Surface Temperature Products Research Articles

Offset of MODIS land surface temperatures from in situ air temperatures in the upper Kaskawulsh Glacier region (St. Elias Mountains) indicates near-surface temperature inversions

Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where air temperature measurements from weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have been found in prior studies to show an offset from available weather station measurements, the source of which is unknown. Here, we show that the MODIS offset likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that an offset in remote sensing temperatures is present not only in MODIS LST products but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the offset was most pronounced in the winter (mean offset >8 ∘C) and least pronounced in the spring and summer (mean offset <2 ∘C). We also find this enhanced seasonal offset in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS LST offset to be consistent in magnitude and seasonal distribution with modeled temperature inversions and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68∘N, 139.78∘ W; 2,603 m a.s.l) and Eclipse Icefield (60.84∘ N, 139.84∘ W; 3017 m a.s.l.). Although these results do not preclude errors in the MODIS sensor or LST algorithm, they demonstrate that efforts to convert MODIS LSTs to an air temperature measurement should focus on understanding near-surface physical processes. In the absence of a conversion from surface to air temperature based on physical principles, we apply a statistical conversion, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.

A novel TIR-derived three-source energy balance model for estimating daily latent heat flux in mainland China using an all-weather land surface temperature product

A reliable thermal-derived method for spatially and temporally continuous latent heat flux (LE) estimates is vital for agricultural water resource management. In this study, we proposed a novel three-source LE model (TSLEM) derived by all-weather thermal infrared (TIR) land surface temperature (LST) to generate all-sky daily LE in mainland China. In this approach, LE was partitioned into the energy fluxes from canopy transpiration, soil evaporation, and interception water evaporation, respectively. Importantly, a new strategy was used to decompose radiational temperature into soil temperature (Ts), canopy temperature (Tc) and interception water temperature (Ti). Then soil evaporation was estimated by Penman-Monteith (PM) equation that parameterizes a soil resistance as a function of the normalized difference temperature index (NDTI) derived from Ts. A simplified MOD16 algorithm framework was used to estimate canopy transpiration and a Priestley-Taylor (PT) model to estimate interception evaporation. The proposed method was validated using 26 eddy covariance (EC) tower sites in mainland China across various vegetation types and applied to generate spatial continuous daily LE in mainland China. The results show that TSLEM accurately yielded daily LE with an average coefficient of determination (R2) of 0.53 (p<0.01) and root-mean-square-error (RMSE) of 27.37 W/m2, indicating TSLEM is a promising method for generating daily LE using all-weather LST at the regional scale.

Assessment of the Paris urban heat island in ERA5 and offline SURFEX-TEB (v8.1) simulations using the METEOSAT land surface temperature product

Abstract. Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This urgently demands for accurate urban climate projections to help organizations and individuals to make climate-smart decisions. However, most of the large ensembles of global and regional climate model simulations do not include sophisticated urban parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation reanalysis from the European Centre for Medium-Range Weather Forecasts) and in a simulation with the SURFEX (Surface Externalisée) land surface model employing the widely used bulk bare rock approach. The city of Paris is considered as a case study. Subsequently, we apply a more complex urban scheme – SURFEX coupled to the Town Energy Balance (TEB) urban canopy model to assess its benefits on characterizing the Paris urban climate. Both simulations and ERA5 were compared to the LSA SAF (Satellite Application Facility on Land Surface Analysis) land surface temperature product to evaluate the simulation of Parisian surface urban heat island (SUHI). Our results show a significant added value of SURFEX-TEB in reproducing the SUHI during the daytime and the UHI during both the daytime and nighttime (with overall reductions in the bias and root mean square error and improvements in the representation of the statistics of the SUHI/UHI displayed by the Perkins skill score or S score). The improvement in the simulated SUHI is lower during the nighttime due to the lack of land–atmosphere feedbacks in the proposed offline framework. Nonetheless, the offline SURFEX-TEB framework applied here clearly demonstrates the added value of using more comprehensive parameterization schemes to simulate the urban climate and, therefore, allowing the improvement of urban climate projections.

Seasonal and diurnal surface urban heat islands in China: an investigation of driving factors with three-dimensional urban morphological parameters

ABSTRACT Surface urban heat island (SUHI) can considerably influence the urban environment and the quality of life. It is vital to examine how underlying surface properties impact seasonal and diurnal SUHIs. However, the influence of three-dimensional (3D) urban morphological parameters (UMPs) on SUHIs has not been thoroughly studied under varying climatic settings. To fill this knowledge gap, the present study investigated seasonal and diurnal changes in SUHI intensities (ΔT) in 208 cities in China from 2014 to 2016 using moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products. In addition, the influence of potential factors in urban surface energy balance, including two-dimensional (2D) and 3D UMPs, socio-economic indices, urban greening, and surface albedo, on seasonal and diurnal ΔT were assessed under different climatic settings and with different city sizes using the method of Geographically Weighted Regression (GWR). Results show that negative summer daytime ΔT was observed in some cities under dry climates. Generally, in summer, the ΔT during daytime was higher than at nighttime. The 3D UMPs (i.e. building height and volume) yielded more decisive influences on ΔT than 2D UMP (i.e. building coverage). This is particularly true for the summer diurnal cycle and under dry climatic settings. Building height was found to be negatively correlated with surface temperatures, while building volume was positively correlated. Additionally, the 3D UMPs yielded more influences on winter ΔT than summer ΔT. The capability of vegetation to regulate ΔT was more potent in dry climates than in wet climates and in small cities than in large cities. Varying climates and city sizes can modify the significance of the 2D and 3D UMPs on the urban surface energy balance, suggesting that urban thermal mitigation should consider climate background and population size.

Towards a combined Landsat-8 and Sentinel-2 for 10-m land surface temperature products: The Google Earth Engine monthly Ten-ST-GEE system

Efforts to combine satellite images from different sources are particularly needed in Land Surface Temperature-based (LST) studies. This research proposes for the first time, to our knowledge, a Google Earth Engine-based (GEE) 10-m LST system, named Ten-ST-GEE. It is based on both Landsat-8 and Sentinel-2 bands. Ten-ST-GEE has the ability to automatically transform 30-m to 10-m LST at Landsat-8 overpass time. Machine learning and regression methods (i.e., OLS, RLS, DisTrad, RF, and SVM) are embedded within this system. Ten-ST-GEE was applied over two agricultural lands and two urban regions in the United States of America and in Lebanon. OLS and RLS showed an RMSE of ∼1.1 °C compared to ∼2.4 °C for DisTrad and ∼2.5 °C for RF and SVM. The open-source and automated Ten-ST-GEE can generate information at the building-level and within the agricultural parcels. It has the potential to be portable to any region across the Globe, aiming at better management of environmental resources.

Adequacy of the GK-2A AMI Land Surface Temperature Product According to Geographic Factors and Compared with Other Satellite Products (MODIS and S-VIRRS)

In this study, we evaluated the land surface temperatures (LSTs) retrieved from the GEO-KOMPSAT-2A (GK-2A) satellite AMI sensor by comparing them to LSTs measured at 106 ground-based stations (95 ASOS and 11 AAOS stations). In addition, the statistical performance of the GK-2A AMI LST was compared with those of LSTs retrieved from the MODIS and S-VISSR satellites. LSTs were evaluated in terms of inland versus coastal regions and at different inland elevations. A time series analysis of the entire study period confirmed that the GK-2A satellite yielded the smallest bias among four satellite-based LST products. The satellite-based LSTs also exhibited higher correlations with ground-based measurements in the inland region than in the coastal region. In addition, the difference in LSTs at the highest and lowest elevations was approximately 1 ℃. A comparison of the annual mean LST and observation frequency revealed that geostationary satellite-based LSTs tend to be overestimated because of the lower observation frequency (1/3 of the polar orbit satellite frequency). Overall, the GK-2A AMI LSTs can be used to analyze the spatiotemporal variations in the urban heat island effect and heat waves at fine-scale resolutions, while further topographical correction is recommended to improve their accuracy.

Can Satellite-Based Thermal Anomalies Be Indicative of Heatwaves? An Investigation for MODIS Land Surface Temperatures in the Mediterranean Region

In recent years, an exceptional number of record-shattering temperature extremes have been observed, resulting in significant societal and environmental impacts. The Mediterranean region is particularly thermally vulnerable, frequently suffering from intense and severe heatwaves. Using daily temperature observations from 58 weather stations (NOAA Global Historical Climatology Network daily database) in the Mediterranean area, past heatwave episodes were initially detected. A daily LST time series was developed using Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra &amp; Aqua satellites) for a 19-year period (2002–2020) at the station locations. LST anomalies were identified using percentile-based indices. It was found that remotely sensed-based LST presents the potential for understanding and monitoring heatwave events, as surface thermal anomalies were generally indicative of heatwaves. Approximately 42% (39%) of heatwave days during daytime (nighttime) coincided with LST anomalies; conversely, 51% of daytime LST anomalies overlapped with the exact days of a heatwave (38% at night). Importantly, the degree of association was significantly higher for extremely hot days (up to an 80% match) and long-lasting heatwaves (up to an 85% match). Rising trends in frequency and duration were observed for both heatwaves and LST anomalies. The results advance the understanding of surface-atmosphere coupling during extreme temperature days and reflect the suitability of thermal remote sensing in heatwave preparedness strategies.

Quantifying the influence of 2D and 3D urban morphology on the thermal environment across climatic zones

Urban heat islands (UHIs) exert a substantially negative impact on human health and urban sustainability. The role of two-dimensional (2D) landscape patterns in UHIs are well documented; while the effects and contributions of three-dimensional (3D) urban structures remain unclear, especially across different climatic zones. Here we investigated the relationship between 2D/3D urban morphology and the urban thermal environment in summer and winter during the day and at night in 62 representative large cities across four major climate zones in China. First, we extracted the seasonal surface regional heat island intensity (SRHII) using the MODIS 8-Day land surface temperature product. Subsequently, we constructed 25 2D and five 3D urban features and explored their relative importance and respective roles in UHIs in different climatic contexts. Results show that: (1) significant differences (p < 0.05) exist in SRHII between various climate zones; cities with a humid subtropical climate experience temperatures approximately 2 °C higher during the day in summer compared to those with the other climate types. (2) 3D urban features can effectively improve the interpretation of urban features for SRHII, with an average optimization level of 21%. (3) Urban trees have a higher cooling effect than other green spaces, whereas tall buildings can also reduce the UHI effect. (4) On summer days, equal proportions of tree to building volume provide the greatest cooling effects. This study provides new insights into the effect of 3D urban characteristics on SRHII and has promising implications for climate resilience planning and heat-related risk management

Thermal Environment Effects of Built-Up Land Expansion in Shijiazhuang

Exploring the thermal environment effects of built-up land expansion can lay a firm foundation for urban planning and design. This study revealed the spatiotemporal dynamic characteristics of built-up land and heat island center points in Shijiazhuang using land-use/land-cover data and land surface temperature (LST) products from 1996 to 2019, and the response mechanism between the percentage of built-up land (PLAND) and LST with the grid sampling method and statistical analysis. Results indicated that heat islands are mainly clustered in the downtown, built-up areas of counties and the Hutuo River Basin. The spatiotemporal shift direction of the center point of the urban heat island (UHI) and built-up land in the whole study area varied due to the eco-environmental transformation of the Hutuo River Basin. In areas far from the Hutuo River Basin, the center points of UHI and built-up land were shifted in a similar direction. There is a remarkable linear correlation between the PLAND and LST, the correlation coefficient of which was higher than 0.7 during the study period. Areas with PLAND &gt; 60% are urban regions with stronger heat island effects, and areas with PLAND &lt; 55% are villages and towns where the temperature raised more slowly.

Spatial portability of random forest models to estimate site-specific air temperature for prediction of emergence dates of the Asian Corn Borer in North Korea

The air temperature estimates derived from satellite data products would facilitate site-specific prediction of pest outbreaks. Here we propose an approach to estimate air temperature with spatial portability using both land surface temperature (LST) and atmospheric profile (AP) products based on MODIS satellites. In the approach, quantitative and qualitative variables were used as inputs to assess and improve the spatial portability of random forest (RF) models. Daily temperature data at sites in the Korean peninsula were collected from a weather database operated by the Korean Meteorological Administration. Sets of input variables were defined to represent temperature data from the LST (LT) and AP (AT) products, geographical properties (GE), data quality and cloud conditions (QC), land cover type (LC), and auxiliary properties (AX), respectively. The combinations of these sets were used as inputs to the RF models, which were cross-validated using satellite data and weather observation data in South Korea from 2013 to 2019. Concordance Correlation Coefficient (CCC) and Spatial Portability Index (SPI) were determined to compare the accuracy of the RF models across the sites of interest. The RF model that uses AT, LT, and QC variable sets as inputs (RFALQ) tended to have high values of CCC among test sites in South Korea, which ranged from 0.91 to 0.99. RFALQ also had higher values of SPI at sites in North Korea than the other models in which either AT or LT was excluded. Furthermore, the emergence date of Asian Corn Borer (Ostrinia furnacalis) was predicted with high values of CCC (0.94–0.97) across the study sites when RFALQ was used to prepare the input data to an insect phenology model. Our results suggest that both LST and AP products would contribute to higher spatial portability of air temperature estimation. This also hints that the temperature estimation model with high spatial portability would facilitate the pest models, which can be used to reduce the risk of food insecurity in developing countries with sparse weather station networks.

Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data

By characterizing high-frequency surface thermal dynamics at a medium spatial scale, hourly land surface temperatures (LST), retrieved from geostationary satellite thermal infrared (TIR) observations, shows great potential to be used across a range of scientific applications; however, cloud cover typically leads to data gaps and degraded retrieval accuracy in TIR LST products, such as the official Advanced Baseline Imager (ABI) LST product. Studies have focused on the LST gap reconstruction; however, most interpolation-based methods only work for a short-term cloud duration and are unable to adequately compensate for cloud effects, and traditional surface energy balance (SEB)-based methods are able to handle cloud coverage while they are not feasible for use at night. Moreover, few studies have concentrated on recovering the abnormal retrievals of partial cloud pixels. In this study, an all-sky diurnal, hourly LST estimation method based on SEB theory was proposed; the proposed method involved three major steps: 1) an original spatiotemporal dynamic model of LST was constructed from ECMWF Reanalysis v.5 (ERA5); 2) clear-sky ABI LST was then assimilated to the dynamic model to generate a continuous LST series; 3) the diurnal cloud effects were superimposed on cloudy time estimated by an innovative optimization method from satellite radiation products. A 2-km, all-sky, hourly LST product was produced over the contiguous US and Mexico from July 2017 to June 2021. Validation was conducted using ground measurements at 18 sites from Surface Radiation (SURFRAD) and core AmeriFlux networks, and produced an overall root-mean-square error (RMSE) of 2.44 K, a bias of −0.19 K, and an R2 of 0.97 based on 408,300 samples. For clear-sky samples, the RMSE values were 2.37 and 2.24 K for day and nighttime, respectively, which was a notable improvement over the corresponding values of the official ABI LST product (2.73 and 2.86 K, respectively). The RMSE values on cloudy-sky were 2.78 and 2.23 K for day and nighttime, respectively. The daily mean LST by aggregating all-sky, hourly LST had an RMSE of 1.13 K and R2 of 0.99. Overall, this product showed reliability under consistent cloud durations, although it was slightly affected by surface elevation. The diurnal temperature cycle climatology of major land cover types was also characterized. The product is freely available at: http://glass.umd.edu/allsky_LST/ABI/.

MAPPING PERMAFROST DISTRIBUTION IN THE PARVATI VALLEY, KULLU USING LANDSAT 8 DERIVED LAND SURFACE TEMPERATURE

Abstract. Permafrost covers much of the Parvati valley, but mapping its distribution is difficult due to the scarcity of ground observed data sets and the region's high spatial heterogeneity. We show permafrost distribution maps for the Parvati valley from 2013 to 2021 based on the Mean Annual Air Temperature. We created maps of Mean Annual Air Temperatures (MAAT) using Landsat 8 Land Surface Temperature (LST) products in Google Earth Engine. Google Earth Engine (GEE) is a cloud-based platform that was utilised to quickly and efficiently obtain the spatial and temporal variations of permafrost distribution. Permafrost is defined as ground that maintains a temperature below zero degrees Celsius for at least two consecutive years. To justify the definition, we examined Mean Biennial Air Temperature (MBAT), Mean Triennial Air Temperature (MTAT), Mean Quadrennial Air Temperature (MQAT), Mean Quinquennial Air Temperature (MQnAT), Mean Septennial Air Temperature (MSpAT), Mean Octennial Air Temperature (MOAT), and Mean Novennial Air Temperature (MNOAT) (MNAT). Our findings demonstrate that the percentage of Permafrost distribution in the Parvati valley is about the same in all situations, accounting for 22 percent of the overall study area excluding glaciers. Our maps were divided into four categories: Continuous Permafrost Zone, Discontinuous Permafrost Zone, Sporadic Permafrost Zone, and No Permafrost Zone.

VEGETATION COVER MAPPING FROM RGB WEBCAM TIME SERIES FOR LAND SURFACE EMISSIVITY RETRIEVAL IN HIGH MOUNTAIN AREAS

Abstract. Land Surface Temperature (LST) products from thermal infrared imaging rely on information about the spatial distribution of Land Surface Emissivity (LSE). For portable, broadband thermal cameras for drone- or ground-based measurements with camera to object distances up to a few kilometres and with meter-scale resolution, threshold-based retrieval of LSE from Fractional green Vegetation Cover (FVC) can be used. As seasonal changes in vegetation LSE over the year cannot be accounted for by single satellite images or aerial orthophotos, this study evaluates an approach for FVC retrieval via permanently installed RGB webcams and derived Excess Green vegetation index (ExG) time series at a high-mountain test site in the European Alps. Daily ExG values were derived from the imagery of 27 days between 12/07/2021 and 30/10/2021 and projected to a 0.5 m Digital Surface Model (DSM). FVC reference data from 765 in-situ vegetation plots were used to assess the relationship between ExG and the vegetation cover and to determine the thresholds of ExG for no vegetation cover and full vegetation cover. Despite the bad correlation between ExG and in-field FVC with an R² score of 0.15, an approach using a well-tested orthophoto-retrieved NDVI for FVC retrieval performs just slightly better. The comparison of the remotely sensed data and the field measurements therefore remains complex. Time series analysis of both ExG and FVC for highly vegetated areas showed a significant decrease from summer to autumn, which reflects the seasonal changes of LSE for senescent vegetation. Calculated emissivities for vegetated pixels ranged from the minimum of 0.95 to the maximum of 0.985 over the season, while emissivity values for less vegetated pixels stayed constant during the season. The results of this study will be used as input to a correction model for remote LST measurements in the context of micro-scale investigations of the thermal niche of Alpine flora.

An Integrated Framework for Mapping Nationwide Daily Temperature in China

Air temperature (Ta) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially continuous coverage. Nowadays, land surface temperature (Ts) measurements can be obtained from some satellite sensors (e.g., MODIS), further enabling us to estimate Ta in view of the relationship between Ta and Ts. In this article, we proposed a two-phase integrated framework to estimate daily mean Ta nationwide. In the first phase, multivariate linear regression models were fitted between site-based observations of daily mean air temperature (Ta-mean) and MODIS land surface temperature products (including Terra day: TMOD-day, Terra night: TMOD-night, Aqua day: TMYD-day, and Aqua night: TMYD-night) conditional on some covariates of environmental factors. The fitted models were then used to predict Ta-mean from those covariates at unobserved locations. The predicted Ta-mean were looked on as stochastic variables, and their distributions were also obtained. In the second phase, Bayesian maximum entropy (BME) methods were used to produce spatially continuous maps of Ta-mean taking the meteorological station observations as hard data and the predicted Ta-mean in the first phase as soft data. It is shown that the proposed approach is promising to improve the interpolation accuracy significantly, comprehensively considering the prior knowledge and the context of space variability and correlation, which will enable it to compile spatially continuous air temperature products with higher accuracy.

Validation and Improvement of COCTS/HY-1C Sea Surface Temperature Products.

In oceanographic study, satellite-based sea surface temperature (SST) retrieval has always been the focus of researchers. This paper investigates several multi-channel SST retrieval algorithms for the thermal infrared band, and evaluates the accuracy of the COCTS/HY-1C SST products. NEAR-GOOS in situ SST data are utilized for validation and improvement, and a three-step matching procedure including geographic location screening, cloud masking, and homogeneity check is conducted to match in situ SST data with satellite SST data. Two improvement schemes, including nonlinear regression and regularization iteration, are proposed to improve the accuracy of the COCTS/HY-1C SST products and the typical application scenarios and the algorithm characteristics of these two schemes are discussed. The standard deviation of residual between retrieved SST and measured SST for these two data improvement algorithms, which are considered as the main indexes for assessment, result in an improvement of 13.245% and 14.096%, respectively. In addition, the generalization ability of the SST models under two data improvement methods is quantitatively compared, and the factors affecting the model accuracy are also carefully evaluated, including the in situ data acquisition method and measurement time (day/night). Finally, future works about SST retrieval with COCTS/HY-1C satellite data are summarized.

Comparing Coarse-Resolution Land Surface Temperature Products over Western Australia

Satellite-derived land surface temperature (LST) has commonly been used to monitor global temperature changes. The MODIS MYD11A2 product is the most common coarse-resolution product used for this purpose. An updated MODIS product (MYD21A2) and new VIIRS (VNP21A2) product have also recently become available. This study analyses eight-day, quality-controlled, LST imagery over Western Australia (WA) for the three products for an urban and a non-urban area for the years 2013, 2016, and 2019. An analysis of the data indicates that (i) the averaged daytime LST difference between the three products for Perth city over the three years was 1.32 °C, while at night it was 0.89 °C; (ii) the averaged daytime difference over the Kimberley region was 7.02 °C with a night average difference of 2.39 °C; and (iii) both the MYD21A2 and VNP21A2 products still appear to record anomalous monthly LST values, particularly in the humid Kimberley monsoonal months. The overall objective of the National Aeronautics and Space Administration (NASA) is to ensure that the LST values of the two satellite system products are comparable, so evidence of LST value differences will require further investigation, especially if the older product is to be replaced by the newer systems.

Exploring South African Pacific oyster mariculture potential through combined Earth observation and bioenergetics modelling

The combined use of satellite-derived environmental data and a dynamic energy budget (DEB) model to determine Pacific oyster growth potential was adapted for the South African marine environment. Study areas consisted of the West Coast (high-chlorophyll, low temperature) and the South Coast (variable chlorophyll, higher temperature) ecoregions. Chlorophyll-a and sea surface temperature products from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to simulate yearly growth for 18 years. Average growth performance at the end of the culture period was mapped and compared for prominent sectors. Industry-relevant growth indicators, “days to reach commercial weight” and “optimal culture period length” were also established.High growth potential was found in eastern nearshore sectors of the South Coast, particularly Plettenberg Bay, where oysters reached a median total weight (TW) of 271 g within 9 months. Other sectors in the region yielded Large commercial size within 150 days. However, South Coast growth hotspots were found to be highly variable, with transient unfavourable growth conditions leading to low flesh mass relative to TW. Growth potential was favourable in northern sectors of the West Coast, where oysters reached a median TW of up to 148 g in 9 months, Large commercial size within 200 days, and contained high flesh mass relative to TW.Current oyster production sites were not found to be optimal for growth. Higher-growth sites coincide with areas in economic decline or with high levels of poverty. However, due to constraints of applying DEB models over large spatial scales, these results are considered preliminary and await in situ verification, as well as a spatial multi-criteria analysis, before investment and development.

Analysis of 22-year Drought Characteristics in Heilongjiang Province Based on Temperature Vegetation Drought Index.

Heilongjiang Province is the main grain producing region in China and an important part of Northeast China Plain, which is one of the three black soil belts in the world. The cultivated region of black soil accounts for 50.6% of the black soil region in Northeast China. Due to the obvious rise of temperature and uneven distribution of precipitation in the 20th century, it has been considered to be one of the important reasons for agricultural drought and aridity. Under the background of climate change, understanding the multiyear changes and occurrence characteristics of cultivated land drought in different agricultural regions in Heilongjiang Province is of great significance for the establishment of agricultural drought prediction and early warning system in the future, guiding agricultural high-standard farmland irrigation in different regions, promoting black soil protection, and then improving grain yield. This paper calculates the temperature vegetation drought index (TVDI) based on the normalized difference vegetation index (NDVI) and surface temperature (TS) product data of MODIS from 2000 to 2021. Taking TVDI as the drought evaluation index, this paper studies the temporal and spatial variation distribution characteristics and occurrence frequency of drought in the whole region and four agricultural regions of Heilongjiang Province: Daxing an Mountain and Xiaoxing an Mountain (region I), Sanjiang Plain (region II), Zhangguangcai Mountains (region III), and Songnen Plain (region IV). The results show that medium drought generally occurred in Heilongjiang Province from 2000 to 2021, accounting for about 70% of the total cultivated land. The drought was severe from 2000 to 2009 and weakened from 2010 to 2021. In the 110 months of the crop growing season from 2000 to 2021, about 63.84% of the region suffered more than 60 droughts. It is found that the frequency of drought varies from region to region. More than 80 droughts occurred in the west of region IV and the middle of region II. The characteristics of region IV are large sandstorm, less precipitation, and lack of water conservancy facilities, resulting in frequent and strong drought. It is also found that the occurrence frequency, degree grade and regional distribution of drought are closely related to seasonal changes. In spring, the occurrence grade and frequency of drought in region IV are the strongest and the drought phenomenon is serious. In autumn, drought is frequent and distributed in all regions, but the grade is not strong (mainly medium drought), and the drought phenomenon is medium. It is humid in summer. Crops in Heilongjiang Province are one crop per annual. Spring drought seriously restricts the water content of crops. Long-term drought will lead to poor crop development and reduce yield. Therefore, only by clarifying the characteristics of regional time drought, monitoring accurate drought events and accurately predicting the occurrence of drought, can we guide high-standard farmland precision irrigation, improve crop yield and ensure national food security. At the same time, severe drought will affect the terrestrial ecosystem, resulting in the distribution of crops and microorganisms, and the transformation between carbon sink and carbon source.

Adequacy of satellite derived data for streamflow simulation in three Hexi inland river basins, Northwest China

The limited gauge measurements usually constrain the accurate hydrological modeling of runoff, especially in the ungauged or gauge-sparsely areas with complex terrain conditions. Satellite derived data provide alternative forcing data for hydrological modeling in ungauged areas, while quantitative evaluation on their performance is still needed. This study explores the adequacy and accuracy of satellite derived data based on the gauge observations over three inland river basins in the arid Hexi Corridor, Northwest China, where the satellite-based products are adopted to drive the HBV-Light model, including six satellite precipitation datasets (CHIRPS, CMORPH, GPM, GSMaP, MSWEP, PERSIANN) and the widely used evaporation product (GLEAM) combing with MODIS land surface temperature product (MOD11A1). Seven statistical metrics (CC, ME, RMSE, BIAS, POD, FAR, CSI) are adopted to assess satellite-based precipitation estimates, and three indices (NSE, R2, PBIAS) are used to evaluate the hydrological performance. Results indicate that: (1) For precipitation evaluation at point-to-pixel scale, all products have a relatively poor agreement with gauged observations with the CC ranging from 0.16 to 0.56. Both MSWEP and GPM outperformed other products at daily scale with higher CC values, and showed the capability of detecting the precipitation events with higher POD and CSI. PERSIANN, GSMaP, GPM, and CMORPH tend to underestimate the precipitation amount in three river basins. (2) For the hydrological evaluation, the statistical assessment showed that the GPM presents the best results at daily scale, while underestimates streamflow amount in three watersheds. All the grided precipitation datasets show higher NSE and R2 in the relatively humid HHRB and XYRB than SLRB. (3) The combination of MOD11A1, GLEAM and satellite-based precipitation as forcing data of the HBV-Light can achieve good performance in the three inland river basins. This study aims to provide an additional reference for the hydrological application of satellite derived data in gauge-sparsely areas located in high altitude and arid regions.

A stratified environmental reference system for better understanding of the relationship between remote sensing observations and ground monitoring of karst rocky desertification

AbstractQuickly and accurately monitoring karst rocky desertification via remote sensing could assist in its mitigation. Yet this is limited by high environmental heterogeneity that introduces uncertainty into satellite observations' relationships with ground‐based karst rocky desertification. Influenced by variations of environmental factors, the relevance of satellite data for karst rocky desertification is questionable, especially at a large geographic scale. Here a novel stratified degradation reference system was built to describe this complex relationship. The method was applied in Guizhou–Guangxi Karst Mountainous Region, a typical karst region in China undergoing severe karst rocky desertification. A stratified procedure of elevation‐based initial division and sub‐division of lithology and land use was designed using moderate resolution imaging spectroradiometer (MODIS) data. MODIS values of surface vegetation, albedo, and temperature products were standardized by percentiles to reveal their relevance for evaluating each environmental stratification of land degradation. We found a substantial spatial variation of MODIS observations across elevation gradients and different land uses. This was corrected using our proposed reference method, wherein standardized MODIS observations show a nonlinear but clear downward trend with increasing karst rocky desertification intensities. Most areas affected by karst rocky desertification had lower MODIS values. Using the stratified environmental reference approach between MODIS observations and karst rocky desertification can enable the cost‐effective screening of high‐heterogeneity karst areas, to better support monitoring and identify desertification hotspots.