These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

138 related articles for article (PubMed ID: 30701108)

  • 1. Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms.
    Levitan N; Gross B
    Remote Sens (Basel); 2018; 10(12):1968. PubMed ID: 30701108
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index.
    Ji Z; Pan Y; Zhu X; Wang J; Li Q
    Sensors (Basel); 2021 Feb; 21(4):. PubMed ID: 33671356
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models.
    Levitan N; Kang Y; Özdoğan M; Magliulo V; Castillo P; Moshary F; Gross B
    Remote Sens (Basel); 2019 Aug; 11(16):1928. PubMed ID: 31534785
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean.
    Liao C; Wang J; Dong T; Shang J; Liu J; Song Y
    Sci Total Environ; 2019 Feb; 650(Pt 2):1707-1721. PubMed ID: 30273730
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions.
    Ban HY; Ahn JB; Lee BW
    PLoS One; 2019; 14(2):e0211874. PubMed ID: 30802254
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.
    Paciorek CJ; Liu Y;
    Res Rep Health Eff Inst; 2012 May; (167):5-83; discussion 85-91. PubMed ID: 22838153
    [TBL] [Abstract][Full Text] [Related]  

  • 7. An integrated approach of field, weather, and satellite data for monitoring maize phenology.
    Nieto L; Schwalbert R; Prasad PVV; Olson BJSC; Ciampitti IA
    Sci Rep; 2021 Aug; 11(1):15711. PubMed ID: 34344979
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Synergistic integration of optical and microwave satellite data for crop yield estimation.
    Mateo-Sanchis A; Piles M; Muñoz-Marí J; Adsuara JE; Pérez-Suay A; Camps-Valls G
    Remote Sens Environ; 2019 Dec; 234():111460. PubMed ID: 31798192
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation.
    Pan H; Chen Z; Allard W; Ren J
    Sensors (Basel); 2019 Jul; 19(14):. PubMed ID: 31323829
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2.
    Meroni M; d'Andrimont R; Vrieling A; Fasbender D; Lemoine G; Rembold F; Seguini L; Verhegghen A
    Remote Sens Environ; 2021 Feb; 253():112232. PubMed ID: 33536689
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products.
    Zheng Y; Wu B; Zhang M; Zeng H
    Sensors (Basel); 2016 Dec; 16(12):. PubMed ID: 27973404
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The use of remote sensing to derive maize sowing dates for large-scale crop yield simulations.
    Rezaei EE; Ghazaryan G; González J; Cornish N; Dubovyk O; Siebert S
    Int J Biometeorol; 2021 Apr; 65(4):565-576. PubMed ID: 33252716
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (
    Quirós Vargas JJ; Zhang C; Smitchger JA; McGee RJ; Sankaran S
    Sensors (Basel); 2019 Apr; 19(9):. PubMed ID: 31052251
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research.
    Liu R; Chen JM; Liu J; Deng F; Sun R
    J Environ Manage; 2007 Nov; 85(3):649-58. PubMed ID: 17123698
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Evaluating Crop Area Mapping from MODIS Time-Series as an Assessment Tool for Zimbabwe's "Fast Track Land Reform Programme".
    Hentze K; Thonfeld F; Menz G
    PLoS One; 2016; 11(6):e0156630. PubMed ID: 27253327
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Constraining chemical transport PM
    Friberg MD; Kahn RA; Limbacher JA; Appel KW; Mulholland JA
    Atmos Chem Phys; 2018 Jul; 18(17):12891-12913. PubMed ID: 30288162
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale.
    Skakun S; Vermote E; Roger JC; Franch B
    AIMS Geosci; 2017; 3(2):163-186. PubMed ID: 29888751
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level.
    Jiang H; Hu H; Zhong R; Xu J; Xu J; Huang J; Wang S; Ying Y; Lin T
    Glob Chang Biol; 2020 Mar; 26(3):1754-1766. PubMed ID: 31789455
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Drought imprints on crops can reduce yield loss: Nature's insights for food security.
    Fu P; Jaiswal D; McGrath JM; Wang S; Long SP; Bernacchi CJ
    Food Energy Secur; 2022 Feb; 11(1):e332. PubMed ID: 35846892
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images.
    Tang J; Zeng J; Zhang Q; Zhang R; Leng S; Zeng Y; Shui W; Xu Z; Wang Q
    Int J Biometeorol; 2020 Aug; 64(8):1273-1283. PubMed ID: 32266528
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.