6d846b087d6d4d02a7cff8379f0f8945 remotesensing-18-02530-v2.pdf 331bb9011487eef9b3171d9178ca4a2bcd1e2959 remotesensing-18-02530-v2.pdf c42f216562207a7ad279105e5c8257db5c867e2707c5e91a66b50524067fee13 remotesensing-18-02530-v2.pdf Title: Vegetation Optical Depth at Enhanced Spatial Resolution: Progress, Challenges, and Perspectives Subject: Microwave satellite vegetation parameters are widely used to monitor ecosystem spatiotemporal dynamics. Among these, vegetation optical depth (VOD) stands out as a critical microwave vegetation indicator, widely used for applications such as monitoring crop yield, estimating carbon stocks, and assessing risks threatening forests and their resilience to them. However, current VOD products are available at a coarse resolution, often around tens of kilometers, limiting their usefulness to only large-scale applications or those that do not need high spatial precision. Nevertheless, although extensive research has been conducted to improve the spatial resolution of several geophysical indicators, such as soil moisture, advances have been scarce for VOD. Here, we review the advances conducted to estimate VOD at medium-high spatial resolutions, overviewing the state of the art of different methods and their potentials and limitations. Basedon the available literature, we propose a taxonomy that classifies existing VOD downscaling approaches into proxy-based methods, which exploit the relationship between VOD and auxiliary variables, and data-fusion strategies that combine complementary microwave observations across sensors and frequencies. Also, we synthesize the suitability of different proxy variables according to the VOD frequency band, showing that while optical vegetation indices perform well for high-frequency VOD, the downscaling of low-frequency VOD benefits from the integration of complementary radar-derived proxies. Additionally, we examine the effectiveness and affordability of current validation methods and review the potential of emerging ones, such as GNSS technology and land surface models, to guarantee the reliable quality of future VOD downscaled products. Finally, we highlight the capabilities of future missions, particularly the upcoming CIMR multiresolution capability across frequency bands, which could considerably aid in obtaining VOD at better spatial scales. Keywords: microwave remote sensing; vegetation optical depth; spatial resolution; downscaling algorithms Author: Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25; modified using OpenPDF 1.4.2 CreationDate: Tue Aug 4 05:57:31 2026 CEST ModDate: Tue Aug 4 06:05:55 2026 CEST Custom Metadata: yes Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 27 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2693347 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- HCJGWZ+URWPalladioL-Roma Type 1 Custom yes yes yes 117 0 KEOFZP+URWPalladioL-Ital Type 1 Custom yes yes yes 118 0 WOFEOA+URWPalladioL-Bold Type 1 Custom yes yes yes 119 0 SYFPBV+CMMI10 Type 1 Builtin yes yes yes 169 0 BTIABM+PazoMath-Italic Type 1 Builtin yes yes yes 231 0 IMMXGH+CMSY10 Type 1 Builtin yes yes yes 232 0 BCDEEE+Calibri-Bold TrueType WinAnsi yes yes no 218 0 BCDFEE+Calibri-BoldItalic TrueType WinAnsi yes yes no 219 0 BCDEEE+Calibri TrueType WinAnsi yes yes no 256 0 GIGFZE+CMR10 Type 1 Builtin yes yes yes 469 0 WMGRYN+CMEX10 Type 1 Builtin yes yes yes 470 0 BCDEEE+Calibri TrueType WinAnsi yes yes no 587 0 BCDFEE+Calibri-Italic TrueType WinAnsi yes yes no 588 0 BBETNX+PazoMath Type 1 Builtin yes yes yes 657 0 NETBHX+EURM10 Type 1 Builtin yes yes yes 734 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2026-09-30 03:56:49 CEST RepresentationInformation: remotesensing-18-02530-v2.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2026-09-29 14:45:46 CEST Size: 2693347 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 961 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Item: Title: VOD Retrievals Children: Item: Title: VOD from Radiometric Systems Item: Title: VOD from GNSS Reflectometry and Transmissometry Item: Title: VOD from Active Observations Item: Title: VOD Downscaling Methods Children: Item: Title: Correlation Between VOD and Proxy Indicators for Downscaling Applications Children: Item: Title: Optical Indices Item: Title: Radar Backscatter Item: Title: Other Potential Variables for VOD Downscaling Item: Title: VOD Estimation Derived from Disaggregated Brightness Temperatures Children: Item: Title: Active–Passive Algorithms Item: Title: Multifrequency Data Fusion Techniques Item: Title: Validation Methods of Disaggregated VOD Products Item: Title: Conclusions and Future Directions Item: Title: References Info: Title: Vegetation Optical Depth at Enhanced Spatial Resolution: Progress, Challenges, and Perspectives Author: Subject: Microwave satellite vegetation parameters are widely used to monitor ecosystem spatiotemporal dynamics. Among these, vegetation optical depth (VOD) stands out as a critical microwave vegetation indicator, widely used for applications such as monitoring crop yield, estimating carbon stocks, and assessing risks threatening forests and their resilience to them. However, current VOD products are available at a coarse resolution, often around tens of kilometers, limiting their usefulness to only large-scale applications or those that do not need high spatial precision. Nevertheless, although extensive research has been conducted to improve the spatial resolution of several geophysical indicators, such as soil moisture, advances have been scarce for VOD. Here, we review the advances conducted to estimate VOD at medium-high spatial resolutions, overviewing the state of the art of different methods and their potentials and limitations. Basedon the available literature, we propose a taxonomy that classifies existing VOD downscaling approaches into proxy-based methods, which exploit the relationship between VOD and auxiliary variables, and data-fusion strategies that combine complementary microwave observations across sensors and frequencies. Also, we synthesize the suitability of different proxy variables according to the VOD frequency band, showing that while optical vegetation indices perform well for high-frequency VOD, the downscaling of low-frequency VOD benefits from the integration of complementary radar-derived proxies. Additionally, we examine the effectiveness and affordability of current validation methods and review the potential of emerging ones, such as GNSS technology and land surface models, to guarantee the reliable quality of future VOD downscaled products. Finally, we highlight the capabilities of future missions, particularly the upcoming CIMR multiresolution capability across frequency bands, which could considerably aid in obtaining VOD at better spatial scales. 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