The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2

被引:2
|
作者
Burger, Rogier [1 ]
Aouizerats, Benjamin [1 ]
den Besten, Nadja [1 ]
Guillevic, Pierre [1 ]
Catarino, Filipe [1 ]
van der Horst, Teije [1 ]
Jackson, Daniel [1 ]
Koopmans, Regan [1 ]
Ridderikhoff, Margot [1 ]
Robson, Greg [1 ]
Zajdband, Ariel [1 ]
de Jeu, Richard [1 ]
机构
[1] Planet Labs PBC, Wilhelminastr 43A, NL-2011 VK Haarlem, Netherlands
关键词
agricultural monitoring; crop biomass; fusion algorithm; radar; optical; field scale; vegetation index; remote sensing; VEGETATION WATER-CONTENT; CROPS; RADAR; SAR; CORN; CANOPIES; NDVI;
D O I
10.3390/rs16050835
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection.
引用
收藏
页数:27
相关论文
共 50 条
  • [31] A synergic method of Sentinel-1 and Sentinel-2 images for retrieving soil moisture content in agricultural regions
    Liang, Jiatan
    Liang, Guojian
    Zhao, Yanchun
    Zhang, Yechun
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 190
  • [32] A novel image fusion-based post classification framework for agricultural variations detection using Sentinel-1 and Sentinel-2 data
    Vyas, Narayan
    Singh, Sartajvir
    Sethi, Ganesh Kumar
    EARTH SCIENCE INFORMATICS, 2025, 18 (01)
  • [33] SENTINEL-1 & SENTINEL-2 DATA FOR SOIL TILLAGE CHANGE DETECTION
    Satalino, G.
    Mattia, F.
    Balenzano, A.
    Lovergine, F. P.
    Rinaldi, M.
    De Santis, A. P.
    Ruggieri, S.
    Nafria Garcia, D. A.
    Paredes Gomez, V.
    Ceschia, E.
    Planells, M.
    Le Toan, T.
    Ruiz, A.
    Moreno, J. F.
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 6627 - 6630
  • [34] Enhancing Peatland Classification using Sentinel-1 and Sentinel-2 Fusion with Encoder-Decoder Architecture
    Zelioli, Luca
    Farahnakian, Fahimeh
    Farahnakian, Farshad
    Middleton, Maarit
    Heikkonen, Jukka
    2024 27TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, FUSION 2024, 2024,
  • [35] Canonical Analysis of Sentinel-1 Radar and Sentinel-2 Optical Data
    Nielsen, Allan A.
    Larsen, Rasmus
    IMAGE ANALYSIS, SCIA 2017, PT II, 2017, 10270 : 147 - 158
  • [36] Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe
    Tarpanelli, Angelica
    Mondini, Alessandro C.
    Camici, Stefania
    NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2022, 22 (08) : 2473 - 2489
  • [37] Sentinel-1 and Sentinel-2 data fusion to distinguish building damage level of the 2018 Lombok Earthquake
    Putri, Ade Febri Sandhini
    Widyatmanti, Wirastuti
    Umarhadi, Deha Agus
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2022, 26
  • [38] Application of Sentinel-1 and Sentinel-2 data to conduct reconnaissance analyses
    Jenerowicz, Agnieszka
    Orych, Agata
    Siok, Katarzyna
    Smiarowski, Michal
    ELECTRO-OPTICAL REMOTE SENSING XIII, 2019, 11160
  • [39] SENTINEL-1 & SENTINEL-2 FOR SOIL MOISTURE RETRIEVAL AT FIELD SCALE
    Mattia, F.
    Balenzano, A.
    Satalino, G.
    Lovergine, F.
    Peng, J.
    Wegmuller, U.
    Cartus, O.
    Davidson, M. W. J.
    Kim, S.
    Johnson, J.
    Walker, J.
    Wu, X.
    Pauwels, V. R. N.
    McNairn, H.
    Caldwell, T.
    Cosh, M.
    Jackson, T.
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 6143 - 6146
  • [40] ESTIMATION OF SOIL MOISTURE USING SENTINEL-1 AND SENTINEL-2 IMAGES
    Sarteshnizi, R. Esmaeili
    Vayghan, S. Sahebi
    Jazirian, I.
    ISPRS GEOSPATIAL CONFERENCE 2022, JOINT 6TH SENSORS AND MODELS IN PHOTOGRAMMETRY AND REMOTE SENSING, SMPR/4TH GEOSPATIAL INFORMATION RESEARCH, GIRESEARCH CONFERENCES, VOL. 10-4, 2023, : 137 - 142