Remote Sensing of Environment最新文献

{"title":"Comparison of correction methods for bidirectional effects in ocean colour remote sensing","authors":"Davide D'Alimonte ,&nbsp;Tamito Kajiyama ,&nbsp;Jaime Pitarch ,&nbsp;Vittorio Ernesto Brando ,&nbsp;Marco Talone ,&nbsp;Constant Mazeran ,&nbsp;Michael Twardowski ,&nbsp;Srinivas Kolluru ,&nbsp;Alberto Tonizzo ,&nbsp;Ewa Kwiatkowska ,&nbsp;David Dessailly ,&nbsp;Juan Ignacio Gossn","doi":"10.1016/j.rse.2025.114606","DOIUrl":"10.1016/j.rse.2025.114606","url":null,"abstract":"<div><div>Several methods were developed in Ocean Colour remote sensing over the last 25 years to model the anisotropy of the upwelling radiant field with respect to observation and solar-illumination geometries, also denoted as bidirectional reflectance distribution function (BRDF). These methods are necessary to produce normalized, or “BRDF-corrected,” marine reflectance representative of the seawater's inherent optical properties (IOPs) independently of the measurement conditions. Each scheme relies on specific modeling assumptions and implementation solutions, which can lead to different results depending on the actual combination of the seawater IOPs with the illumination and viewing geometry. The first aim of this study is to analyze the principles and methods of the reference BRDF schemes presented by Morel et al. (denoted as M02), Park and Ruddick (P05), Lee et al. (L11), He et al. (H17), and Twardowski and Tonizzo (T18). Acknowledging the direct applicability of M02, P05, and L11, their performance has been verified under a variety of conditions, including in situ measurements, matchup observations, and space-borne images. Comparisons between non-corrected and normalized data clearly confirm the need to account for the BRDF effect. In particular, the analysis of the results indicates 1) a substantial equivalence of M02, P05, and L11 in clear waters and 2) the tendency to obtain better results with M02 and L11 as the optical complexity increases. Although M02 was conceived for Case 1 waters, the underlying Chlorophyll-<em>a</em> overestimation tendency in some optically complex conditions is likely the reason for its extended applicability. Since L11 is based on a more comprehensive and flexible framework for all water types, the design of this method is suggested for revisions and BRDF correction improvements.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114606"},"PeriodicalIF":11.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444813","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":"First quasi-global soil moisture retrieval using Fengyun-3 GNSS-R constellation observations","authors":"Wentao Yang ,&nbsp;Fei Guo ,&nbsp;Xiaohong Zhang ,&nbsp;Yifan Zhu ,&nbsp;Zheng Li ,&nbsp;Zhiyu Zhang","doi":"10.1016/j.rse.2025.114653","DOIUrl":"10.1016/j.rse.2025.114653","url":null,"abstract":"<div><div>Global Navigation Satellite System-Reflectometry (GNSS-R) has considerable potential for large-scale soil moisture (SM) monitoring. With the Fengyun-3 (FY-3) E, F, and G satellites currently in orbit, the FY-3 satellite series has formed the GNSS-R constellation. A comprehensive analysis and validation of the SM retrieval capability of the FY-3 GNSS-R constellation observations are essential. This study is the first to use FY-3 GNSS-R constellation observations to evaluate their performance in quasi-global daily SM retrieval. Specifically, this study proposed an effective SM retrieval method for obtaining an FY-3 GNSS-R SM with minimal ancillary data. Compared with the Soil Moisture Active Passive (SMAP) reference SM, the FY-3 SM exhibited a reasonable global spatial pattern as SMAP, with a root mean square error (RMSE) of 0.039 <span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span>/<span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span> in low vegetation areas. Validation results from over 200 independent in situ stations showed that the unbiased RMSE and correlation for FY-3 SM were 0.039 <span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span>/<span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span> and 0.60, respectively. Triple collocation (TC) analysis showed that the standard deviation and correlation for the FY-3 SM were 0.017 <span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span>/<span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span> and 0.62, respectively. Global and local validations indicate that the SM derived from the FY-3 GNSS-R constellation observations has well-defined accuracy and effectively captures spatiotemporal variations. Compared to the contemporaneous Cyclone GNSS official SM, the accuracy of the FY-3 SM retrieved using the proposed method improved by 17.1 %. Consequently, the SM from the FY-3 GNSS-R constellation observations can be an invaluable complement to the global SM dataset. Furthermore, this method effectively reduced systematic bias and random errors in SM retrievals (unbiased RMSE (ubRMSE) from 0.041to 0.034 <span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span>/<span><math><msup><mi>cm</mi><mn>3</mn></msup><mspace></mspace></math></span>and TC standard deviation from 0.034to 0.017 <span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span>/<span><math><msup><mi>cm</mi><mn>3</mn></msup></math></span>), which may provide a valuable reference for generating SM products from subsequent FY-3 GNSS-R constellations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114653"},"PeriodicalIF":11.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451518","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":"Spatial-temporal evolution of landslides spanning the impoundment of Baihetan mega hydropower project revealed by satellite radar interferometry","authors":"Jiaming Yao ,&nbsp;Teng Wang ,&nbsp;Xin Yao","doi":"10.1016/j.rse.2025.114668","DOIUrl":"10.1016/j.rse.2025.114668","url":null,"abstract":"<div><div>Reservoir landslides are the focus of geohazards associated with mega hydropower projects and have been extensively studied by monitoring their post-impoundment deformation. However, how landslide deformation changes before, during, and after impoundment is rarely known. Using satellite radar interferometry, we map 200 active landslides with their time-series deformation spanning the impoundment of Baihetan, the second-largest hydropower project globally. We define the amplitude of seasonal fluctuation (ASF) to analyze the impact of rainfall and water level on seasonal landslide velocities before and after impoundment. Interestingly, although landslides are overall accelerated, a reduction in seasonal fluctuation is apparent after the impoundment. We argue that the project elevated water levels during the dry season, only promoting landslide motion when they were kept stable before impoundment. We also find the 32 newly formed landslides are more likely to develop on slopes with structures related to river flow direction, emphasizing the role of the raised water in triggering new landslides. These findings reveal how landslides respond to mega hydropower projects, facilitating disaster risk management and resettlement policy regulation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114668"},"PeriodicalIF":11.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444812","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":"Methodology comparison for correcting woody component effects in leaf area index calculations from digital cover images in broadleaf forests","authors":"Yongkang Lai ,&nbsp;Xihan Mu ,&nbsp;Dasheng Fan ,&nbsp;Jie Zou ,&nbsp;Donghui Xie ,&nbsp;Guangjian Yan","doi":"10.1016/j.rse.2025.114659","DOIUrl":"10.1016/j.rse.2025.114659","url":null,"abstract":"<div><div>Non-destructive methods are widely used for field measurement of leaf area index (LAI). However, the above-ground woody components of trees and shrubs, <em>i.e.</em>, trunks and branches, largely affect the measured gap fraction, thus hindering the accurate measurement of LAI. Many efforts have been made to correct for the woody component effect and estimate LAI, but there is a lack of research to systematically compare and analyze current methods, which mainly include: 1) correcting using woody-to-total area ratio (<span><math><mi>α</mi></math></span>), 2) transforming the leaf-on images into leaf-only images using artificially selected thresholds to determine whether woody components blocked leaves (TRM method), and 3) establishing the regression relationship between LAI and image features and/or other measured parameters using random forests (RFRM method) or 4) neural networks (NNRM method). We used rich data generated from 4734 scenes and 39 tree species to compare and analyze these methods. Additionally, considering the problems with the existing methods and the increasing requirements of LAI measurement, we proposed a new method (P2PLAI) using an image-to-image translation neural network (<em>i.e.</em>, Pixel2Pixel) to transform the leaf-on images into leaf-only images. The effective LAI (LAIe) was estimated using the translated leaf-only images, and then the LAIe was converted into LAI using the clumping index. The results showed that the traditional method using <span><math><mi>α</mi></math></span> was limited by the accuracy of the <span><math><mi>α</mi></math></span> estimation, with the RMSE from 0.34 to 0.92 and the absolute percentage error (Bias%) from 9.56 % to 22.29 %. The TRM method could not stably and accurately transform the leaf-on images and underestimated LAI, with the RMSE from 0.13 to 0.78 and Bias% from 3.39 % to 21.13 %. The regression methods (<em>i.e.</em>, RFRM and NNRM) had strong limitations since the accuracy of these two methods was related to the tree species and viewing zenith angles (VZAs) with RMSE up to 3.12 and Bias% up to 84.74 %. The P2PLAI method achieved the best agreement with the reference LAI. The RMSE and Bias% of P2PLAI respectively ranged from 0.05 to 0.26 and from 1.27 % to 7.70 % and were not influenced by tree species and VZAs. This study cautions against applying regression methods such as RFRM and NNRM for the indirect measurement of LAI in forests due to the complicated structures of vegetation components. The combination of an image-to-image translation neural network and a clumping effect correction model with physical meaning is recommended to measure LAI with digital photography.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114659"},"PeriodicalIF":11.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444814","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":"Deriving anisotropic correction for upwelling radiance from PACE's multi-angle polarimetry","authors":"Xiaodong Zhang ,&nbsp;Meng Gao ,&nbsp;Shuangyan He ,&nbsp;Lucas Barbedo","doi":"10.1016/j.rse.2025.114647","DOIUrl":"10.1016/j.rse.2025.114647","url":null,"abstract":"<div><div>NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission, launched on 8th February 2024, carries a hyperspectral radiometer, Ocean Color Instrument (OCI) and two multi-angle polarimeters, Hyper Angular Rainbow Polarimeter (HARP2) and Spectro-Polarimeter for Planetary Exploration one (SPEX-one). The simultaneous deployment of these sensors offers an unprecedented opportunity to derive more accurate bidirectional factors for correcting the Sun-sensor viewing dependence of the remote sensing reflectance derived from OCI. With a bidirectional remote sensing model based on quasi-single-scattering approximation to the radiative transfer equation, the angular shape of the volume scattering function (VSF) in backward directions, i.e., the χ factor for particles (χ_p) is derived from the multi-angle observation of HARP2. The derived χ_p is in turn used to drive the bidirectional remote sensing model to predict the bidirectional factor. Testing with prelaunch simulated HARP2 L1C data that includes uncertainties due to atmospheric correction, the proposed method can estimate bidirectional factor with an uncertainty &lt;10 % at any three visible bands of HARP2. Because the proposed method estimates the χ_p directly from the multi-angle observation, it fully accounts for the natural variability of VSFs, which were assumed to confine within a limited range of variation in the earlier bidirectional correction models.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"320 ","pages":"Article 114647"},"PeriodicalIF":11.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427876","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":"Quantum yield for sun-induced chlorophyll fluorescence (ΦF) captures rice plant dynamics under interplant competition","authors":"Jihyeon Yeo ,&nbsp;Insu Yeon ,&nbsp;Jaehyoung You ,&nbsp;Do-Soon Kim ,&nbsp;Hyungsuk Kimm","doi":"10.1016/j.rse.2025.114655","DOIUrl":"10.1016/j.rse.2025.114655","url":null,"abstract":"<div><div>Planting density and leaf angle are important factors related to rice growth and yield through interplant competition. Despite the necessity of understanding the dynamics of interplant competition according to planting density and leaf angle, detailed physiological changes throughout the growth cycle remain less clear due to the requirement for field surveys that are labor-intensive and time-consuming. Sun-induced chlorophyll fluorescence and its physiological quantum yield (Φ_F) have shown its capability for plant physiological investigations and can provide new opportunities for improved monitoring of crop physiological dynamics. However, it is uncertain whether Φ_F can quantify the impact of agronomic differences on the vegetative and reproductive growth of crops. In this study, we aim to explore whether Φ_F can quantify physiological dynamics in rice of different leaf angle distributions under varying planting density levels. We conducted an experiment of four different planting densities (<span><math><mn>11.2</mn><mo>×</mo><msup><mn>10</mn><mn>4</mn></msup></math></span>, <span><math><mn>15.2</mn><mo>×</mo><msup><mn>10</mn><mn>4</mn></msup></math></span>,<span><math><mspace></mspace><mn>18.2</mn><mo>×</mo><msup><mn>10</mn><mn>4</mn></msup></math></span>, <span><math><mn>24.2</mn><mo>×</mo><msup><mn>10</mn><mn>4</mn></msup></math></span> hills/ha) with two cultivars of different leaf angle distributions (i.e., erectophile and semi-erectophile leaf angle distribution) in a rice paddy. We measured Φ_F and collected agronomic data to monitor plant physiological and structural changes. Φ_F quantified the downregulation of photosynthetic activity at higher planting density plots during the vegetative growth period (a significant correlation between Φ_F and rate of LAI increase, R² = 0.62, <em>p</em>-value&lt;0.05) and indicated differences in grain yield, which was dominantly driven by the limited carbon sink (a significant correlation between Φ_F and yield, R² = 0.44, <em>p</em>-value&lt;0.1). Particularly, Φ_F showed different patterns of the planting density impact on yield between the two cultivars confirming the effect of leaf angle distribution on the interplant competition or the light. Our findings showed that Φ_F not only captures the difference in vegetative growth but also in reproductive growth and grain yield. This study demonstrated the importance of Φ_F for physiological investigations in agroecosystems and the potential for estimating crop productivity during the grain-filling stage as well as for improved crop yield estimation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"320 ","pages":"Article 114655"},"PeriodicalIF":11.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427879","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":"Geostationary ocean color satellite observations reveal the fine structure of mesoscale eddy dynamics","authors":"Xiaosong Ding ,&nbsp;Xianqiang He ,&nbsp;Yan Bai ,&nbsp;Wentao Ma ,&nbsp;Jiajia Li ,&nbsp;Feng Ye ,&nbsp;Shujie Yu ,&nbsp;Qiwei Hu ,&nbsp;Fang Gong ,&nbsp;Difeng Wang ,&nbsp;Teng Li","doi":"10.1016/j.rse.2025.114652","DOIUrl":"10.1016/j.rse.2025.114652","url":null,"abstract":"<div><div>Observations of mesoscale eddy structures rely heavily on satellite altimetry data. However, due to altimetry's coarse spatial resolution, the fine structure of eddy dynamics remains mysterious. Using high spatiotemporal resolution observations from the Geostationary Ocean Color Imager (GOCI), we reveal the fine structure and hourly dynamics of the eddy surface flow velocities, as well as the horizontal eddy transport processes. The sea surface flow field retrieved by the dense optical flow algorithm from the GOCI was consistent with the results derived from satellite altimetry data but had a much higher spatial resolution (500 m), which makes it feasible to capture the fine structure of eddy dynamics. Additionally, the hourly observations exposed rapid variations of the eddy kinetic energy and the horizontal advection transport of surface phytoplankton. These fine-scale and frequent GOCI observations increase the understanding of the dynamics and mass transport in mesoscale eddies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"320 ","pages":"Article 114652"},"PeriodicalIF":11.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436436","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 practical SIF-based crop model for predicting crop yields by quantifying the fraction of open PSII reaction centers (qL)","authors":"Yakai Wang ,&nbsp;Qiang Yu ,&nbsp;Zhunqiao Liu ,&nbsp;Wei Ren ,&nbsp;Xiaoliang Lu","doi":"10.1016/j.rse.2025.114658","DOIUrl":"10.1016/j.rse.2025.114658","url":null,"abstract":"<div><div>Crop models are essential for evaluating the effects of climate change on crop yields, optimizing agronomic practices, and guiding policy decisions to enhance food security. However, using traditional crop models, including both process-based and statistical models, for regional applications presents significant challenges. Process-based crop models often require extensive, locally-sensed inputs to drive the models, which are generally lacking at the regional level. Meanwhile, statistical crop models depend heavily on training data, but it is often difficult, or even impossible, to find high-quality training data on a large scale. Solar-induced chlorophyll fluorescence (SIF), a more physiologically based proxy for gross primary production (GPP), has shown good potential for estimating GPP and crop yield. We developed a practical SIF-based crop model driven by satellite SIF observations and three readily available datasets: air temperature, vapor pressure deficit, and soil moisture content. The key improvement of our research is to parameterize the fraction of open PSII reaction centers (<em>q</em>_L) for crops, and incorporate variations in <em>q</em>_L into the SIF-based estimation of crop GPP and yield. Using a leaf-level measurement system, we provided parameters for <em>q</em>_L in corn and soybean. We showed that the simulated <em>q</em>_L closely matches the measured <em>q</em>_L, with <em>R</em>² &gt; 0.95 and RMSE &lt;0.05, even under conditions of high light and/or high temperature, whereas the performance of SIF alone significantly decreased under stress. By using SIF and <em>q</em>_L within the mechanistic light response model, one can accurately estimate crop GPP without the need to parameterize various plant physiological processes or nutrient dynamics and management practices. This improvement substantially simplifies the model, reduces the need for driving variables and calibration data, and minimizes associated uncertainties. We applied the model to estimate corn and soybean yields in the U.S. Midwest for the period 2018–2023. A comparison with eddy covariance-based GPP measurements reveals that the simulated GPP accounts for 85 % of the variability in daily observed GPP for corn and 81 % for soybean. The model's performance at the regional scale was assessed by comparing it against county-level crop yield statistics. On average, the model captures 78 % of the county-level yield variability across more than 700 counties during the study period, achieving 76 % for corn and 81 % for soybean, with RMSE values of 14.47 Bu/Acre, and 4.09 Bu/Acre, respectively. The practical, yet mechanistic, SIF-based model introduced in this study represents a significant advance in regional and national crop yield estimation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"320 ","pages":"Article 114658"},"PeriodicalIF":11.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427875","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 transformer-based model for detecting land surface phenology from the irregular harmonized Landsat and Sentinel-2 time series across the United States","authors":"Khuong H. Tran ,&nbsp;Xiaoyang Zhang ,&nbsp;Hankui K. Zhang ,&nbsp;Yu Shen ,&nbsp;Yongchang Ye ,&nbsp;Yuxia Liu ,&nbsp;Shuai Gao ,&nbsp;Shuai An","doi":"10.1016/j.rse.2025.114656","DOIUrl":"10.1016/j.rse.2025.114656","url":null,"abstract":"<div><div>Land surface phenology (LSP) has been widely generated using traditional methods of fitting satellite-based time series of vegetation indices over the past two decades. However, these methods are highly vulnerable to the presence of temporal gaps and the use of specific smoothing or gap-filling algorithms. Several attempts have recently used Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to detect phenology, which produce an uncertainty of larger than three weeks and as large as two months. Further, these deep learning methods cannot handle missing data in time series and still need temporal gap-filling, which results in low to moderate accuracy in phenology detection. This study proposed a novel Transformer-based model to detect 30 m phenological events across the United States for the years 2019 and 2020. Specifically, we adapted the Transformer architecture to handle irregular time series and capture long-range relationships among all high-quality observations in the satellite time series. The Transformer-based model was trained using reference time series samples extracted from the high-quality LSP product, which was recently produced by fusing the HLS (Harmonized Landsat and Sentinel-2) observations with near-surface PhenoCam time series (HLS-PhenoCam LSP). The accuracy of the Transformer-based model for LSP detection using the two-band Enhanced Vegetation Index (EVI2) was investigated and compared with the most widely used Hybrid Piecewise Logistic Model based Land Surface Phenology Detection (HPLM-LSPD) algorithm and one dimensional (1D) CNN model. The phenological patterns detected from the Transformer-based model and the HPLM-LSPD algorithm were mostly comparable, despite the occasional differences in magnitude and local details when compared to the reference HLS-PhenoCam LSP product. The accuracy metrics indicated that the Transformer-based model produced overall higher accuracy than the HPLM-LSPD algorithm and the 1D-CNN model, with a correlation coefficient (R) of 0.74–0.88, a mean absolute difference (MAD) of 9.6–15 days, a root mean squared error (RMSE) of 13.5–20.6 days, and a mean systematic bias (MSB) of 0.7–4.7 days. The statistical analyses also showed that the Transformer-based model outperformed the HPLM-LSPD algorithm across all vegetation types and the HLS time series with different numbers of high-quality observations. Further, the accuracy of the Transformer-based model is contingent upon the proportion of high-quality observations. The model could achieve consistently high accuracy if the high-quality observations exceed 25 %. This study paved an effective way from traditional methodologies to machine learning methods for detecting phenological transition dates of vegetation development across various ecosystems.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"320 ","pages":"Article 114656"},"PeriodicalIF":11.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427878","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":"Revolutionizing crop phenotyping: Enhanced UAV LiDAR flight parameter optimization for wide-narrow row cultivation","authors":"Puchen Yan ,&nbsp;Yangming Feng ,&nbsp;Qisheng Han ,&nbsp;Hui Wu ,&nbsp;Zongguang Hu ,&nbsp;Shaozhong Kang","doi":"10.1016/j.rse.2025.114638","DOIUrl":"10.1016/j.rse.2025.114638","url":null,"abstract":"<div><div>This study introduces a method for optimizing flight modes using unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) technology, aiming for the efficient and accurate estimation of crop phenotypes in wide-narrow row planting patterns, for cotton. It proposes specialized flight plans that take into account the unique growth stages of cotton and recommends the s-along flight path, which is derived from a detailed analysis of the cross flight path, to facilitate effective and precise data collection. A comprehensive phenotypic index, labeled as ‘P', and a fitting function are developed to describe the relationship between flight parameters, paths, and digital elevation model (DEM) data. The study also introduces two flight strategies, one focusing on accuracy and the other on efficiency, utilizing a sophisticated multi-objective optimization method. Comparative analyses show that the s-along flight path significantly improves efficiency without sacrificing accuracy, compared to traditional cross flight path techniques. The use of high-precision prior DEM data greatly enhances the precision in estimating critical phenotypic parameters such as plant height (PH) and leaf area index (LAI), especially during key stages of canopy growth. By carefully adjusting flight height, speed, and overlap during different growth stages, an ideal balance is achieved between the precision and efficiency of data collection. These strategies markedly increase the accuracy of estimating phenotypic features (<em>P</em> &gt; 0.75) and efficiency (by 42 %–44 %). This research highlights the potential of these approaches in facilitating large-scale phenotypic data collection for precision agriculture, demonstrating their ability to simultaneously improve data quality and operational efficiency. Future research will aim to expand the applicability and robustness of these methods across various planting conditions and crops, further enhancing essential tools for the advancement of precision agriculture practices and development.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"320 ","pages":"Article 114638"},"PeriodicalIF":11.1,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417969","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}