Remote Sensing of Environment最新文献

{"title":"Mitigating the directional retrieval error of solar-induced chlorophyll fluorescence in the red band","authors":"Zhaoying Zhang ,&nbsp;Yongguang Zhang","doi":"10.1016/j.rse.2024.114496","DOIUrl":"10.1016/j.rse.2024.114496","url":null,"abstract":"<div><div>Solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP), but the retrieval of SIF is commonly noisy and highly sensitive to various interference factors. Particularly, the retrieval of SIF in the red band (RSIF) is more challenging than in the far-red SIF (FRSIF) due to the weaker fluorescence signal and the weaker absorption depth of oxygen at the red band compared with the far-red band. A comprehensive evaluation of all factors will allow a reproducible interpretation of SIF signals and advance the estimation of GPP from SIF. Recent studies have assessed the sensitivity of SIF retrieval to sensor characteristics, retrieval methods, and hardware specifications. However, none of these studies have systematically investigated the directional retrieval error of SIF resulting from the mismatch between irradiance measured above the canopy and the true irradiance reaching the canopy components viewed by a sensor. This study illustrated the effect of mismatched irradiance on the retrieval of RSIF using the commonly used standard 3FLD method based on SCOPE model simulations. The retrieval accuracy was highest in the hotspot direction, but it decreased as the observation direction was away from the hotspot. The relative root mean square error (RRMSE) was generally higher than 20 % in the forward directions. To reduce the retrieval error due to the mismatch effect, we proposed a modified 3FLD method (MFLD) by calculating the true irradiance reaching the canopy in a given direction based on geometric optical theory. The results showed that MFLD clearly improved the retrieval accuracy for RSIF, especially in the forward directions where RRMSE decreased by 10 % in most cases. For example, the RRMSE was reduced from 19.26 % to 5.50 % after mitigating the mismatch between the measured and actual solar irradiance, when the solar zenith angle was 40° and viewing zenith angle was 30° in the forward solar principal plane. Even at the nadir observation, the RRMSE was also reduced from 12.84 % to 5.64 %. In summary, MFLD can effectively mitigate the irradiance mismatch effect on the retrieval of RSIF. These results will improve our interpretation of the relationship between GPP and RSIF at different observation directions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114496"},"PeriodicalIF":11.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of runoff variability in transboundary basins over High Mountain Asia: Multi-dataset merging based on satellite gravimetry constraint","authors":"Jiashuang Jiao ,&nbsp;Yuanjin Pan ,&nbsp;Xiaoming Cui ,&nbsp;Hussein A. Mohasseb ,&nbsp;Hao Ding","doi":"10.1016/j.rse.2024.114493","DOIUrl":"10.1016/j.rse.2024.114493","url":null,"abstract":"<div><div>Runoff variability in glacierized transboundary river basins over High Mountain Asia (HMA) directly affects the stability of water supply for more than one billion people in Asia. However, limited by insufficient in-situ gauges and imprecise hydrological model output, it is still a challenge to accurately monitor and comprehensively analyze the HMA runoff change. In this paper, we construct a water budget closure test of water balance equation based on satellite gravimetry constraints to assess the accuracy of hydrological dataset outputs, and propose a multi-dataset merging method to evaluate runoff variability in ten HMA transboundary basins over the past two decades. Results show that the runoff quantified by the hydrological dataset has relatively maximum uncertainty compared to precipitation and evapotranspiration. The performance of the reconstructed terrestrial water storage change (TWSC) from hydrological dataset varies with basins, and the maximum Nash-Sutcliffe Efficiency (NSE) value ranges from 0.31 to 0.94. Nevertheless, the current hydrological dataset struggles to accurately reconstruct the interannual and annual variability of TWSC, with the maximum cyclostationary NSE (NSEc) value ranging from −1.07 to 0.24. Runoff change in HMA exhibits both overall stability and regional climatic condition-related spatial heterogeneity. A significant downstream change-driven increase trend of runoff occurs in Indus Basin (0.2 ± 0.1 mm/mon/yr), while Brahmaputra Basin (−0.5 ± 0.4 mm/mon/yr) and Salween Basin (−0.4 ± 0.2 mm/mon/yr) show significant runoff decrease trends driven by upstream and downstream changes, respectively. Climate change has exacerbated the instability of runoff in the arid basins over northern HMA, leading to evident increase in annual amplitude. Furthermore, negative correlation is found between temperature and runoff at the interannual scale, especially in Ganges Basin (−19.73 ± 12.53 Gt/month per °C) and Mekong Basin (−17.46 ± 9.43 Gt/month per °C). Our multi-dataset merging methodology can improve the reliability of using global hydrological datasets to quantify runoff variability in poorly in-situ gauged regions, and may also be applicable to the evaluation of precipitation and evapotranspiration.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114493"},"PeriodicalIF":11.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the wilderness status of long-distance trails in the United States - Exploring the potential of SDGSAT-1 glimmer imager data","authors":"Liding Wang ,&nbsp;Mingyang Lv ,&nbsp;Changyong Dou ,&nbsp;Yue Cao ,&nbsp;Steve Carver ,&nbsp;Xiancai Lu ,&nbsp;Shaochun Dong ,&nbsp;Siming Deng ,&nbsp;Huadong Guo","doi":"10.1016/j.rse.2024.114499","DOIUrl":"10.1016/j.rse.2024.114499","url":null,"abstract":"<div><div>Long-distance hiking trails worldwide serve as vital ‘threads’ connecting vast wilderness areas, offering unique opportunities to evaluate progress toward the United Nations' Sustainable Development Goals (SDGs). However, their extensive lengths pose challenges for data collection, limiting their potential use in sustainable development research. Remote sensing technologies, such as high-spatial-resolution and color glimmer imager data from SDGSAT-1, hold promise in addressing these challenges. This study focuses on seven prominent U.S. long-distance trails: the Appalachian Trail, Arizona National Scenic Trail, Buckeye Trail, Hayduke Trail, Ice Age National Scenic Trail, Pacific Crest Trail, and Pacific Northwest Trail, along with 20 km buffer zones surrounding each trail. By integrating glimmer and population data, we introduce a method to quantify human populations within these wilderness areas. Anthropogenic indicators, including population density, land use, grazing intensity, and transportation networks, are used to develop a wilderness evaluation methodology, employing an enhanced human footprint index. Our findings offer a comparative assessment of the wilderness conditions across the selected trails, providing insights into varying levels of human impact and identifying areas where conservation efforts are most urgently needed.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114499"},"PeriodicalIF":11.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Himawari-8/AHI land surface reflectance at mid-latitudes using LEO sensors with off-nadir observation","authors":"Beichen Zhang ,&nbsp;Kazuhito Ichii ,&nbsp;Wei Li ,&nbsp;Yuhei Yamamoto ,&nbsp;Wei Yang ,&nbsp;Ram C. Sharma ,&nbsp;Hiroki Yoshioka ,&nbsp;Kenta Obata ,&nbsp;Masayuki Matsuoka ,&nbsp;Tomoaki Miura","doi":"10.1016/j.rse.2024.114491","DOIUrl":"10.1016/j.rse.2024.114491","url":null,"abstract":"<div><div>Land-surface reflectance (LSR) is a basic physical retrieval in terrestrial monitoring. The potential for high-frequency surface product estimation was evident in third-generation Geostationary Earth Orbit (3rd-GEO) satellites, substantially improving spectral, spatial, and temporal resolutions. Intercomparisons with LSR products from Low Earth Orbit (LEO) satellites have been employed as a common way to evaluate the LSRs of GEO satellites. However, in mid-latitude regions, comparing the LSR between two satellites is challenging due to constraints in the sun–target–sensor geometries. In this study, we proposed a method to obtain observations with consistent viewing and illumination conditions aligned with those of the Himawari-8/Advanced Himawari Imager (AHI) at mid-latitudes, by utilizing forward and backward viewing cameras from LEO sensors, such as Terra/Multi-angle Imaging SpectroRadiometer (MISR). The reflectance intercomparison revealed that the estimated AHI LSR closely matched the LSR from MISR in the red and near-infrared (NIR) bands at latitudes higher than 30°N/S during 2018–2019, with correlation coefficient (<em>r</em>) greater than 0.8 and a relative root mean square error (RRMSE) below 25 %. The data accuracy in the NIR bands was higher than in the red band, as indicated by a lower RRMSE. The correlation was also stronger in non-forested regions compared to forested areas, with higher <em>r</em> values. Additionally, screening observation pairs based on the relative azimuth angle (RAA), which assumes rotational symmetry in LSR, was examined and proved effective for GEO–LEO intercomparisons. This RAA-matching criterion enables reflectance intercomparisons across a wide longitude range at mid-latitudes, including areas like mainland China and New Zealand, where ray-matching is not applicable. The reflectance consistency demonstrated by RAA matches was comparable to that of ray matches, although the RAA-matching is constrained by timing due to the solar location. The findings from this study have potential applications for other satellites.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114491"},"PeriodicalIF":11.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Retrieval of high-resolution melting-season albedo and its implications for the Karakoram Anomaly” [Remote Sensing of Environment Volume 315 (2024) 114438]","authors":"Fuming Xie ,&nbsp;Shiyin Liu ,&nbsp;Yu Zhu ,&nbsp;Xinyi Qing ,&nbsp;Shucheng Tan ,&nbsp;Yongpeng Gao ,&nbsp;Miaomiao Qi ,&nbsp;Ying Yi ,&nbsp;Hui Ye ,&nbsp;Muhammad Mannan Afzal ,&nbsp;Xianhe Zhang ,&nbsp;Jun Zhou","doi":"10.1016/j.rse.2024.114474","DOIUrl":"10.1016/j.rse.2024.114474","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114474"},"PeriodicalIF":11.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The EnMAP spaceborne imaging spectroscopy mission: Initial scientific results two years after launch","authors":"Sabine Chabrillat ,&nbsp;Saskia Foerster ,&nbsp;Karl Segl ,&nbsp;Alison Beamish ,&nbsp;Maximilian Brell ,&nbsp;Saeid Asadzadeh ,&nbsp;Robert Milewski ,&nbsp;Kathrin J. Ward ,&nbsp;Arlena Brosinsky ,&nbsp;Katrin Koch ,&nbsp;Daniel Scheffler ,&nbsp;Stephane Guillaso ,&nbsp;Alexander Kokhanovsky ,&nbsp;Sigrid Roessner ,&nbsp;Luis Guanter ,&nbsp;Hermann Kaufmann ,&nbsp;Nicole Pinnel ,&nbsp;Emiliano Carmona ,&nbsp;Tobias Storch ,&nbsp;Tobias Hank ,&nbsp;Sebastian Fischer","doi":"10.1016/j.rse.2024.114379","DOIUrl":"10.1016/j.rse.2024.114379","url":null,"abstract":"<div><div>Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80° N to 80° S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114379"},"PeriodicalIF":11.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142369401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning","authors":"Bart Slagter ,&nbsp;Kurt Fesenmyer ,&nbsp;Matthew Hethcoat ,&nbsp;Ethan Belair ,&nbsp;Peter Ellis ,&nbsp;Fritz Kleinschroth ,&nbsp;Marielos Peña-Claros ,&nbsp;Martin Herold ,&nbsp;Johannes Reiche","doi":"10.1016/j.rse.2024.114380","DOIUrl":"10.1016/j.rse.2024.114380","url":null,"abstract":"<div><div>Road development has affected many remote tropical forests around the world and has accelerated human-induced deforestation, forest degradation and biodiversity loss. The development of roads in tropical forests is largely driven by industrial selective logging, which can provide a sustainable source of revenue for developing countries while avoiding more detrimental forms of forest degradation or deforestation. Understanding the dynamics and impacts of road development is challenging, because road inventories in remote tropical forests have been largely incomplete or outdated. In this study, we present novel remote sensing-based methods for automated monitoring of road development and apply them across the Congo Basin forest region, an area characterized by increasing road development rates driven by logging activities. We trained a deep learning model with Sentinel-1 and -2 satellite imagery to map road development on a monthly basis at 10 m spatial scale, leveraging the complementary value of radar and optical imagery. Applying the model across the Congo Basin forest, we present a vectorized map of road development from January 2019 until December 2022, demonstrating an F1-score of 0.909, a false detection rate of 4.2% and a missed detection rate of 14.9%. In total, we mapped 35,944 km of road development in the Congo Basin forest during the four years, with at least 78% apparently related to logging activities, mainly located in the western part of the region. We estimate that 30% of the detected road openings were previously abandoned logging roads that were reopened. In addition, 23% of detected road development was located in areas considered to be intact forest landscapes. The road monitoring methods demonstrated in this study can facilitate several crucial forest management and conservation objectives in the tropics, such as assessing ecological and climate impacts related to selective logging, monitoring illegal or unsustainable activities, and providing a basis for improved understanding and evaluation of human impacts on forests at large scale. More information, including a full overview of the Congo Basin forest road map, can be found at: <span><span>https://wur.eu/forest-roads</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114380"},"PeriodicalIF":11.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142369402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil moisture effects on InSAR - A correction approach and example from a hyper-arid region","authors":"R.B. Lohman,&nbsp;P.M. Bürgi","doi":"10.1016/j.rse.2023.113766","DOIUrl":"10.1016/j.rse.2023.113766","url":null,"abstract":"<div><p>We present Interferometric Synthetic Aperture<span> data spanning a series of precipitation events that impacted the southern edge of the Arabian Peninsula in 2017-2018. The arid climate, sparse vegetation and low topographic relief result in very high interferometric coherence magnitude between most pairs of dates, even for those separated by multiple years. For pairs of dates with differing soil moisture conditions, such as a “dry” date and a date immediately following one of the precipitation events, the interferometric coherence magnitude is much lower. However, pairs spanning the same event, but with a longer time interval, have high interferometric coherence magnitude. This observation suggests that the phase changes that result in lower coherence for some pairs are not permanent, such as those that would result from erosion or deposition of material, but are due to the variations in soil moisture. In support of this view, when we compare the phase of individual pixels to their neighbors, we observe similar phase change trends for each precipitation event. We present a simple statistical model of the relationship between soil moisture and phase, and show that it predicts the observed coherence and phase histories within this particular SAR time series. We also show how the parameters of this relationship can be inferred from the InSAR observables, and can be used to reduce the soil moisture effects on coherence and phase even for pairs of dates that were not used in that parameter estimation. For the test data considered here, the noise associated with soil moisture is reduced by 40%. We present results for synthetic time series, including a demonstration of the widely-observed phenomenon that displacement rates inferred from InSAR time series depend on the choice of interferometric pairs used in the analysis.</span></p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"297 ","pages":"Article 113766"},"PeriodicalIF":13.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41984567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spatial and temporal evaluation of the SMAP cropland b-parameter across the U.S. Corn Belt","authors":"Theodore Hartman ,&nbsp;Richard Cirone ,&nbsp;Kaitlin Togliatti ,&nbsp;Brian K. Hornbuckle ,&nbsp;Andy VanLoocke","doi":"10.1016/j.rse.2023.113752","DOIUrl":"10.1016/j.rse.2023.113752","url":null,"abstract":"<div><p><span><span>The liquid water contained within plant tissue per ground area is an indicator of plant biomass and related to plant transpiration. In the U.S. Corn Belt this plant water is dominated by water in crop tissue. </span>Microwave remote sensing provides an opportunity to measure crop water at a 30 to 40-km spatial scale across the U.S. Corn Belt. Crop water has been shown to be directly proportional to L-band vegetation optical depth (VOD) through a proportionality constant called the b-parameter. NASA’s SMAP satellite uses a static b-parameter of 0.11 for cropland areas, however the b-parameter varies with crop type. This study answers the following research questions. First, does the value of the b-parameter value vary across the U.S. Corn Belt given the observed differences in the proportions of corn and soybeans grown across the region? Secondly, does the SMAP b-parameter value vary throughout the growing season given the significant change in the distribution of crop water within tissue types throughout the growing season? Since in-situ measurements of the b-parameter are higher for corn, we hypothesize that as the relative fraction of corn in a SMAP pixel increases, the b-parameter will increase. We test this hypothesis using satellite scale modeling of crop water and SMAP L2 DCA VOD measurements at 18 sites across the U.S. Corn Belt for the years 2015, 2016, and 2017. For each site year, the crop water for the satellite pixel is simulated using the Agro-IBIS crop model and is weighted according to the proportion of corn and soybeans grown in that location. We find that there is no statistically significant correlation between the relative corn fraction and the SMAP b-parameter across the U.S. Corn Belt and that the current SMAP cropland value of 0.11 is appropriate for the beginning of the growing season (</span><span><math><mrow><mtext>growing degree days</mtext><mspace></mspace><mrow><mo>(</mo><mtext>GDD</mtext><mo>)</mo></mrow><mo>&lt;</mo><mn>10</mn><mover><mrow><mn>0</mn></mrow><mrow><mo>̄</mo></mrow></mover><mn>0</mn><msup><mrow><mspace></mspace></mrow><mrow><mo>∘</mo></mrow></msup><mtext>C</mtext><mi>⋅</mi><mtext>day</mtext></mrow></math></span>). However, we find that the b-parameter in the second half of the growing season (<span><math><mrow><mtext>GDD</mtext><mo>≥</mo><mn>10</mn><mover><mrow><mn>0</mn></mrow><mrow><mo>̄</mo></mrow></mover><mn>0</mn><msup><mrow><mspace></mspace></mrow><mrow><mo>∘</mo></mrow></msup><mtext>C</mtext><mi>⋅</mi><mtext>day</mtext></mrow></math></span>), during the crop reproductive stages, has an average value of 0.17. The results from this study indicate that while there is no change in the b-parameter due to the proportion of crops growing in a satellite pixel, there may be a difference in b-parameter due to changing water distribution within the crop canopy due to the development of crop reproductive structures (ears and pods) during the second half of the growing season.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"297 ","pages":"Article 113752"},"PeriodicalIF":13.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42541500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions","authors":"Julien Michel ,&nbsp;Olivier Hagolle ,&nbsp;Simon J. Hook ,&nbsp;Jean-Louis Roujean ,&nbsp;Philippe Gamet","doi":"10.1016/j.rse.2023.113765","DOIUrl":"10.1016/j.rse.2023.113765","url":null,"abstract":"<div><p><span>Satellite observations in the Thermal Infra-Red (TIR) domain provide valuable information on Land Surface Temperatures, Evapo-Transpiration and water use efficiency and are useful for monitoring vegetation health, agricultural practices and urban planning. By 2030, there will be 3 new high-resolution global coverage satellite TIR missions in space, all of them with fields of view larger than </span><span><math><mo>±</mo></math></span> 30°. Directional anisotropy in TIR can affect the estimation of key application variables, such as temperature, and are typically studied by means of field campaigns or physical modelling. In this work, we have evaluated directional effects using simultaneous measurements from Landsat-8 and the <span><math><mo>±</mo></math></span><span> 45°field of view MASTER airborne TIR sensor from NASA. Differences as high as 6 K are observed in the surface temperatures derived from these simultaneous observations. Those differences are attributed to directional effects, with the greatest differences associated with hotspot conditions, where the solar and satellite viewing directions align. Five well studied parametric directional models have then been fitted to the temperature differences, allowing the amplitude of the measured directional effects to be reduced below 1 K, with small variations between models. These results suggest that a simple correction for directional effects could be implemented as part of the ground segment processing for the upcoming missions.</span></p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"297 ","pages":"Article 113765"},"PeriodicalIF":13.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46643755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}