Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

被引:48
|
作者
Onojeghuo, Alex O. [1 ]
Blackburn, George A. [1 ]
Wang, Qunming [1 ,2 ]
Atkinson, Peter M. [3 ,4 ,5 ]
Kindred, Daniel [6 ]
Miao, Yuxin [7 ]
机构
[1] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England
[2] Ctr Ecol & Hydrol, Lancaster LA1 4YQ, England
[3] Univ Lancaster, Fac Sci & Technol, Engn Bldg, Lancaster LA1 4YR, England
[4] Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England
[5] Queens Univ Belfast, Sch Geog Archaeol & Palaeoecol, Belfast BT7 1NN, Antrim, North Ireland
[6] ADAS UK Ltd, Cambridge CB23 4NN, England
[7] China Agr Univ, Coll Resource & Environm Sci, Dept Plant Nutr, Beijing 100094, Peoples R China
基金
英国科学技术设施理事会;
关键词
NDVI; MODIS; Landsat; downscaling; spatiotemporal fusion; VEGETATION PHENOLOGY; REFLECTANCE FUSION; SEASONAL-VARIATION; LEAF-AREA; NDVI DATA; CHINA; YIELD; PATTERNS; LANDSAT; SOUTH;
D O I
10.1080/15481603.2018.1423725
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.
引用
收藏
页码:659 / 677
页数:19
相关论文
共 50 条
  • [21] CROP PHENOLOGY STAGE FORECASTING AND DETECTION USING NDVI TIME-SERIES AND LSTM
    Katari, Sushma
    Bhowmik, Tapan K.
    Nair, Shabarinath S.
    Aravind, S.
    Nayak, Akasha R.
    Pankajakshan, Praveen
    [J]. 2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 6264 - 6267
  • [22] A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products
    Zeng, Linglin
    Wardlow, Brian D.
    Hu, Shun
    Zhang, Xiang
    Zhou, Guoqing
    Peng, Guozhang
    Xiang, Daxiang
    Wang, Rui
    Meng, Ran
    Wu, Weixiong
    [J]. REMOTE SENSING, 2021, 13 (07)
  • [23] Constructing MODIS LAI Time-Series Background Library Based on Temporal and Spatial Analysis of MODIS LAI Products
    Zhang, Huifang
    Shi, Runhe
    Liu, Chaoshun
    Gao, Wei
    [J]. REMOTE SENSING AND MODELING OF ECOSYSTEMS FOR SUSTAINABILITY VII, 2010, 7809
  • [24] Cropland abandonment mapping at sub-pixel scales using crop phenological information and MODIS time-series images
    Zhao, Xuan
    Wu, Taixia
    Wang, Shudong
    Liu, Kai
    Yang, Jingyu
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2023, 208
  • [25] Mapping Crop Cycles in China Using MODIS-EVI Time Series
    Li, Le
    Friedl, Mark A.
    Xin, Qinchuan
    Gray, Josh
    Pan, Yaozhong
    Frolking, Steve
    [J]. REMOTE SENSING, 2014, 6 (03) : 2473 - 2493
  • [26] Monitoring winter-wheat phenology in North China using time-series MODIS EVI
    Zhang, Mingwei
    Fan, Jinlong
    Zhu, Xiaoxiang
    Li, Guicai
    Zhang, Yeping
    [J]. REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XI, 2009, 7472
  • [27] MAPPING THE PLANTING DATES: AN EFFORT TO RETRIVE CROP PHENOLOGY INFORMATION FROM MODIS NDVI TIME SERIES IN AFRICA
    Guo, Zhe
    [J]. 2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 3281 - 3284
  • [28] Spatio-Temporal Reconstruction of MODIS NDVI by Regional Land Surface Phenology and Harmonic Analysis of Time-Series
    Padhee, Suman Kumar
    Dutta, Subashisa
    [J]. GISCIENCE & REMOTE SENSING, 2019, 56 (08) : 1261 - 1288
  • [29] A hybrid approach for detecting corn and soybean phenology with time-series MODIS data
    Zeng, Linglin
    Wardlow, Brian D.
    Wang, Rui
    Shan, Jie
    Tadesse, Tsegaye
    Hayes, Michael J.
    Li, Deren
    [J]. REMOTE SENSING OF ENVIRONMENT, 2016, 181 : 237 - 250
  • [30] Spatial-aware SAR-optical time-series deep integration for crop phenology tracking
    Zhao, Wenzhi
    Qu, Yang
    Zhang, Liqiang
    Li, Kaiyuan
    [J]. REMOTE SENSING OF ENVIRONMENT, 2022, 276