The question of whether the MODIS enhanced vegetation index (EVI) can be directly applied to other satellite sensors has remained unexplored for nearly three decades since its development. This is important because the EVI's empirical parameters are specifically designed for MODIS. For a long time, users have applied these MODIS-based parameters directly to other sensor data without modification. Therefore, this study systematically explores this issue using Landsat-8/9 data. We introduce a novel methodological framework: using NDVI (parameter-free) as a reference benchmark and designing an auxiliary index (EVIb) to isolate the impact of the blue band, as it is introduced in EVI. Results show that, based on the synchronized observations of MODIS and Landsat-8/9 across eleven vegetation test areas, the deviation between MODIS and Landsat-8/9 EVI is, on average, 1.7 times larger than that of their NDVI, with an R 2 deviation reaching 10%. Analysis confirms that the blue band is not the primary cause of the deviation; instead, the discrepancy arises from the inapplicability of the MODIS-specific parameters to Landsat-8/9 data. Sensitivity analysis reveals strong interactions among the four parameters of EVI, with the soil adjustment factor (L) being the most sensitive. Based on these findings, the study proposes and tests two parameter optimization strategies for Landsat-8/9 EVI: a site-specific cross-calibration method that reduces the mean absolute percentage bias (|PB|) between the MODIS and Landsat-8/9 EVI from 13% to nearly 0%, and a generalized parameter optimization using the Nelder-Mead algorithm, which reduces the average |PB| to 8.33%. Cross-validation indicates that the optimized parameters can reduce bias by approximately 32.5% in unseen areas from similar biomes. This study concludes that the direct cross-sensor application of MODIS EVI parameters introduces significant bias. We recommend sensor-specific parameter optimization and provide a transferable methodological framework to guide future efforts towards consistent multi-sensor vegetation monitoring.

Google Earth Engine (GEE) is a cloud-based platform that provides powerful capabilities for remote sensing image compositing, processing, and analysis. Among GEE's diverse applications, temporal aggregation of multiple images is one of its widely used techniques. Nevertheless, a systematic exploration of the advantages and disadvantages of its five primary image compositing methods - minimum (Min), maximum (Max), Median, Mean, and Mode - has not been conducted. It remains unclear whether the commonly used Median method is genuinely universal. Additionally, it is usually unknown which input images and their seasonal origins predominantly contribute to the final composite image. Therefore, this study systematically explores the performance of the five compositing methods across four regions in China and Uganda, with different geographic locations and climatic conditions, by examining their strengths, weaknesses, and applicable scenarios. A novel quantitative metric, contribution percentage (CP), is developed to identify which input images and bands in a time series primarily contribute to the final composite image. The results show that the commonly used Median metric is not always the optimal choice. The Min and Mode methods perform better than the Median method in haze and cloudy regions, particularly in haze removal and vegetation monitoring. The Max method also exhibits superior performance in compositing thermal infrared and near-infrared images compared to the Median. Based on this study, the applicable scenarios for the five compositing methods have been clarified. Furthermore, the proposed CP metric effectively reveals the input images and bands that contribute principally to the final composite. Understanding these insights is crucial for users to choose appropriate GEE compositing methods and study plant phenology and surface thermal environments, thereby providing a foundation for the scientific application of GEE compositing methods.

As global urbanization accelerates and ecological challenges intensify, effective monitoring and assessing ecological conditions have become critical for sustainable development. Remote sensing technologies play an increasingly crucial role in this context. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1), a next-generation remote sensing satellite, provides 10-m spatial resolution and multispectral imaging capabilities, offering new opportunities for ecological monitoring. This study explores the ecological potential of SDGSAT-1 data, focusing on the comprehensive assessment of urban heat islands (UHI), urban vegetation coverage, and regional ecological conditions. This is achieved through a detailed comparison with the widely-used Landsat-8/9 data. The study develops several methodologies for cloud detection, atmospheric correction, and land dryness retrieval. Validation shows that the cloud removal effect achieved by the proposed SDGSAT Cloud Mask (SCM) algorithm is comparable to, or slightly better than, those of the CFMask algorithm for Landsat-9 and the machine learning-based S2cloudless algorithm for Sentinel-2A, with F1 scores greater than 0.92. The results show that the monitoring of regional ecological conditions by SDGSAT-1 is very similar to that of Landsat-8/9, with differences generally under 5 %. Because SDGSAT-1's multispectral and thermal infrared imagery has higher spatial resolution than Landsat-8/9, it can detect 5.6 % more vegetation area and 2.6 times larger high-temperature areas within urban environments than Landsat data. SDGSAT-1's finer resolution enables more detailed ecological assessments, supporting urban sustainability applications. However, due to the lack of shortwave infrared bands in the SDGSAT-1 imagery, it is less effective than Landsat-8/9 in interpreting land surface dryness and moisture content.

Urbanization and global warming have led to more frequent extreme heat events, highlighting the importance of Park Cooling Islands. This study analyzes the cooling effect (PCE) of 50 urban parks in Fuzhou to explore the relationship between park area and cooling effect. The results indicate that there is no simple positive correlation between park area and cooling effect. Specifically, while larger parks may have greater cooling potential, a larger area does not necessarily lead to better cooling effects. The optimal park area for cooling effect ranges from 0.594 to 56 hm2; beyond this range, an increase in park area does not significantly enhance the cooling effect. A low proportion of impervious surfaces, a high proportion of water bodies and vegetation, as well as complex patch patterns can enhance PCE, while excessive edge density and landscape fragmentation can weaken PCE. Based on importance analysis, the external morphological characteristics and internal patch characteristics of parks significantly influence cooling effects. Furthermore, the cooling effect of parks is jointly determined by internal and external conditions, with internal conditions having a more significant impact. Therefore, merely pursuing a “large” park area does not guarantee a “good” cooling effect; instead, greater emphasis should be placed on optimizing park design and layout, simplifying boundary shapes, reducing impervious surface ratios, and increasing vegetation diversity to maximize cooling effects.

Gray-green spaces play a crucial role in shaping local and regional temperatures. Numerous studies have explored the relationships between the components of gray-green spaces and their spatial distribution characteristics in relation to the thermal environment. However, research combining path-interaction and non-linear analysis on these relationships remains relatively scarce. This study focused on 28 coastal cities in China, specifically within the provinces of Zhejiang, Fujian, and Guangdong, and the special administrative regions of Hong Kong and Macao. By combining the partial least squares structural equation model (PLS-SEM) with the machine learning algorithm boosted regression trees (BRT), we explored the relationships between multidimensional gray-green space features and multiple temperature metrics across urban-suburban gradients. Our findings revealed the following: (1) The linear path influences of gray space features on temperature progressively weaken from urban cores to fringes, while green space features exhibit gradually strengthening path influences. (2) The thermal effects of most gray-green space features demonstrate non-monotonic characteristics. Particularly, three-dimensional (3D) building and green space features exhibit differentiated convex and concave marginal effect response patterns with distinct inflection points across urban core-fringe gradients for various temperature metrics. (3) In urban cores, 3D building features exert multi-tiered moderating effects on air temperature by shaping wind speed. These findings deepen our understanding of how the fundamental gray-green space features and their interactions affect the thermal environment, offering valuable insights for developing more effective heat mitigation strategies.

The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus making them invaluable for monit-oring forest health.To compare the performance of these two satellites'red-edge bands in monitoring forest health,this study selected forests in Liuyang City,Hunan Province and Tonggu County,Jiangxi Province and Hanzhong City,Shaanxi Province in China as study areas and used three commonly used red-edge indices and the Random Forest(RF)algorithm for the comparison.The three selected red-edge indices were the Normalized Difference Red-Edge Index 1(NDRE1),the Missouri emergency resource information system Ter-restrial Chlorophyll Index(MTCI),and the Inverted Red-Edge Chlorophyll Index(IRECI).Through training of sample regions,this study determined the spectral differences among three forest health levels and established classification criteria for these levels.The res-ults showed that GF-6 imagery provided higher accuracy in distinguishing forest health levels than Sentinel-2A,with an average accur-acy of 90.22%versus 76.55%.This difference is attributed to variations in the wavelengths used to construct the red-edge indices between GF-6 and Sentinel-2A.In the RF algorithm,this study employed three distinct band combinations for classification:all bands including red-edge bands,excluding red-edge bands,and only red-edge bands.The results indicated that GF-6 outperformed Sentinel-2A when using the first and second band combinations,yet slightly underperforming with the third.This outcome was closely associ-ated with the importance of each band's contribution to classification accuracy reveled by the Gini importance score,their sensitivity in detecting forest health conditions,and the total number of bands employed in the classification process.Overall,the NDRE1 derived from GF-6 achieved the highest average accuracy(90.22%).This study provides a scientific basis for selecting appropriate remote sens-ing data and techniques for forest health monitoring,which is of significant importance for the future ecological protection of forests.

The recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites' SR data, with an average absolute percent change (PC) of 1.88% and an average R2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24 degrees C lower than Landsat-8 LST across the five test areas but can be 0.58 degrees C lower in built-up land-dominated areas and 0.42 degrees C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

Forest fires pose a significant threat to ecosystems, biodiversity, and human settlements, necessitating accurate and timely detection of burned areas for post-fire management. This study focused on the immediate assessment of a recent major forest fire that occurred on March 15, 2024, in southwestern China. We comprehensively utilized high temporal resolution MODIS and Black Marble nighttime light images to monitor the fire’s development and introduced a novel method for detecting burned forest areas using a new Shadow-Enhanced Vegetation Index (SEVI) coupling with a machine learning technique. The SEVI effectively enhances the vegetation index (VI) values on shaded slopes and hence reduces the VI disparity between shaded and sunlit areas, which is critical for accurately extracting fire scars in such terrain. While SEVI primarily identifies burned forest areas, the Random Forest (RF) technique detects all burned areas, including both forested and non-forested regions. Consequently, the total burned area of the Yajiang forest fire was estimated at 23,588 ha, with the burned forest area covering 19,266 ha. The combination of SEVI and RF algorithms provided a comprehensive and efficient tool for identifying burned areas. Additionally, our study employed the Remote Sensing-based Ecological Index (RSEI) to assess the ecological impact of the fire on the region, uncovering an immediate 15 % decline in regional ecological conditions following the fire. The usage of RSEI has the potential to quantitatively understand ecological responses to the fire. The findings achieved in this study underscore the significance of precise fire-burned area extraction techniques for enhancing forest fire management and ecosystem recovery strategies, while also highlighting the broader ecological implications of such events.

The Russia-Ukraine conflict has persisted for over a year, posing challenges in assessing and verifying the extent of damage through on-site investigations. Nighttime light (NTL) remote sensing, an emerging approach for studying regional conflicts, can complement traditional methods. This study employs NASA's Black Marble products to reveal the response characteristics of NTL intensity at national and state scales during the first anniversary of the conflict (January 2022 to February 2023) in Ukraine. The study used the nighttime light ratio index (NLRI) to assess the relative intensity of NTL and month-on-month change rate (MoM), nighttime light change rate index (NLCRI), and the rate (R value) of linear regression analysis to depict spatiotemporal dynamics. In addition, Theil-Sen median trend analysis and Mann-Kendall tests were employed to analyze intensity trends, with a “dual-threshold method” to reduce extensive noise interference. The results showed: At the national scale, the conflict resulted in an 84.0% decrease in NTL across Ukraine. At the state scale, the most severe NTL decline occurred near the southwestern border and eastern conflict zone under Ukrainian government control, witnessing over 80% decline rates. The correlation of decreases in NLCRI and R values with population displacement, infrastructure damage, or curfew measures demonstrated that the concentration of refugees and electricity facility restoration led to increased NLCRI and R values. Overall, NTL reflects critical moments at the national scale and provides insights into military intentions and humanitarian measures at the state scale. Therefore, NTL can effectively serve as a tool for observation and assessment in military conflicts.

Numerous researches have been conducted on the impacts of urban thermal environmental factors on land surface temperature (LST) variations. However, few have comprehensive studied on their diurnal variations. This study explored whether the novel ECOSTRESS LST data can create fresh opportunities for analyzing the diurnal urban thermal environment. Here we utilized ECOSTRESS to explore the relative contribution, marginal effects, and granularity effects of 2D/3D urban factors on diurnal LSTs in a "furnace city" Fuzhou. Our results revealed that: (1) Anthropogenic heat flux and urban blue and green spaces have the greatest influence on nighttime LST and daytime LST, respectively. (2) The impacts of urban factors on diurnal LSTs are nonlinear. The marginal effect curve can reveal the crucial value ranges and turning points of the factors that affect diurnal LSTs. (3) The overall impact of the 2D factors on diurnal LSTs is more significant than that of the 3D factors. Meanwhile, 3D factors are indispensable elements in urban thermal environment research. They serve as an important supplementary to 2D factors in the vertical space of cities. Our findings can identify crucial factors influencing urban thermal environment, providing innovative insights for the development of strategies to mitigate urban heat island effects.

Urbanization has disrupted the energy balance of natural surfaces, leading to the formation and intensification of urban heat island (UHI) phenomenon. Current research on the relationship between surface energy balance (SEB) and UHI mainly focuses on either a macro perspective of large-scale regions or a micro perspective based on numerical microclimate simulations. However, there are still relatively few studies conducted on the local scale of cities. Here, we investigated the relationships between seasonal SEB and land surface temperature (LST) based on local climate zones (LCZ) and explored the impact of three-dimensional (3D) urban morphology on SEB. Our results revealed that: (1) urban building spaces have relatively higher LST, sensible heat flux, and storage heat flux, but lower net radiation and latent heat flux, compared to urban blue and green spaces; (2) among LCZ built types, the compact and large low-rise buildings have high LST and heat-inducing energy fluxes, while the compact and open high-rise buildings exhibit the opposite situation; (3) the component proportions of SEB fluxes exhibit a more significant linear correlation with UHI intensity compared to the relative differences between SEB fluxes; and (4) 3D building morphology has a higher relative importance in influencing the variations of surface energy balance ratio (SEBR) components than 3D vegetation morphology. Specifically, building height has the greatest impact on the seasonal variations of SEBR components, while the 3D urban greening ratio has relatively high importance among vegetation morphology. These findings enable us to better consider local UHI effect from the perspective of SEB.

The red-edge band is closely related to biochemical parameters that characterize the growth condition of green plants and is an important factor in monitoring vegetation health. Therefore, red-edge indices based on the red-edge band have been developed to measure vegetation health. However, due to the limited availability of satellites with a red-edge band, most existing red-edge indices were not developed based on satellite data. Fortunately, the launch of the GaoFen-6 (GF-6) satellite provides favorable conditions for monitoring vegetation health using satellite imagery, as it has two red-edge bands with a spatial resolution of 16 m. To investigate the effectiveness of the red-edge bands on the GF-6 satellite in monitoring forest health, this study selected six red-edge indices and conducted tests in Zhangjiajie region in Hunan Province, China and Hetian Basin in Fujian Province, China. The selected indices are the normalized difference red-edge index 1 (NDRE1), the modified chlorophyll absorption ratio index 2, the red-edge chlorophyll (CIred-edge), the inverted red-edge chlorophyll index, the red-edge position, and the Missouri emergency resource information system terrestrial chlorophyll index. The results showed that when applied to NDRE1 and CIred-edge, the red-edge bands of GF-6 can effectively distinguish forest health conditions, with a discrimination accuracy of 92.3% and 92.5%, respectively. However, the performance of the GF-6 red-edge bands with the other four indices yielded accuracy generally lower than 70%. Overall, the two red-edge bands added to the GF-6 satellite contribute to discerning forest health conditions, with NDRE1 and CIred-edge being the preferred red-edge indices.

In recent years, China has launched a number of Gaofen (GF) series earth observation satellites. It is crucial to understand the relationship between the data of the GF series of satellite sensors for the selection of sensor images for scientific research. Taking the same-day transit image pairs of three regions as the study data, this paper compares the consistency of the Top of Atmosphere (TOA) reflectance of GF-1 WFV4 and GF-6 WFV sensors by using the TOA mean comparison method, and discusses the differences in water body and vegetation extraction. The results indicate that the satellite signal intensity in different land cover types and areas differs significantly. In the bare soil-dominated region, GF-1 WFV4 has a larger signal strength than GF-6 WFV, while in the vegetation-dominated region, it turns out to be just the opposite. The difference between the two sensors is mainly related to the difference in the spectral response function and the radiometric resolution of the two satellites. In addition, the determination coefficient (R2) of the corresponding bands of the two sensors is all greater than 0.90, indicating that the two sensors have strong linear correlation and good complementarity.

Predicting the spatiotemporal dynamics of land cover and its carbon stock holds significant importance in guiding regional sustainable development, enhancing regional carbon stocks, and addressing global climate change. However, there is insufficient research on the quantitative relationships between various land cover types and carbon stock changes, as well as their future spatial predictions. Focusing on the core area of Fuzhou City, China, this study constructs a streamlined framework by coupling deep learning and the InVEST model to predict urban land cover and carbon stock changes in 2025 and 2035. The results show that: (1) The prediction model for land cover change has high applicability and can produce the simulated images with high accuracy. Impervious surface is expected to increase by 53 km(2) in 2025 and 131 km(2) in 2035 compared to 2020, resulting in considerable reductions in forest and cropland. (2) Carbon stocks of the study area are expected to decrease by 1.68 x 10(6)t in 2035 compared to 2020 due to large amounts of high-carbon-density forests and croplands being converted into low-carbon-density impervious surfaces. (3) Multiple regression analysis reveals that forests have the largest impact on carbon stocks in the area, with a magnitude 5.25 times greater than impervious surfaces and 11.5 times greater than cropland, whereas impervious surfaces are the second most influential land cover type on carbon stock changes. Therefore, expanding forest areas becomes an essential initiative as forests could offset the carbon stock loss caused by impervious surface growth. This study provides scientific references for optimizing land-use planning and formulating policies for the development of low-carbon cities.

Xiong’an New Area was established as a state-level new area in 2017 and serves as a typical representative area for studying the ecological evolution of rural areas under rapid urbanization in China. Remote sensing-based ecological index (RSEI) is a regional eco-environmental quality (EEQ) assessment index. Many studies have employed RSEI to achieve rapid, objective, and effective quantitative assessment of the spatio-temporal changes of regional EEQ. However, research that combines RSEI with machine learning algorithms to conduct multi-scenario simulation of EEQ is still relatively scarce. Therefore, this study assessed and simulated EEQ changes in Xiong’an and revealed that: (1) The large-scale construction has led to an overall decline in EEQ, with the RSEI decreasing from 0.648 in 2014 to 0.599 in 2021. (2) Through the multi-scenario simulation, the non-unidirectional evolution of RSEI during the process of urban-rural construction has been revealed, specifically characterized by a significant decline followed by a slight recovery. (3) The marginal effects of urban-rural construction features for simulated RSEI demonstrate an inverted “U-shaped” curve in the relationship between urbanization and EEQ. This indicates that urbanization and EEQ may not be absolute zero-sum. These findings can provide scientific insights for maintaining and improving the regional EEQ in urban-rural construction.

The ecological quality of a region is significantly influenced by its geographical conditions, which can yield different effects on ecosystems. Nevertheless, the lack of adequate technology has impeded quantitative investigations into these differences. Consequently, there is an increasing demand for effective techniques to quantitatively measure differences in ecological quality resulting from variations in geographical conditions. This study applied the novel Remote Sensing-based Ecological Index (RSEI) concurrently to two distinct provincial-level regions in China, Fujian and Ningxia, to quantitatively detect their ecological differences. These two regions possess contrasting geographical conditions, with Fujian having high forest coverage and abundant rainfall, while Ningxia features low forest coverage and extensive loess plateau and desert terrain. By linking geographical factors with their corresponding ecological responses, we conducted a comprehensive analysis to determine whether the contrasting geographical conditions between the two regions had caused significant disparities in their ecological status. The results indicate that the contrasting geographical conditions have indeed led to marked ecological differences, with Fujian exhibiting excellent ecological status, while Ningxia lags behind due to unfavorable geographical conditions. In terms of RSEI scores, Fujian consistently achieved higher RSEI values (>0.8) in the study years, reaching an excellent ecological level, whereas Ningxia recorded scores lower than 0.45 during the comparable years, corresponding to a poor to moderate ecological level. Regarding the impact of geographical factors on ecological conditions, the positive contributions of greenness and wetness indicators to the ecology in Fujian were significantly greater than those in Ningxia (58% vs. 39%), whereas the contributions of negative indicators, dryness and hotness, were notably higher in Ningxia compared to Fujian (|–61|% vs. |–42|%). The successful concurrent application of RSEI to these two geographically distant regions also demonstrates the robustness of the RSEI technique.

The spatiotemporal non-stationary relationships between 2D/3D urban features and land surface temperature (LST) introduce uncertainty to the quantitative exploration between them. This study focused on the urban building spaces of "furnace city " Fuzhou and explored the quantitative relationships between urban features and ECOSTRESS diurnal LSTs from a block perspective. Our results revealed that: (1) Compared to the ordinary least squares regression model, the multi-scale geographically weighted regression model can better capture the spatiotemporal non-stationary relationships. (2) Largest patch index of building patches (LPI_B) and building height (BH) have the greatest impact on the variations in daytime and nighttime LSTs, respectively. The interaction between largest patch index of vegetation patches (LPI_V) and LPI_B has the largest enhancing effect on daytime LST, while that between BH and LPI_B enhances nighttime LST the most. (3) The diversification of architectural morphology highlights the equal importance of both 2D and 3D building features in influencing LST variations. Meanwhile, the standardization of urban greening emphasizes the greater significance of 2D vegetation features compared to 3D. (4) Based on varying spatial characteristics, differentiated urban renewal schemes should be adopted. These findings can deepen our understanding of spatiotemporal non-stationarity, which cannot be ignored in urban thermal environment research.

One important mission of the newly launched Landsat-9 is to collaborate with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires a high level of consistency between the two satellite data. Previous cross-calibrations between Landsat-8 Operational Land Imager (OLI) and Landsat-9 OLI2 have been performed via band-by-band approaches, and the spectral deviation revealed between the two sensors was within 1%. However, it remains uncertain whether this deviation will persist when multiple bands are combined to perform certain calculations and whether the offset found in each band will be amplified in the combined multiband performance. Therefore, a comprehensive cross-comparison using a multiband combination approach is necessary to ensure agreement between the two sensor data. This study conducted a multiband combination-based cross-comparison using simultaneous underfly data of both sensors along with a Tasseled Cap Transform (TCT) performance. The coefficients of Landsat-8 were utilized to calculate the three components of Landsat-9 TCT. The calculation of the TCT involves six bands of both OLI sensors, which enables a more thorough examination of the consistency of the two sensor data. In addition, calculating Landsat-9 TCT directly using Landsat-8 coefficients allows further investigation of the similarity between the two sensors by determining whether Landsat-8 TCT coefficients are suitable for Landsat-9 TCT. Both top of atmosphere (TOA) reflectance data and surface reflectance (SR) data were employed for this comparison. Apart from whole image pair-based comparisons, land cover category-based comparisons were also performed. The results show that the three TCT components of Landsat-9, calculated using Landsat-8 coefficients, are similar to those of Landsat-8, with an average R2 of 0.983 and RMSEs on the order of 0.009 in most scenarios. The deviations observed in the TCT components between the two sensors are primarily due to the higher radiometric resolution of Landsat-9 (14 bits) compared to Landsat-8 (12 bits), as the deviations occurring in bright and dark areas are larger than those in other areas. Besides, the uncertainty in the green band for vegetative targets and the uncertainty revealed in typical vegetative surfaces also contributed to the higher divergence in the greenness component. The 1% difference detected in band-by-band cross-calibrations increased by 0.2–0.5 percentage points in this comprehensive multiband comparison. Overall, this cross-comparison study generates confidence that the Landsat-8 OLI and Landsat-9 OLI2 data are in strong agreement even when performing multiband combination operations. This demonstrates that the synergistic use of the two sensor data can well maintain the continuity of Landsat Earth observations.

Objectives: The nonlinear remote sensing ecological index（nRSEI） is a recently proposed ecological index, which used the kernel principal component analysis（kPCA） algorithm to integrate four indicators of the existing remote sensing ecological index（RSEI） rather than using the traditional principal component analysis（PCA） technique. The main reason for using kPCA was that in the Beijing area the correlations between wetness, greenness, dryness, and heat that are four indicators used in RSEI were generally weak, so the kPCA that is specially developed to deal with variables with nonlinear relationship was needed to handle these four non-linear indicators. This paper aims to examine the correlation strength of these four indicators in the Beijing area to see whether their relationship is strong or weak and analyzes the effectiveness of the accuracy assessment method that was used for the validation of the new index. Methods:Through examining correlation coefficients and scatter diagrams, the correlation between the four indicators is investigated to find out whether the relationship between the indicators is linear or non-linear. Also, the effectiveness of the validation for the new index is analyzed. Results: The results show that the four indicators are strongly linearly correlated with each other, therefore, the kPCA was not suitable for the intergradation of the four indicators. The methods used for the accuracy assessment of the new index also have obvious defects and thus failed to effectively validate the accuracy of the new index. In addition, some important issues related to prepare remote sensing papers are also discussed. These include the feasibility of remote sensing modeling, the robustness of the model, the scale consistency of the sub-indicators in the model, the validation method for the model, the selection of reference images for the validation, and the required sample size for accuracy validation. Conclusions: The nRSEI is not a suitable index for the assessment of regional ecological status as it mistakenly employed kPCA to intergrade the four indicators that are linearly related.

Landsat Collection 2 Level-2 Surface Temperature (LC2L2ST) was formally released in December 2020 by the U. S. Geological Survey (USGS). However, there are few reports on this new land surface temperature (LST) product. As this product will be the only LST data provided by the USGS starting in 2022, it is necessary to evaluate the product timely. Among various satellite LST products, the quality of the MODIS LST product is well recognized, and widely used. Therefore, this paper, for the first time, performed a cross-comparison between the new Landsat LST product and the MODIS LST product to examine the quality of the new product. Different regions in China (Fuzhou, Taihu, Yinchuan and Dunhuang) were selected as the test areas, and 20 pairs of LC2L2ST and MODIS LST synchronous images were used for the comparison. The images cover different land types, such as vegetation, water, town and deserts across different seasons. A total of 560 homogeneous regions of interest (ROI) were selected from the images of the test areas. The regression analysis was carried out to examine the fit of the ROIs and the quantitative relationship between the two LST products. The conversion model between them was also developed. The results showed that the new LC2L2ST product is highly correlated with the MODIS LST product. Each of the four test areas can achieve a coefficient of determination (R-2) greater than 0. 98. Integrating the 560 samples from the four test areas also obtain an R-2 close to 0. 98. Nevertheless, differences between the two products have also been founded. The LC2L2ST is 0. 90 degrees C averagely higher than the MODIS LST (RMSE = 2. 29 degrees C). However, LC2L2ST can be slightly lower than MODIS LST in late fall and winter seasons but significantly higher than extremely hot summer seasons with a bias close to 7 degrees C. The analysis revealed that the differences were related to spatial resolution, sensor viewing angles, land cover types and seasons. In general, the new LC2L2ST product strongly correlates with the MODIS LST, but significant differences were also observed in the summer months. Therefore, the new Landsat LST product must be further tested with in-situ measured LST data. Due to the differences in this paper, the two LST data products need to be converted when they must be collaboratively used. This study developed the conversion equation between the two LSTs based on the 560 ROIs. The verification found that the differences between the two data after conversion were greatly reduced. It is conducive to the cooperative use of the two LST data and providing continuous remote sensing data for long-term LST monitoring.