Science of Remote Sensing最新文献

{"title":"Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges","authors":"Shunlin Liang ,&nbsp;Tao He ,&nbsp;Jianxi Huang ,&nbsp;Aolin Jia ,&nbsp;Yuzhen Zhang ,&nbsp;Yunfeng Cao ,&nbsp;Xiaona Chen ,&nbsp;Xidong Chen ,&nbsp;Jie Cheng ,&nbsp;Bo Jiang ,&nbsp;Huaan Jin ,&nbsp;Ainong Li ,&nbsp;Siwei Li ,&nbsp;Xuecao Li ,&nbsp;Liangyun Liu ,&nbsp;Xiaobang Liu ,&nbsp;Han Ma ,&nbsp;Yichuan Ma ,&nbsp;Dan-Xia Song ,&nbsp;Lin Sun ,&nbsp;Liulin Song","doi":"10.1016/j.srs.2024.100152","DOIUrl":"10.1016/j.srs.2024.100152","url":null,"abstract":"<div><p>For many applications, raw satellite observations need to be converted to high-level products of various essential environmental variables. While numerous products are available at kilometer spatial resolutions, there are few global products at high spatial resolutions (10–30 m), which are also referred to fine or medium resolutions in the literature. To facilitate the development of more high spatial resolution products, this paper systematically reviews the state-of-the-art progress on inversion algorithms and publicly available regional and global products. We begin with an inventory of available high-resolution satellite data, and then present different algorithms for determining cloud masks, estimating aerosol optical depth, and performing atmospheric correction and topographic correction for land surface reflectance retrieval. The majority of this paper reviews the inversion algorithms and existing regional to global products of 18 variables in four major categories: 1) Land surface radiation, including broadband albedo, land surface temperature, and all-wave net radiation; 2) Terrestrial ecosystem variables, including leaf area index, fraction of absorbed photosynthetically active radiation, fractional vegetation cover, fractional forest cover, tree height, forest above-ground biomass gross primary production, net primary production, and agricultural crop yield; 3) Water cycle and cryosphere, including soil moisture, evapotranspiration, and snow cover; and 4) Land surface types, such as global land cover, impervious surface, inland water, crop type, and fire. Since the existing products over large regions are usually spatially discontinuous due to cloud contamination, different data fusion and data assimilation algorithms and some products for producing spatially seamless and temporally continuous products are presented. In the end, we discuss a variety of challenges in generating global high spatial resolution satellite products.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100152"},"PeriodicalIF":5.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000361/pdfft?md5=6c8abeea2014d16908b1a59b3d7b3275&pid=1-s2.0-S2666017224000361-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting tree species composition using airborne laser scanning and multispectral data in boreal forests","authors":"Jaime Candelas Bielza ,&nbsp;Lennart Noordermeer ,&nbsp;Erik Næsset ,&nbsp;Terje Gobakken ,&nbsp;Johannes Breidenbach ,&nbsp;Hans Ole Ørka","doi":"10.1016/j.srs.2024.100154","DOIUrl":"10.1016/j.srs.2024.100154","url":null,"abstract":"<div><p>Tree species composition is essential information for forest management and remotely sensed (RS) data have proven to be useful for its prediction. In forest management inventories, tree species are commonly interpreted manually from aerial images for each stand, which is time and resource consuming and entails substantial uncertainty. The objective of this study was to evaluate a range of RS data sources comprising airborne laser scanning (ALS) and airborne and satellite-borne multispectral data for model-based prediction of tree species composition. Total volume was predicted using non-linear regression and volume proportions of species were predicted using parametric Dirichlet models. Predicted dominant species was defined as the species with the greatest predicted volume proportion and predicted species-specific volumes were calculated as the product of predicted total volume multiplied by predicted volume proportions. Ground reference data obtained from 1184 sample plots of 250 m² in eight districts in Norway were used. Combinations of ALS and two multispectral data sources, i.e. aerial images and Sentinel-2 satellite images from different seasons, were compared. The most accurate predictions of tree species composition were obtained by combining ALS and multi-season Sentinel-2 imagery, specifically from summer and fall. Independent validation of predicted species proportions yielded average root mean square differences (RMSD) of 0.15, 0.15 and 0.07 (relative RMSD of 30%, 68% and 128%) and squared Pearson's correlation coefficient (r²) of 0.74, 0.79 and 0.51 for Norway spruce (<em>Picea abies</em> (L.) Karst.), Scots pine (<em>Pinus sylvestris</em> L.) and deciduous species, respectively. The dominant species was predicted with median values of overall accuracy, quantity disagreement and allocation disagreement of 0.90, 0.07 and 0.00, respectively. Predicted species-specific volumes yielded average values of RMSD of 63, 48 and 23 m³/ha (relative RMSD of 39%, 94% and 158%) and r² of 0.84, 0.60 and 0.53 for spruce, pine and deciduous species, respectively. In one of the districts with independent validation plots of mean size 3700 m², predictions of the dominant species were compared to results obtained through manual photo-interpretation. The model predictions gave greater accuracy than manual photo-interpretation. This study highlights the utility of RS data for prediction of tree species composition in operational forest inventories, particularly indicating the utility of ALS and multi-season Sentinel-2 imagery.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100154"},"PeriodicalIF":5.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000385/pdfft?md5=d681df750ffcb4aad15b1ca7a324f9ae&pid=1-s2.0-S2666017224000385-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop yield estimation at different growing stages using a synergy of SAR and optical remote sensing data","authors":"Natacha I. Kalecinski ,&nbsp;Sergii Skakun ,&nbsp;Nathan Torbick ,&nbsp;Xiaodong Huang ,&nbsp;Belen Franch ,&nbsp;Jean-Claude Roger ,&nbsp;Eric Vermote","doi":"10.1016/j.srs.2024.100153","DOIUrl":"10.1016/j.srs.2024.100153","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Crop yield forecasting is an essential component of crop production assessment, impacting people at the global scale down to the level of individual farms. Until now, yield forecasting has predominantly relied on optical data, particularly the maximum value of vegetation indexes. However, this approach only presents a short forecasting window, and it is essential to obtain yield estimates as early as possible in the growing season and then further improve forecasting even after the vegetation index has reached its peak. So far, optical satellite data at high-temporal resolution (1–3 days) has been actively used for real time crop yield monitoring, whereas fewer operational models make a use of synthetic aperture radar (SAR). In this study, we explore whether SAR data can capture distinct aspects of crop dynamics, providing new insights for yield estimation depending on the crop's phenological stage. We assess the efficiency of dual- (Sentinel-1) and quad-polarimetric (UAVSAR, RADARSAT-2) data to explain inter-field crop yield variability for corn, soybean, and rice over a test area in Arkansas, US (258 fields, 2019). We used optical imagery acquired by Planet/Dove-Classic, Sentinel-2, and Landsat 8, to establish a baseline performance of satellite-based indicators to explain yield variability and assess dual- and quad-polarimetric SAR data for crop yield assessment. In terms of polarimetric indexes, the results showed that in general the results for rice were mostly stable and better than the other crops (R&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;adj&lt;/sub&gt; ∼ 0.4 on average). The best results were obtained for the Sentinel-1 VH&lt;sub&gt;asc&lt;/sub&gt; with R&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;adj&lt;/sub&gt; = 0.47 and RADARSAT-2 phase difference with R&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;adj&lt;/sub&gt; = 0.45. The results for corn performed the least with an R&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;adj&lt;/sub&gt; &lt;0.35 for all the indexes. The results for soybeans were more variable and were highly correlated with certain indicators such as RADARSAT-2 HV, RADARSAT-2 Volume, and RADARSAT-2 Pauli HV with R&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;adj&lt;/sub&gt;&gt;0.4. We also investigated the day of year (DOY) with the maximum correlation between optical and SAR-derived features and the final yields for corn, soybean, and rice. The maximum correlation for optical features occurs over a short time between DOY 155 (June 4) and 185 (July 5) for corn and rice, and DOY 190 (July 9) and DOY 211 (July 30) for soybean, with these results being consistent across various optical-based sensors. On the contrary, the maximum correlation for SAR-derived features varied significantly and was between DOY 120 (April 30) to DOY 225 (August 13). A study of the time series parameters cross-correlation showed that the optical parameters were highly correlated, but the SAR parameters showed strong temporal decorrelation. We conducted a comparison between C-band and L-band to assess their sensitivity at each stage of growth. In this experiment, we determined that for low vegetation, the C band will","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100153"},"PeriodicalIF":5.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000373/pdfft?md5=8094bda8c58d9c01f3e5b0c68ccff1c2&pid=1-s2.0-S2666017224000373-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OptiSAIL: A system for the simultaneous retrieval of soil, leaf, and canopy parameters and its application to Sentinel-3 Synergy (OLCI+SLSTR) top-of-canopy reflectances","authors":"Simon Blessing ,&nbsp;Ralf Giering ,&nbsp;Christiaan van der Tol","doi":"10.1016/j.srs.2024.100148","DOIUrl":"10.1016/j.srs.2024.100148","url":null,"abstract":"<div><p>This paper describes the selected algorithm for the ESA climate change initiative vegetation parameters project. Multi- and hyper-spectral, multi-angular, or multi-sensor top-of-canopy reflectance data call for an efficient generic retrieval system which can improve the consistent retrieval of standard canopy parameters as albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (<em>f</em>APAR) and their uncertainties, and exploit the information to retrieve additional parameters (e.g. leaf pigments). We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL (canopy reflectance), PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (soil reflectance anisotropy, moisture effect), and a cloud contamination model. The inversion is gradient based and uses codes created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/<em>f</em>APAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. The system is computationally efficient with a rate of 150 pixel s⁻¹ (7 ms per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals and puts real-time processing with this kind of system into reach.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100148"},"PeriodicalIF":5.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000324/pdfft?md5=b8326fb2acbe63e32f1a4a2583c6730e&pid=1-s2.0-S2666017224000324-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping rice-crayfish co-culture (RCC) fields with Sentinel-1 and -2 time series in China's primary crayfish production region Jianghan Plain","authors":"Wenxia Tan ,&nbsp;Xingcheng Wang ,&nbsp;Lin Yan ,&nbsp;Jun Yi ,&nbsp;Tian Xia ,&nbsp;Zhe Zeng ,&nbsp;Gongliang Yu ,&nbsp;Min Chai ,&nbsp;Naga Manohar Velpuri ,&nbsp;Apichaya Thaneerat","doi":"10.1016/j.srs.2024.100151","DOIUrl":"10.1016/j.srs.2024.100151","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Crayfish is a high-risk invasive species with devastating impacts on freshwater ecosystems. Meanwhile, nicknamed “little lobster”, it is a popular food in many countries including China. The crayfish production in China increased from 1.13 to 2.39 million tons in 2017–2020, accounting for 97% global production. This phenomenal increase is attributed to the expansion of the rice-crayfish co-culture (RCC) farming mode whose area increased by 123% from 0.57 to 1.26 million ha in 2017–2020. However, the fast expansion of RCC is undertaken in an uncontrolled and unregulated manner, referred by some researchers as a “blind expansion”. It raises wide concerns on ecological risks (crayfish can escape in high-magnitude floods), endangerment of riverbanks (crayfish burrows), food security (reduced rice production), excessive water consumption, and greenhouse gas (methane) emission. It is thus urgent to accurately map the spatial distributions of RCC fields using satellite remote sensing data, so as to assess the ecological and environmental impacts and risks, and to better regulate the expansion. However, there are currently no practically-scalable approaches to reliably map RCC fields in large areas. In particular, there lack the knowledge on the relationship between satellite observations and on-ground biophysical processes in RCC fields. In this study, we conducted field surveys in RCC fields, and in particular, the daily water levels in RCC fields were measured for the complete year of 2020. The comparison of annual water-level time series and satellite-NDVI time series, combined with the RCC farming information collected in surveys, reveals how satellite observations vary in correspondences to on-ground biophysical processes in RCC fields; and importantly, it provides information on how RCC fields can be efficiently distinguished from other land covers using satellite data. Based on that, we propose an approach to map RCC fields from annual Sentinel-2 optical-wavelength and Sentinel-1 Synthetic Aperture Radar (SAR) time series, utilizing the annual water-occurrence frequency (AWF) and characteristic phenological features derived from the satellite data. This method was demonstrated in Jianghan Plain, the primary crayfish production region in China with an area of approximately 37,000 km&lt;sup&gt;2&lt;/sup&gt;. A total of 273,365 ha (2733.65 km&lt;sup&gt;2&lt;/sup&gt;) RCC field area in year 2020 was mapped, which accounted for 24.6% of the whole plain's cropland area (approximately 11,100 km&lt;sup&gt;2&lt;/sup&gt;), meaning a significant proportion of the rice paddies were converted to RCC fields. The RCC mapping accuracies were validated using the samples collected in field surveys and also from Google Earth images, and was compared with the state-of-practice RCC mapping method using bi-seasonal optical-wavelength satellite images. The proposed method obtained 93.8% overall accuracy and 0.91 kappa coefficient, and outperformed the compared bi-seasonal method. The proposed met","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100151"},"PeriodicalIF":5.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266601722400035X/pdfft?md5=891ee11d03709fe67ca534d000f73304&pid=1-s2.0-S266601722400035X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards global spaceborne lidar biomass: Developing and applying boreal forest biomass models for ICESat-2 laser altimetry data","authors":"A. Neuenschwander ,&nbsp;L. Duncanson ,&nbsp;P. Montesano ,&nbsp;D. Minor ,&nbsp;E. Guenther ,&nbsp;S. Hancock ,&nbsp;M.A. Wulder ,&nbsp;J.C. White ,&nbsp;M. Purslow ,&nbsp;N. Thomas ,&nbsp;A. Mandel ,&nbsp;T. Feng ,&nbsp;J. Armston ,&nbsp;J.R. Kellner ,&nbsp;H.E. Andersen ,&nbsp;L. Boschetti ,&nbsp;P. Fekety ,&nbsp;A. Hudak ,&nbsp;J. Pisek ,&nbsp;N. Sánchez-López ,&nbsp;K. Stereńczak","doi":"10.1016/j.srs.2024.100150","DOIUrl":"10.1016/j.srs.2024.100150","url":null,"abstract":"<div><p>Space-based laser altimetry has revolutionized our capacity to characterize terrestrial ecosystems through the direct observation of vegetation structure and the terrain beneath it. Data from NASA's ICESat-2 mission provide the first comprehensive look at canopy structure for boreal forests from space-based lidar. The objective of this research was to create ICESat-2 aboveground biomass density (AGBD) models for the global entirety of boreal forests at a 30 m spatial resolution and apply those models to ICESat-2 data from the 2019–2021 period. Although limited in dense canopy, ICESat-2 is the only space-based laser altimeter capable of mapping vegetation in northern latitudes. Along each ICESat-2 orbit track, ground and vegetation height is captured with additional modeling required to characterize biomass. By implementing a similar methodology of estimating AGBD as GEDI, ICESat-2 AGBD estimates can complement GEDI's estimates for a full global accounting of aboveground carbon. Using a suite of field measurements with contemporaneous airborne lidar data over boreal forests, ICESat-2 photons were simulated over many field sites and the impact of two methods of computing relative height (RH) metrics on AGBD at a 30 m along-track spatial resolution were tested; with and without ground photons. AGBD models were developed specifically for ICESat-2 segments having land cover as either Evergreen Needleleaf or Deciduous Broadleaf Trees, whereas a generalized boreal-wide AGBD model was developed for ICESat-2 segments whose land cover was neither. Applying our AGBD models to a set of over 19 million ICESat-2 observations yielded a 30 m along-track AGBD product for the pan-boreal. The ability demonstrated herein to calculate ICESat-2 biomass estimates at a 30 m spatial resolution provides the scientific underpinning for a full, spatially explicit, global accounting of aboveground biomass.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100150"},"PeriodicalIF":5.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000348/pdfft?md5=661b8ab3a5093b4544ae59dcd82bf5f6&pid=1-s2.0-S2666017224000348-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence of climate change - Investigating glacial terminus and lake inventory using earth observation data for mountainous Bhutan","authors":"Bhartendu Sajan ,&nbsp;Shruti Kanga ,&nbsp;Suraj Kumar Singh ,&nbsp;Praveen Kumar Rai ,&nbsp;Bojan Đurin ,&nbsp;Vlado Cetl ,&nbsp;Upaka Rathnayake","doi":"10.1016/j.srs.2024.100149","DOIUrl":"10.1016/j.srs.2024.100149","url":null,"abstract":"<div><p>The mapping and monitoring of different types of Glacial lakes through the Geospatial techniques is vital to show the impact of climate changes on the Glacier and alleviate hazards that result from the bursting of Glacial Lakes and cause catastrophic consequences to human lives. The main goal of the present work was to map and analyze different types of glacial lakes in Bhutan during the years 1990, 2000, and 2017. Several sets of satellite images, Landsat-TM for 1990, Landsat ETM + for 2000, and Landsat 8-OLI satellite image for 2017, were used to estimate the changes in the glacial lakes and the inventory study. Several glacial lakes, i.e., moraine-dammed lake, supra glacial lake, lateral moraine lake, erosional lake, medial moraine lake, and end moraine lake, were mapped within these periods. It was found that there was a rapid increase in glacial lakes from 1990 to 2017. The number of glacial lakes in 1990 was increased from 213 to 436 in 2017. It was also observed that the spatial dimensions of some of the glacial lakes increased. The study revealed five end moraine lakes, 40 lateral moraine lakes, 50 supra glacial lakes, 239 erosional lakes, and 15 other moraines dammed lakes in 2017.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100149"},"PeriodicalIF":5.7,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000336/pdfft?md5=2838e984abc73e33a2ce85dc1e66a380&pid=1-s2.0-S2666017224000336-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the uncertainties of satellite derived soil moisture at global scale","authors":"François Gibon ,&nbsp;Arnaud Mialon ,&nbsp;Philippe Richaume ,&nbsp;Nemesio Rodríguez-Fernández ,&nbsp;Daniel Aberer ,&nbsp;Alexander Boresch ,&nbsp;Raffaele Crapolicchio ,&nbsp;Wouter Dorigo ,&nbsp;Alexander Gruber ,&nbsp;Irene Himmelbauer ,&nbsp;Wolfgang Preimesberger ,&nbsp;Roberto Sabia ,&nbsp;Pietro Stradiotti ,&nbsp;Monika Tercjak ,&nbsp;Yann H. Kerr","doi":"10.1016/j.srs.2024.100147","DOIUrl":"https://doi.org/10.1016/j.srs.2024.100147","url":null,"abstract":"<div><p>This study attempts to derive the uncertainty of the soil moisture estimation from passive microwave satellite mission at global scale. To do so, the approach is based on the sensitivity of the Soil Moisture and Ocean Salinity (SMOS) soil moisture retrieval quality to the land surface characteristics within its footprint (presence of forest, topography, open water bodies, sand, clay, bulk density and soil organic carbon content). First, we performed a global assessment of SMOS using <em>in situ</em> measurements from the International Soil Moisture Network (ISMN) as reference, with more than 1900 ISMN stations and 10 years of SMOS data. This assessment shows that the ubRMSD scores vary greatly between locations (with a mean of 0.074 m³m⁻³ and an interquartile range of 0.030 m³m⁻³). Second, the scores are analyzed for different surface conditions within the satellite footprint. The best agreement between the ground measurement and SMOS time series are obtained for low forest cover, low topographic complexity, and marginal presence of open water bodies within the SMOS footprint. Soil parameters also have an impact, with better scores for sandier soils with a high bulk-density and low soil organic carbon content. Finally, we propose to extrapolate the obtained relationships, using a multiple linear regression, in order to derive a global map of SMOS uncertainties based on surface conditions. This map of predicted uncertainties show a diverse range of ubRMSD values across the globe (with a mean of 0.076 m³m⁻³ and an interquartile range of 0.031 m³m⁻³) depending on the surface characteristics. At the ISMN site location, the predicted ubRMSD shows similar results than the comparison between SMOS and the <em>in situ</em> measurements. The map of predicted SMOS ubRMSD represents an upper bound estimate of the SMOS uncertainty, as it includes the uncertainties of the <em>in situ</em> sensor measurements and the scale mismatch. Further investigations will focus on the different components of this uncertainty budget to obtain a better assessment of the absolute uncertainties of SMOS soil moisture retrievals across the globe.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100147"},"PeriodicalIF":5.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000312/pdfft?md5=ef45d7efb157d210212aa9b323c36eb6&pid=1-s2.0-S2666017224000312-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model","authors":"Jianxi Huang ,&nbsp;Jianjian Song ,&nbsp;Hai Huang ,&nbsp;Wen Zhuo ,&nbsp;Quandi Niu ,&nbsp;Shangrong Wu ,&nbsp;Han Ma ,&nbsp;Shunlin Liang","doi":"10.1016/j.srs.2024.100146","DOIUrl":"https://doi.org/10.1016/j.srs.2024.100146","url":null,"abstract":"<div><p>Combining the advantages of crop growth models and remote sensing observations, data assimilation (DA) has emerged as a vital tool for crop growth monitoring and early-season crop yield forecasting. As an increasing number of related studies have been conducted, data assimilation systems for remote sensing and crop growth models have grown increasingly sophisticated. However, within this context, the research on data assimilation algorithms, as a core component of data assimilation system, highly need investigating the potential. In this review, we discuss the essential differences and inherent connections of various data assimilation algorithms based on Bayes's Theorem. Building upon this foundation, we review the application progress of different DA algorithms data assimilation of remote sensing and crop models. Additionally, we identify the challenges and limitations faced by current data assimilation algorithms in crop practical applications and propose potential directions for future study. As a summary of the entire paper, we provide recommendations for DA algorithm choice strategy in conjunction with specific application scenarios.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100146"},"PeriodicalIF":5.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000300/pdfft?md5=05e3476625e6d564bd2770f5c9be340e&pid=1-s2.0-S2666017224000300-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting the 2023 wildfire season in Canada","authors":"Flavie Pelletier ,&nbsp;Jeffrey A. Cardille ,&nbsp;Michael A. Wulder ,&nbsp;Joanne C. White ,&nbsp;Txomin Hermosilla","doi":"10.1016/j.srs.2024.100145","DOIUrl":"https://doi.org/10.1016/j.srs.2024.100145","url":null,"abstract":"<div><p>The area burned by wildfires in Canada in 2023 is unprecedented in historical records. To help ensure the safety of communities and support the mobilization of firefighting resources, rapid detection of areas affected by wildfires is required. Satellite data are ideally suited to provide near real-time wildfire information over large areas. At the same time, clouds, smoke, and haze can obscure the collection of observations from sensors typically used for mapping purposes. Established methods using coarse spatial resolution satellites (e.g., MODIS, VIIRS) rely upon the combination of daily revisit to enable the rapid and reliable detection of large active fires, in full or in part, and the application of modeling (including spatial buffering) to infer additional, yet still obscured, areas. While timely, these initial maps of wildfire-impacted areas do not capture small fires (those smaller than 200 ha) and, importantly, are not intended to differentiate unburned areas within fire perimeters. To address these limitations, we used data from Sentinel-2A and -2B, and Landsat-8 and -9, which form a virtual constellation of four satellites to revisit and map burned area in Canada's forested ecosystems for the 2023 fire season. Availing upon the high temporal data density and using the Tracking Intra- and Inter-year Change algorithm (TIIC), an aggregate seasonal mapping of wildfires resulted in a total area affected by wildfires in 2023 of 12.74 Mha. Within this total area, 9.51 Mha of treed land cover was impacted. Shrubs and wetlands comprised most of the remaining non-treed area that was burned. Using a 2022 map of aboveground treed biomass (AGB), approximately 0.649 Pg of AGB was impacted by 2023 wildfires, representing an 11-fold increase in AGB impacts relative to a long-term annual average of treed AGB loss. Differences between the estimate of total burned area reported herein and the total burned area indicated by the Natural Resources Canada (NRCan) Fire M3 hotspot fire perimeters (18.64 Mha) were analyzed. Overall, estimates of burned area differed by 5.9 Mha, including over 1.13 Mha of water identified as burned within the NRCan perimeters. Differences in land cover and AGB impacts between the two products were also investigated and quantified. TIIC enables the near-continuous capture of areas impacted by fire through the fire season, allowing for within-year refinement of total burned area, rapid interrogation of land cover types impacted, and estimation of associated biomass consequences.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100145"},"PeriodicalIF":5.7,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000294/pdfft?md5=34c749dfaa5e4a6a360e818d201b0a7a&pid=1-s2.0-S2666017224000294-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}