Remote Sensing of Environment最新文献

{"title":"Simulation of urban thermal anisotropy at remote sensing pixel scales: Evaluating three schemes using GUTA-T over Toulouse city","authors":"Dandan Wang ,&nbsp;Leiqiu Hu ,&nbsp;James A. Voogt ,&nbsp;Yunhao Chen ,&nbsp;Ji Zhou ,&nbsp;Gaijing Chang ,&nbsp;Jinling Quan ,&nbsp;Wenfeng Zhan ,&nbsp;Zhizhong Kang","doi":"10.1016/j.rse.2023.113893","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113893","url":null,"abstract":"<div><p>The directional variation in upwelling thermal radiance (known as ‘thermal anisotropy’) affects our understanding of urban land surface temperature (LST) from remote sensing observations. Parametric models have been proposed to quantify and potentially correct the thermal anisotropy from satellite systems. The accurate specification of the coefficients is critical for broadening applications of parametric models. However, the current research is limited to only one study area or one particular approach which often does not sufficiently offer transformative understanding for effective applications over other metropolitan areas. This study focuses on systematically evaluating schemes that determine the model coefficients. We use a geometric model to simulate Urban Thermal Anisotropy Time-series (GUTA-T) and propose different approaches to determine the physically-interpretable parameters. The model and solution schemes reduce the uncertainties caused by observational errors and simplifications of complex urban surfaces. The three schemes include estimating parameter values using component urban surface temperatures obtained from model simulations (Scheme #1mod) or field observations (Scheme #1obs) (‘forward’ approach), inverting parameter values from known anisotropy (Scheme #2) and from multi-angular LST observations (Scheme #3) (‘backward’ approach). The three schemes were separately evaluated and compared to an independent airborne dataset. These schemes have consistent results. The root mean square errors (RMSE) between LST anisotropy from the three schemes and airborne measurements are ranked as: Scheme #3 (1.0 K) &lt; Scheme #2 (1.2 K) &lt; Scheme #1 (based on field data) (1.3 K) &lt; Scheme #1 (based on TUF3D simulation) (1.5 K), whereas the overall amplitude of the variation of directional temperature averaged over the 5 flights is 11.9 K. The inverted parameter values from the three schemes agree well with the results from field measurements. The three schemes have advantages and disadvantages, and are expected to be combined depending on the available input data. These schemes represent multiple options to quantify and/or correct the anisotropic impact from remote sensing LST for urban applications.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"300 ","pages":"Article 113893"},"PeriodicalIF":13.5,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72250335","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":"Mountain snow depth retrievals from customized processing of ICESat-2 satellite laser altimetry","authors":"Hannah Besso,&nbsp;David Shean,&nbsp;Jessica D. Lundquist","doi":"10.1016/j.rse.2023.113843","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113843","url":null,"abstract":"<div><p>Snow depth is highly variable across basins, yet most snow depth data in the western U.S. come from sparse in situ point measurements. The water resources community needs accurate snow depth data for improved basin-wide snow depth estimates. The NASA ICESat-2 mission has provided over four years of global satellite laser altimetry measurements since October 2018. Previous studies have shown that standard ICESat-2 data products, when combined with snow-off digital terrain models (DTMs) from airborne laser scanning, have the potential to provide snow depth measurements with varying accuracy depending on factors such as surface slope and canopy cover. In this study we show that ICESat-2 snow depth measurements can be improved with customized data products generated using the SlideRule Earth service. Here we investigate the accuracy of our ICESat-2 SlideRule snow depth method using four years (2019–2022) of reference in situ snow depth measurements and airborne lidar snow depth observations for two watersheds with varying terrain characteristics: the Tuolumne River basin above Hetch Hetchy, CA and the Methow Valley, WA. We observe median differences of −0.14 m (RMSE of 0.18 m) and −0.20 m (RMSE of 0.33 m) between our ICESat-2 snow depth measurements and reference snow depth measurements for the Tuolumne Basin and Methow Valley sites, respectively. While individual ICESat-2 elevation measurements can contain noise, basin-scale aggregation offers robust statistics for snow depth. Differences in accuracy between sites are attributed to terrain characteristics and their spatial distributions. The customized ICESat-2 SlideRule data products used in this study resulted in more accurate median snow depth values, including under canopy, than those found by previous studies using standard ICESat-2 data products in mid-latitude mountainous regions. When combined with snow-off DTMs, the aggregated snow-on ICESat-2 SlideRule observations could provide a new snow depth dataset across the western U.S. and potentially global land surfaces.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"300 ","pages":"Article 113843"},"PeriodicalIF":13.5,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425723003942/pdfft?md5=b77996092043ca5d87e61f3b6734fb1b&pid=1-s2.0-S0034425723003942-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72250388","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":"Global retrieval of the spectrum of terrestrial chlorophyll fluorescence: First results with TROPOMI","authors":"Feng Zhao ,&nbsp;Weiwei Ma ,&nbsp;Jun Zhao ,&nbsp;Yiqing Guo ,&nbsp;Mateen Tariq ,&nbsp;Juan Li","doi":"10.1016/j.rse.2023.113903","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113903","url":null,"abstract":"<div><p>Solar-Induced chlorophyll Fluorescence (SIF) could be used as an indicator of photosynthetic status due to the close relationship between SIF and the photosynthetic apparatus. Terrestrial SIF is emitted throughout the red and near-infrared spectrum and is characterized by two peaks centered around 685 nm and 740 nm, respectively. In this study, we present a data-driven approach to reconstruct the terrestrial SIF spectrum from measurements by TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 precursor mission. This approach makes use of solar Fraunhofer lines in the combined spectral windows devoid of strong atmospheric absorption features to retrieve SIF signal from the solar radiation reflected by the surface and atmosphere system. Information contents are mainly from the two windows close to the red and far-red SIF peaks, 663–686 nm and 743–758 nm. A linear forward model represented as an addition of the SIF-free radiance spectrum and the SIF component is proposed with a proper selection of its parameter settings. The SIF component was simulated as linear combinations of 2 basis SIF spectra. Through inverting the linear forward model, the SIF spectrum was retrieved from the solar radiation reflected by the surface and atmosphere system. The evaluation of the retrieval results is performed by inter-comparison with other SIF datasets. The comparisons display similar spatial distributions for the weekly global SIF composites for the first two weeks in June and December of 2019 and July and December of 2021. Especially the comparison of the far-red SIF datasets with other dedicated far-red SIF retrievals demonstrates close agreement, indicating consistency among the retrieval approaches. The reconstructed TROPOMI red SIF shows improved and more reasonable spatiotemporal distributions. The retrieval uncertainty for the weekly global composite is about 12% and 2% of the peak red and far-red SIF values, respectively, which can be considered as satisfactory error thresholds for global composites of SIF observations. Different spectral features for several typical biomes from reconstructed SIF spectra suggest that red and far-red SIF may carry complementary information on photosynthetic function and biophysical properties of the plant. For the first time, the reconstruction of the SIF spectrum is achieved for spaceborne measurements with the potential to open new applications for better understanding of the ecosystem function.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"300 ","pages":"Article 113903"},"PeriodicalIF":13.5,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72250336","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":"Low-amplitude brittle deformations revealed by UAV surveys in alluvial fans along the northwest coast of Lake Baikal: Neotectonic significance and geological hazards","authors":"Оksana V. Lunina ,&nbsp;Anton A. Gladkov ,&nbsp;Alexey V. Bochalgin","doi":"10.1016/j.rse.2023.113897","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113897","url":null,"abstract":"<div><p>Many large deltas and other areas, underlain by unconsolidated sediments, are heavily populated but impacted by various natural deformational processes. The causes and mechanisms of the deformation are often obscure because of difficulties in the identification of their geological source. We used an unmanned aerial vehicle (UAV) to survey an area of up to several km² to detect and map surface discontinuities with displacements of a few centimeters, which can indicate the presence of initial geological deformations. We encountered such offsets in the alluvial fans on the northwestern coast of Lake Baikal, overlying the damage zones of several faults. These deformations at the different sites are referred to as primary or secondary co-seismic ruptures, brittle creep, or cryogenic fissures, which have specific origins. They are located a few hundred meters from a principal fault plane, from junctions of faults, and in the peripheral parts of alluvial fans, making these areas potentially hazardous. We analyzed the digital surface models of local segments of one of the delta plains between 2020 and 2021 and established that its periphery has subsided by an average of 5–10 cm. In areas of accumulation of the river sediment, the thickness of coarse clastic deposits has increased by approximately the same amount. Locally and in the coastal zone, the vertical surface changes are larger. In the axial parts of some seismically induced gravitational failures, the subsidence reached 33 cm over a period of 11 months and 19 days. Our results show that sediments of alluvial fans are very susceptible to various tectonic and exogenous deformational processes. The interpretation of the ultra-high resolution UAV's images can help the recognition the low-amplitude brittle deformations at an early stage of their development. Therefore, such UAV surveys are critical in the discernment of neotectonic activity and its related hazards over short observation periods.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"300 ","pages":"Article 113897"},"PeriodicalIF":13.5,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72250390","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":"Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar","authors":"Jamie Tolan ,&nbsp;Hung-I Yang ,&nbsp;Benjamin Nosarzewski ,&nbsp;Guillaume Couairon ,&nbsp;Huy V. Vo ,&nbsp;John Brandt ,&nbsp;Justine Spore ,&nbsp;Sayantan Majumdar ,&nbsp;Daniel Haziza ,&nbsp;Janaki Vamaraju ,&nbsp;Theo Moutakanni ,&nbsp;Piotr Bojanowski ,&nbsp;Tracy Johns ,&nbsp;Brian White ,&nbsp;Tobias Tiecke ,&nbsp;Camille Couprie","doi":"10.1016/j.rse.2023.113888","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113888","url":null,"abstract":"<div><p>Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. Repeated measurements of these data allow for the observation of deforestation or degradation of existing forests, natural forest regeneration, and the implementation of sustainable agricultural practices like agroforestry. Assessments of tree canopy height and crown projected area at a high spatial resolution are also important for monitoring carbon fluxes and assessing tree-based land uses, since forest structures can be highly spatially heterogeneous, especially in agroforestry systems. Very high resolution satellite imagery (less than one meter (1 m) Ground Sample Distance) makes it possible to extract information at the tree level while allowing monitoring at a very large scale. This paper presents the first high-resolution canopy height map concurrently produced for multiple sub-national jurisdictions. Specifically, we produce very high resolution canopy height maps for the states of California and São Paulo, a significant improvement in resolution over the ten meter (10 m) resolution of previous Sentinel / GEDI based worldwide maps of canopy height. The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps. We also introduce a post-processing step using a convolutional network trained on GEDI observations. We evaluate the proposed maps with set-aside validation lidar data as well as by comparing with other remotely sensed maps and field-collected data, and find our model produces an average Mean Absolute Error (MAE) of 2.8 m and Mean Error (ME) of 0.6 m.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"300 ","pages":"Article 113888"},"PeriodicalIF":13.5,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S003442572300439X/pdfft?md5=e5d02a4b7c7a4f4410d78a3017036fc8&pid=1-s2.0-S003442572300439X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72250334","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":"A vehicle imaging approach to acquire ground truth data for upscaling to satellite data: A case study for estimating harvesting dates","authors":"Chongya Jiang ,&nbsp;Kaiyu Guan ,&nbsp;Yizhi Huang ,&nbsp;Maxwell Jong","doi":"10.1016/j.rse.2023.113894","DOIUrl":"https://doi.org/10.1016/j.rse.2023.113894","url":null,"abstract":"<div><p>Crop harvesting date is critical information for crop yield prediction, financial and logistic planning of grain market and downstream supply chain. Remote sensing has the potential to map harvesting date at regional scale. However, existing studies generally lack ground truth data, and have not fully utilized spectral and temporal information of satellite data. To address these gaps, we present a new approach named Field Rover to acquire large volumes of binary harvesting status (harvested VS. unharvested) ground truth data at regional scale on a weekly basis, by repeatedly using vehicle-mounted cameras to collect time series images for sampled fields and interpreting them with a deep learning approach. With these vehicle-derived ground truth data, we present a machine learning approach to upscale harvesting status and subsequently estimate harvesting date to each field in a study area based on a new satellite platform Planet SuperDove which provides daily 8-band surface reflectance at 3 m resolution. We acquired &gt;200,000 vehicle images from September to November for two years (2021 and 2022), and the deep learning model was able to generate harvesting status for each image with an accuracy of 0.998, which can be treated as ground truth. From a time series of harvesting status derived from revisiting vehicle images, harvesting dates for &gt;500 fields were obtained by a change detection approach. We then trained a remote sensing classification model using harvesting status ground truth, and applied it to generate a harvesting status map for each Planet SuperDove overpass day. The classification model achieved an accuracy of 0.96 and subsequently accurate harvesting date maps were obtained by a curve fitting approach. We found that the Planet SuperDove harvesting date agreed well with the Field Rover harvesting date ground truth (R² = 0.84, RMSE ≈ 5.5 days) at the field level in two years. When focusing on 2022 when more Planet SuperDove satellites were launched, the remote sensing of the harvest date achieved an accuracy of R² = 0.91, and RMSE ≈ 3.3 days. This study demonstrated the efficacy of using repeated vehicle images to acquire time-related agricultural ground truth data, as well as the efficacy of using vehicle-satellite integrative sensing to upscale ground truth data to the regional scale. We envision this new method can be applied to monitor other agricultural management practices and therefore effectively advance the monitoring and modeling of smart farming and sustainable agriculture.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"300 ","pages":"Article 113894"},"PeriodicalIF":13.5,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71725958","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":"Machine learning based classification of lake ice and open water from Sentinel-3 SAR altimetry waveforms","authors":"Jaya Sree Mugunthan ,&nbsp;Claude R. Duguay ,&nbsp;Elena Zakharova","doi":"10.1016/j.rse.2023.113891","DOIUrl":"10.1016/j.rse.2023.113891","url":null,"abstract":"<div><p>The aim of the study was to evaluate, for the first time, the capability of different machine learning (ML) algorithms in classifying along-track lake surface conditions (open water and ice types) across ice seasons (freeze-up, ice growth and break-up periods) from Sentinel-3 A/B synthetic aperture radar altimeter (SRAL) data. To achieve this goal, over 107,500 radar waveforms extracted from 11 large lakes across the Northern Hemisphere and three ice seasons (2018–2021) were manually labelled using complementary satellite data (Sentinel-1 imaging Synthetic Aperture Radar (SAR), Sentinel-2 Multispectral Instrument (MSI) Level 1C, and MODIS Aqua/Terra data) for the training and testing of the ML algorithms in discriminating between open water, young (thin) ice, growing ice and melting ice. The four ML algorithms tested include Random Forest (RF), Gradient Boosting Trees (GBT), K Nearest Neighbor (KNN) and Support Vector Machine (SVM). To characterize the waveforms, seven waveform parameters were derived: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient (Sigma0), late tail to peak power (LTPP), early tail to peak power (ETPP) and the maximum value of the echo power (Max). Accuracies &gt;95% were achieved across all classifiers using a 4-parameter combination (Sigma0, PP, OCOG Width, and LEW). Among all waveform parameters, Sigma0, OCOG width and PP were found to be the most important parameters for discriminating between lake ice types and open water. Despite showing comparable classification performances in the overall classification, RF and KNN are found to be a better fit for global lake ice mapping as both are less sensitive to their internal hyperparameters. Additionally, consistent results (&gt;93.7% accuracy in all classifiers) achieved on the accuracy assessment carried out for each lake (out-of-sample testing) revealed the strength of the classifiers for spatial transferability. Implementation of RF and KNN could be valuable in a pre-or post-processing step for identifying lake surface conditions under which the retrieval of water level and ice thickness may be limited or not possible and, therefore, inform algorithms currently used for the generation of operational or research products. While the research focused on 11 of the largest lakes of the Northern Hemisphere, the classification approach presented herein has potential for application on smaller lakes too since data in SAR mode (∼300 m along-track resolution) are used.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"299 ","pages":"Article 113891"},"PeriodicalIF":13.5,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S003442572300442X/pdfft?md5=ebf6304468b17b0856bca3e55010b8db&pid=1-s2.0-S003442572300442X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71491962","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":"Mapping integrated crop-livestock systems in Brazil with planetscope time series and deep learning","authors":"Inacio T. Bueno ,&nbsp;João F.G. Antunes ,&nbsp;Aliny A. Dos Reis ,&nbsp;João P.S. Werner ,&nbsp;Ana P.S.G.D.D. Toro ,&nbsp;Gleyce K.D.A. Figueiredo ,&nbsp;Júlio C.D.M. Esquerdo ,&nbsp;Rubens A.C. Lamparelli ,&nbsp;Alexandre C. Coutinho ,&nbsp;Paulo S.G. Magalhães","doi":"10.1016/j.rse.2023.113886","DOIUrl":"10.1016/j.rse.2023.113886","url":null,"abstract":"<div><p>Accurate mapping of crops with high spatiotemporal resolution plays a critical role in achieving the Sustainable Development Goals (SDGs), especially in the context of integrated crop-livestock systems (ICLS). Stakeholders can make informed decisions and implement targeted strategies to achieve multiple SDGs related to agriculture, rural development, and sustainable livelihoods by understanding the spatial dynamics of these systems. Accurate information on the extent of ICLS derived from multitemporal remote sensing and emerging map techniques such as deep learning can help in the implementation of sustainable agricultural practices. However, far too little attention has been paid to ICLS map accuracy because it may not be at the forefront of research agendas compared to those of other agricultural practices. This paper aims to map ICLS using high spatiotemporal resolution imagery and deep learning neural network classifiers at two different sites located in Brazil. The pipeline involves four interpretation approaches based on the ICLS class: evaluating deep neural network classifiers with different image composition intervals, explaining commission and omission errors, evaluating the temporal transferability of the method, and evaluating the influence of variables. The study area consists of two locations in São Paulo (study site 1, SS1) and Mato Grosso state (study site 2, SS2), Brazil. We derived nine spectral variables from PlanetScope (PS) images and four metrics through object-based image analysis (OBIA) using two time intervals, 10 and 15 days, to generate the image compositions. These input variables were used in three deep neural network classifiers: convolutional neural network in one dimension (Conv1D), long short-term memory (LSTM), and LSTM with a fully convolutional network (LSTM-FCN). Our results showed that mapping dynamic land use such as ICLS is possible by using high-spatiotemporal-resolution imagery and deep neural network classifiers. The 15-day LSTM-FCN classifier returned the highest map accuracies for both sites, with the following class-level accuracies: producer accuracy (PA) = 97.0% and user accuracy (UA) = 97.0% for SS1 and PA = 82.0% and UA = 96.5% for SS2. Meanwhile, we found map uncertainties arising from the diverse crop calendars and spectro-temporal similarities between ICLS and other land use. The best approaches revealed that temporal generalization was suitable for mapping ICLS, but some classifiers could not generalize due to the inherent characteristics of the class. Most variables were considered efficient for predicting ICLS, although spectral indices revealed better functional relationships, while the PS bands had a lower influence on the predictions. The accuracies achieved with the proposed method represent promising opportunities for the sufficiently accurate mapping of ICLS and other complex crop activities.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"299 ","pages":"Article 113886"},"PeriodicalIF":13.5,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71491469","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":"Combined modelling of annual and diurnal land surface temperature cycles","authors":"Lluís Pérez-Planells,&nbsp;Frank-M. Göttsche","doi":"10.1016/j.rse.2023.113892","DOIUrl":"10.1016/j.rse.2023.113892","url":null,"abstract":"<div><p><span>The land surface's thermal dynamics follows annual and diurnal cycles that are to a large extent controlled by solar geometry. Therefore, annual and diurnal variations of land surface temperature (LST) can be modelled with relatively simple functions controlled by a small number of parameters, typically from three to six. The parameter values of the models can be determined by fitting the respective functions to time series of LST observations. Commonly either annual or diurnal LST variations are modelled or they are modelled sequentially in a two-step process. Here, we combine an annual temperature cycle (ATC) model controlled by the solar zenith angle (ATC</span>_sza<span><span>) with a four-parameter version of the diurnal temperature cycle (DTC) model GOT09: this yields a new annual-diurnal temperature cycle (ADTC) model that simultaneously describes the annual and diurnal surface temperature dynamics. The proposed ADTC model is controlled by physically meaningful parameters: annual minimum temperature, annual temperature amplitude, annual maximum of daily temperature amplitude, mean time of thermal noon and time lag of maximum temperature with respect to summer solstice. Thus, the entire annual-diurnal LST dynamics is described with only five parameters. The new model was tested by fitting it to one year of LST observations obtained for five globally representative tiles of the Moderate Resolution Imaging Spectroradiometer<span> (MODIS), onboard EOS – TERRA and EOS – AQUA satellites. For these tiles, the mean of the </span></span>root mean square error (RMSE) was 4.2 K. ADTC modelled LSTs were also compared against those obtained with the standard ATC model for the four MODIS overpass times at five representative sites: for these, an overall RMSE of 1.2 K between the two models was obtained. The ADTC derived LSTs were validated against in-situ measurements from three different sites, which yielded an overall RMSE of 3.4 K. Additional investigations over five areas with different land covers (i.e. urban, lake, forest, mountain area and desert) revealed the potential of the ADTC parameters to describe the corresponding surface and climate properties. Since it is driven by solar geometry, the ADTC model reproduces double LST peaks in the tropics naturally. Furthermore, all available observations are modelled simultaneously, which means that a single set of parameters is obtained for each pixel and year.</span></p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"299 ","pages":"Article 113892"},"PeriodicalIF":13.5,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71512733","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":"Novel spectral indices for enhanced estimations of 3-dimentional flavonoid contents for Ginkgo plantations using UAV-borne LiDAR and hyperspectral data","authors":"Kai Zhou,&nbsp;Lin Cao,&nbsp;Xin Shen,&nbsp;Guibin Wang","doi":"10.1016/j.rse.2023.113882","DOIUrl":"10.1016/j.rse.2023.113882","url":null,"abstract":"<div><p><span>Leaf flavonoid content (LFC) is a marked indicator of the protection signals from biotic and abiotic stresses, as well as the potential in the recovery of phenolic compounds from plants for producing potent antioxidants. LFC has been non-destructively retrieved from leaf reflectance spectra in recent studies. However, the LFC estimation from canopy-level spectra remains poorly understood and challenging arise from the confounding effects of other pigments and canopy structure. To address this limitation, this study proposed a suite of new 3-Dimentional spectral indices (SIs), in which the leaf-level standard flavonoid indices (FIs) are normalized by structure indices or chlorophyll indices. The hypothesis investigated is that these new SIs, derived from UAV-based hyperspectral point cloud data (fused by canopy hyperspectral images and LiDAR point cloud data), can enhance detecting LFC distribution within the canopies of </span><em>Ginkgo</em> plantations, by mitigating the effects of canopy structure and chlorophyll absorption. The results demonstrated that most chlorophyll-based normalized indices (CV-R² = 0.56–0.65) outperformed the structure-based normalized indices (CV-R² = 0.44–0.57) and the standard FIs (CV-R² = 0.19–0.54). In specific, FI_420,710/SR_800,710 (CV-R² = 0.65) out of chlorophyll-based normalized indices performed better than other indices. With the use of FI_420,710/SR_800,710, the 3-Dimentional distribution of LFC within <em>Ginkgo</em> canopies can be well mapped. In summary, this study indicates marked potentials of the developed normalized indices for mapping LFC distribution, as well as providing new insight into alleviating the confounding effects of chlorophyll and structure on LFC estimation of <em>Ginkgo</em><span> plantations, with simulations conducted by the canopy radiative transfer model.</span></p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"299 ","pages":"Article 113882"},"PeriodicalIF":13.5,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71491917","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}