias that was noted in the Inter-governmental Panel on Climate Change Sixth Assessment Report (IPCC AR6). Here we present a high-resolution, satellite-derived dataset of near-surface air temperatures for the Arctic sea-ice region (1982–2020). We use it as a benchmark to reevaluate climate reanalyses and model simulations in CMIP6 (Coupled Model Intercomparison Project 6). We find that the CMIP6 simulations in the central Arctic, with generally thicker ice and snow, align well with satellite observations, with an annual mean bias of less than ± 1 °C over sea ice. By contrast, climate reanalyses like ERA5 exhibit widespread warm biases exceeding 2 °C in the same region. We conclude that reliance on ERA5 reanalysis as a reference may have led to an underestimation of climate model reliability in the Arctic region.

Spatio-temporal trend analysis of air pollutants during COVID-19 over Korba district, Chhattisgarh (India) using Google Earth Engine

Air pollution is a significant public health concern and impacts a country’s well-being. The COVID-19 pandemic and subsequent lockdowns have provided researchers with a unique opportunity to examine the impact of human activities and industries on air pollution. The annual mean PM2.5 grids were validated by comparing them with collocated surface in situ PM2.5 measurements and were then used for trend analyses. Using ground in-situ data validated in 2020–21 showed a good correlation (R2 = 0.90, 0.81 & 0.99) with Satellite-derived for summer, monsoon, and winter seasons, respectively. Among these pollutants, human and industrial activities are a significant source of Sulfur dioxide (SO2), Nitrogen dioxide (NO2), Formaldehyde (HCHO), and Ozone (O3). This study examined how these pollutants changed between February 2019 to January 2022 in Korba District, Chhattisgarh (India). The Google Earth Engine (GEE) is based on the cloud platform time series from the Sentinel-5P TROPOMI satellite data sets. The findings show that the annual average concentration of (SO2: R2 = 0.90, 0.97, and 0.97), (NO2: R2 = 0.97, 0.94, and 0.94), (HCHO: R2 = 0.99, 0.99, and 0.99), and (O3: R2 = 0.99, 0.94, and 0.94) and also increased monsoon, summer, and winter in ascending order for 2019–20, 2020–21, and 2021–22 respectively, due to topology, meteorology, and anthropogenic activities. These results are essential guidelines for future industrial, transportation, and traffic management as sustainable development legislation and programs. Additionally, by applying the COVID-19 scenario, it is now feasible to research how industrial activities, transportation, and biomass burning are, compounded by the region’s topography, affecting air quality using free satellite-derived data sets. These findings also benefit future policy choices on dealing with viral proliferation and reducing its negative societal and economic repercussions.

A novel approach for improving the spatiotemporal distribution modeling of marine benthic species by coupling a new GIS procedure with machine learning

In this study we developed and validated a new integrative method for constructing and assessing Species Distribution Models (SDMs) for marine benthic species across spatial and temporal scales. This methodology synthesizes four dimensions-latitude, longitude, depth and time-into a unified predictive output, achieved by integrating a novel GIS tool, Bathymetric Projection (BP) with machine learning algorithms. BP can extract multi-dimensional data from remote sensing and satellite-derived datasets across the oceanic water column, including latitude, longitude, depth, and time. This data is then mapped onto the ocean floor using a reference bathymetric layer. The resulting seafloor layer is a predictive variable within the SDMs for benthic species. The model output quantifies the probability of species occurrence, or habitat suitability, as determined by environmental conditions specific to the ocean floor across the four dimensions. We validated this approach using two benthic species from the Colombian Pacific Ocean: the White shrimp (Litopenaeus occidentalis) and the Coliflor shrimp (Solenocera agassizii). The model's performance ranged from 87% to 98%, as indicated by 30-fold cross-validation on test datasets. Depth ranges and monthly seasonal patterns were accurately predicted when compared to independent literature data, confirming the model's capability to generate precise spatiotemporal distributions for benthic species. This research underscores the necessity of incorporating depth and time into SDMs, given their critical interactions with latitude and longitude in determining species distribution. The methodology offers a valuable tool for informing decision-making processes in fisheries and environmental management by enhancing the predictive accuracy of spatiotemporal distributions for benthic species, thereby facilitating more holistic interpretations of habitat suitability.

The persistent urbanising effect of refugee camps: evidence from Tanzania, 1985–2015

ABSTRACT With the rise of forced displacement, attention has turned to the economic impact of refugees. However, few studies investigate long-term impacts. We use data for Tanzania for the period 1985–2015 to examine the effect of camps on urbanisation and local development, exploiting a unique satellite-derived dataset of high spatial resolution and temporal frequency. We show a modest but significant effect of refugee camps on built-up area up to a 100 km distance. We then match camp locations to regional gross domestic product, local consumption spending and employment patterns. Output in areas with camps grew at a faster rate during camp operation, but closure of camps was associated with change in economic activity. Activity induced by camps is largely in non-tradeable goods and services rather than inducing longer run structural transformation.

Evaluation of a regional crop model implementation for sub-national yield assessments in Kenya

CONTEXTCropping system models can be used to both assess regional food security and to monitor and predict agricultural drought. Agriculture in Kenya is extremely important to both the economy and food security of the country. OBJECTIVEThis study evaluated a regional implementation of a widely used crop model, the Decision Support System for Agrotechnology Transfer (DSSAT), within a coupled modeling framework, the Regional Hydrologic Extremes Assessment System (RHEAS), over Kenya. The goal of this study was to assess the ability of RHEAS to simulate the annual variability of maize yields at the county level and evaluate the uncertainty inherent in the model and inputs. METHODSThe RHEAS system implements a stochastic ensemble approach to account for field scale variabilities in crop management practices and underlying soil and weather conditions. Satellite-derived datasets were used to evaluate the land surface component of the system and seasonally disaggregated yield for 5 years was used to assess the performance of the cropping system model. RESULTS AND CONCLUSIONSThe median correlation between RHEAS and satellite-derived soil moisture and evapotranspiration estimates were 0.78, and 0.51, respectively, indicating that the model is able to capture the key drivers of the hydrological budget. Overall, RHEAS simulated yearly yield variations with a median correlation of 0.7 with reported yields, with the best performance in the short rains season. However, across both seasons, the RHEAS model was positively biased on the order of ∼1.6 MT/ha. The overall median unbiased RMSE was 0.66 MT/ha. The RHEAS system shows skill at simulating extreme departures in anomalies, and a majority of the time (62.5%) the reported yields fall within the interquartile range of the simulations. SIGNIFICANCEOne of the most important areas of improvement for the next generation of agricultural data and models is to better understand and communicate the inherent uncertainties. This is especially critical in data-limited regions. Here we present a modeling system and its implementation that begins to address these concerns. We demonstrate the ability to simulate broad trends in yields at the county level for sub-annual yields with skills that commensurate previous national/annual level studies.

Global inequities in population exposure to urban greenspaces increased amidst tree and nontree vegetation cover expansion

Cities worldwide are expanding greenspaces, including parks, urban forests, and grasslands. However, population exposure to tree and nontree vegetation is less well understood. Here, we use multiple satellite-derived datasets on vegetation, climate, and socioeconomic factors to examine the variations of urban tree and nontree vegetation cover, drivers of changes, and the impacts on global urban population exposure between 2000 to 2020. We find that approximately 90% of cities experienced an ascending trend in tree cover, and 49% of cities showed growth in nontree vegetation (p < 0.05). The increase in urban tree cover is most evident in high-latitude areas, such as Eastern Russia and Northern Europe. Human exposure to urban tree vegetation considerably declined in the Global South, widening the disparity between the North and South. Our study underscores persistent inequalities in human exposure to green space in cities and calls for evidence-based strategies that reduce increasing disparities.

Four decades of lake surface temperature in the Northwest Territories, Canada, using a lake-specific satellite-derived dataset

Study regionThis study analyzed lake surface temperature (LST) trends and spatial distribution across 535 predominantly small to medium lakes across the North Slave Region (NSR) of the Northwest Territories (NWT), Canada. Study focusThe NWT is characterized by a vast number of lakes covering a significant portion of its spatial extent. However, there is limited knowledge of how LST responds to climate warming in this region. To address this, LST was analyzed in four distinct periods: open water season (OW), ice cover season (IC), and the transitional months of May (TM) and October (TO). LSTs from 1984 to 2021 were retrieved from a lake-specific satellite-derived LST dataset (North Slave LST). LST trend distribution and relationships were analyzed using the Mann-Kendall test and a multilinear regression model. New hydrological insightsThe analysis revealed an overall increase in LST, with average rates (max) of 0.03 °C/year (0.05 °C/year), 0.03 °C/year (0.06 °C/year), and 0.13 °C/year (0.27 °C/year) for OW, TM, and TO, respectively accross study lakes. A faster rate of change was observed in October compared to other periods. Results indicated significant increases in LST for 411 lakes (77%) during OW, 418 lakes (78%) during TO, and 490 lakes (92%) during TM. The spatial distribution and magnitude of LST change were primarily influenced by geographical than morphometric properties. The analysis demonstrated later freeze-up (0.20 day/year) and earlier break-up (−0.17 day/year) of lake ice across the NSR.

Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (&lt;30 years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at a spatial resolution of ~1 km. In our dataset, the Great Lakes are represented by ~33,000 unstructured grid points and the LSTM training incorporated the information from each grid point. The LSTM was trained with seven meteorological variables from reanalysis data as feature variables and the LST from a historical satellite-derived dataset as the target variable. The LSTM was able to capture the spatial heterogeneity of LST in the Great Lakes well and exhibited high correlation (≥0.92) and low bias (limited to ±1.5 °C) for the temporal evolution of LST during the training (1995–2020) and testing (1979–1994) periods.

Estimation of groundwater level and storage changes using innovative trend analysis (ITA), GRACE data, and google earth engine (GEE)

Groundwater is the supreme source of fresh water and the third-largest water resource around the world. But in recent decades, groundwater-related issues in Manbhum Singhbhum Plateau have been significantly increasing day by day. In this plateau region, overuse of groundwater and inappropriate irrigation has greatly harmed the ecosystem, triggering salinity, increasing contamination of groundwater. Therefore, it is essential to estimate the magnitude of groundwater storage changes and water table fluctuation in both pre and post-monsoon seasons in this plateau fringe. The principal objectives of this study are i) to investigate groundwater level changes in the last two decades using observed water level data and ii) to estimate groundwater storage changes using satellite-derived datasets in a hard rock terrain region of India. The Innovative Trend Analysis (ITA) technique has been applied for its better visual interpretation and advantage in serial correlation than the traditional Mann-Kendell test. Gravity Recovery and Climate Experiment (GRACE) gridded data have been processed to obtain a time series trend of mean Equivalent Water Thickness (EWT) and soil moisture has been estimated in Google Earth Engine (GEE). Therefore, soil moisture is subtracted from terrestrial water storage (TWS) to estimate the changes in groundwater storage (GWS) in this plateau fringe. ITA result indicates that approximately 40% and 67.5% of monitoring sites had seriously dropping tendencies of groundwater table in pre and post-monsoon seasons respectively. Simultaneously, maximum groundwater storage (9.484 cm) occurred in Jul 2004 (wet months) whereas a minimum of −30.21 in March 2016 (dry months). Overall results indicate massive decline in groundwater table and groundwater storage. The main reasons for these alarming challenges are geological structure and excessive extraction of groundwater for drinking and irrigation purposes. These research findings will help to further investigations and perform crucial well-organized management of water resources in this part of Manbhum-Singhbhum Plateau.

Introducing ICEDAP: An ‘Iterative Coastal Embayment Delineation and Analysis Process’ with Applications for the Management of Coastal Change

Coastal embayments provide vital benefits to both nature and humans alike in the form of ecosystem services, access to waterways, and general aesthetic appeal. These coastal interfaces are therefore often subject to human development and modifications, with estuarine embayments especially likely to have been anthropogenically altered. Frequent alterations include damming to eliminate tidal influx, backfilling to create new land, and development for the sake of economic gain, which may cause profound damage to local habitats. By providing a record of transitions in surface waters over time, satellite imagery is essential to monitoring these coastal changes, especially on regional to global scales. However, prior work has not provided a straightforward way to use these satellite-derived datasets to specifically delineate embayed waters, limiting researchers’ ability to focus their analyses on this ecologically and economically important subset of coastal waters. Here, we created ICEDAP, a geometry-based ArcGIS toolbox to automatically delineate coastal embayments and quantify coastal surface water change. We then applied ICEDAP to the coast of South Korea, and found that coastal habitat change was particularly profound within embayed regions identified using an 8 km epsilon convexity setting (denoting a moderate distance from the coast and degree of enclosure by surrounding land areas). In the mapped coastal embayments, more than 1400 km2 of coastal habitats were lost during the past 38 years, primarily due to human modification such as large-scale land reclamation projects and the construction of impoundments. Our results suggest that anthropogenic alterations have resulted in the widespread loss of more than USD 70 million of valuable coastal ecosystem services. Together, ICEDAP provides a new innovative tool for both coastal scientists and managers to automatically identify hotspots of coastal change over large spatial and temporal scales in an epoch where anthropogenic and climate-driven changes commonly threaten the stability of coastal habitats.

A Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products

Estimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance for the application of satellite-based SM data throughout many Earth Science/Environmental Engineering disciplines. Here, we introduce the Bayesian inference approach to analyze the error characteristics of widely used passive and active microwave satellite-derived SM data sets, at different overpass times, acquired from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) missions. In particular, we apply Bayesian hierarchical modeling (BHM) and triple collocation analysis (TCA) to investigate the relative importance of different environmental factors and human activities on the accuracy of satellite-based data.To start, we compare the BHM-based sensitivity analysis method to the classic multiple regression models using a frequentist approach, which includes complete pooling and no-pooling models that have been widely used for sensitivity analysis in the field of remote sensing and demonstrate the BHM's adaptability and great potential for providing insight into sensitivity analysis that can be used by various remote sensing research communities.Next, we conduct an uncertainty analysis on BHM's model parameters using a full range of uncertainties to assess the association of various environmental factors with the accuracy of satellite-derived SM data. We focus on investigating human-induced error sources such as disturbed surface soil layers caused by irrigation activities on microwave satellite systems, naturally introduced error sources such as vegetation and soil organic matter, and errors related to the disregard of SM retrieval algorithmic assumptions - such as the thermal equilibrium passive microwave systems. Based on the BHM-based sensitivity analysis, we find that assessments of SM data quality with a single variable should be avoided, since numerous other factors simultaneously influence their quality. As such, this provides a useful framework for applying Bayesian theory to the investigation of the error characteristics of satellite-based SM data and other time-varying geophysical variables.

Simulated seasonal oceanographic changes and their implication for the small pelagic fisheries in the Java Sea, Indonesia

Small pelagic fisheries in the Java Sea (JS) contributes to about 26.6% of the total marine fisheries resources, where their spatial-temporal variation is controlled by seasonal oceanographic changes. This study aims to investigate a relationship between seasonal reversal circulation and number of light-fishing vessels (VBD) dispersion that capture small pelagic fishes, using multi-datasets from a regional ocean circulation model, satellite-derived datasets, and pelagic fish landing datasets between 2010 and 2020. The model demonstrates that main axis of eastward (westward) monsoon current that brings warmer and fresher (cooler and saltier) water, confines much closer along the northern Java (southern Kalimantan) during the northwest (southeast) monsoon period. These changes are followed unprecedentedly by southward (northward) shift of VBD and high abundance of euryhaline (stenohaline) fish species. This new evidence implies that reversal monsoon current and surface component of Makassar Throughflow play a significant role on delineating potential small pelagic fishing ground and fish productions.

Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020

Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates are largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields. Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000–2020 across a 12-state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015 to 2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forest model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008–2014. We also evaluated the full 2000–2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000–2020, each standard deviation increase in early-season rainfall delayed planting by ∼2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall or persisting early-season frost risks may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.

An Evaluation of a 1/18o Resolution Regional Ocean Circulation Model of CROCO in the Southern Sunda Shelf

The Coastal and Regional Ocean Community Model (CROCO) was applied to simulate the three dimensional ocean circulation on the southern Sunda Shelf with a horizontal resolution of 1/18o and 40 vertical layers. This configuration is called as CROCO18-SSS. The initial and open boundary conditions were derived from a global reanalysis product at a resolution of 1/12o from the Copernicus Marine Environment Monitoring Service (CMEMS). The regional model was driven by atmospheric, tides, and river discharges data from a wide variety of datasets, each with a different resolution. To test the model performance and capabilities of the CROCO18-SSS model outputs including sea level anomaly, current velocity, temperature, salinity, and volume transport, we have compared in the simulation results with other reanalysis product and satellite-derived datasets. The CROCO18-SSS model outputs reproduced the regional ocean circulation well from observations and reanalysis products. For example, the reversal of the monsoonal current in the southern sunda shelf, as well as strong persistent southward Makassar throughflow and their interaction in the southern Makassar Strait, can be seen clearly. The vertical structure of the zonal current component was comparable to that of the reanalysis product reference. The seasonal shift in temperature and salinity, with the water becoming colder and saltier during the northwest monsoon and warmer and fresher during the southeast monsoon, was also accurately reproduced by the CROCO18-SSS model. Additionally, the mean volume transport calculated by the CROCO18-SSS model for the Makassar and Karimata Straits was comparable to that reported in previous studies.