Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

Abstract

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals (∆PR K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.synthesized vegetation index products have many applications in monitoring global climate change, material and energy cycles and the growth of surface vegetation. integrated the MODIS eight-day and 16-day products to quantitatively estimate green leaf area index (GLAI) over corn fields and evaluate the potential and limitations of the satellite data. Doraiswamy et al. [6] evaluated the applicability of the eight-day MODIS composite imagery in monitoring crop growth and yield estimation and the results showed that these images were very reliable. Sakamoto et al. [7,8] proposed a method which could identify the growth period of maize and soybean with two-step filtering based on MODIS data. However, the low spatial resolution (i.e., 250 m or 500 m) will cause a lot of errors by mixing multiple objects which have great differences in spectral curves and phenological characteristics in one pixel. Wang et al. [9] monitored and evaluated the growth condition of spring maize based on MODIS vegetation index products, MODIS LAI products and Global Land Surface Satellites LAI product. Moreover, the medium-resolution satellites (i.e., Landsat and GF-1) have developed rapidly and can well express the detailed featu...

Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images

“…Unfortunately, there is generally insufficient measurement information for the remote sensing deficiency resulting from cloud, smog, rain, etc. [45]. This issue can be studied further in future.…”

“…K k is Kalman gain, which effectively weights the prior state and the measurements by their respective uncertainties. P k is the uncertainties of the previous state and the present observation [15,25,45]. x…”

“…is the uncertainties of the previous state and the present observation [15,25,45]. Figure 4 illustrates the scheme of the KF approach for fusing Sentinel-2 and time-series MODIS images.…”

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

“…To enhance its generalization capacity, universal image representation is indispensable which aims to transform the source and the target images into a same space. For instance, Zhang et al [75] improved the Kalman filter and harmonized multi-source RS images for summer corn growth monitoring. Notably, generative adversarial network [59] has been used to align both panchromatic and multi-spectral images for data fusion [76].…”

Monitoring the Damage of Armyworm in Summer Corn by Unmanned Aerial Vehicle Imaging

“…Meigs et al [12] mapped tree mortality based on long time-series of Landsat images and forest inventory data and analyzed the spatiotemporal dynamics of insect pests. Unfortunately, the long revisit period, low spatial resolution and the requirements for clear weather of optical satellite images hampered the ability of monitoring armyworm infestations, which break out in short times [13,14]. Recently, unmanned aerial vehicles (UAV) with different sensors are increasingly being used in precision agriculture for monitoring crops growth condition [15][16][17].…”