JOURNAL OF NATURAL RESOURCES ›› 2016, Vol. 31 ›› Issue (5): 875-885.doi: 10.11849/zrzyxb.20150632

• Resource Research Method • Previous Articles     Next Articles

Agricultural Productivity Estimation with MODIS-OLI Fusion Data

NIU Zhong-en1, 2, YAN Hui-min1, HUANG Mei1, HU Yun-feng1, CHEN Jing-qing1   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2015-06-05 Revised:2015-11-27 Online:2016-05-20 Published:2016-05-19
  • Supported by:
    STS Project of CAS, No.KFJ-EW-STS-019; Key Program of the CAS, No.KSZD-EW-Z-021-02; National Natural Science Foundation of China, No.41430861

Abstract: Large-scale and high-precision of agricultural productivity monitoring depends on remote sensing with high spatio-temporal resolution. Remote sensing data with high spatial resolution or temporal resolution acquired by single type of sensor cannot meet the need of clearly monitoring dynamic crop growth on farmland parcel scale. MODIS data with 250-1 000 m spatial resolutions and Landsat data with 30 m spatial resolution are generally used to monitor vegetation dynamics. To supply the gaps of low spatial resolution of MODIS data and long revisit period of Landsat data, this study used Landsat 8 OLI data with 30 m spatial resolution and MODIS data with 500 m spatial resolution and 8-day temporal resolution as data sources, and adopted data fusion technique to fuse high spatial resolution of OLI data and high temporal resolution of MODIS data. Using this technology, the time-series data with 30 m spatial resolution and 8-day temporal resolution were acquired. We took Yongning County in Ningxia as the study area, and used VPM (Vegetation Photosynthesis Model) to estimate the NPP in this area. Results show that, there was high consistency between fused vegetation indexes and OLI vegetation indexes, the determination coefficient of EVI and LSWI being 0.70 and 0.51, respectively. Fused NPP data with 30 m spatial resolution has better detailed information. This data improves the estimated accuracy of mixed pixels in MODIS images, while retains original time and process information of MODIS data. Fused NPP data was consistent with the NPP obtained with MODIS data in pixels where farmland accounted for more than 30% of the mixed pixel, meanwhile fused NPP data was significantly higher than NPP calculated form MODIS data in pixels where farmland accounted for less than 30% of the mixed pixel, since fused NPP had distinct boundaries while NPP calculated from MODIS had not. Fused NPP data show the growing of crop with 30 m spatial resolution. Compared with studies that use MODIS data with 500 m resolution and MOD17 product with 1 000 m resolution, NPP data estimated by fused data can more effectively detect the promotion of agricultural productivity generated by high standard farmland construction. The difference between regions with high standard farmland construction and the neighbors calculated with MODIS-OLI data, VPN-MODIS data and MOD17 data were 62.66, 39.87 and 2.90 g C/(m2·a), respectively.

Key words: data fusion, ESTARFM, NPP, vegetation index, VPM model

CLC Number: 

  • TP79