Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice

Abstract

Abstract:The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the world's first Geostationary Ocean Color Imager (GOCI) were used for spectral analyses of paddy rice in South Korea. A vegetation index was calculated from GOCI data based on the bidirectional reflectance distribution function (BRDF)-adjusted reflectance, which was then used to visually analyze the seasonal crop dynamics. These vegetation indices were then compared with those calculated using the Moderate-resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) based on Nadir BRDF-adjusted reflectance. The results show clear advantages of GOCI, which provided four times better temporal resolution than the combined MODIS sensors, interpreting subtle characteristics of the vegetation development. Particularly in the rainy season, when data acquisition under clear weather conditions was very limited, it was possible to find cloudless pixels within the study sites by compiling GOCI images obtained from eight acquisition periods per day, from which the vegetation index could be calculated. In this study, ground spectral measurements from CROPSCAN were also compared with satellite-based vegetation products, despite their different index magnitude, according to systematic discrepancy, showing a similar crop development pattern to the GOCI products. Consequently, we conclude that the very high temporal resolution of GOCI is very beneficial for monitoring crop development, and has potential for providing improved information on phenology. OPEN ACCESSRemote Sens. 2015, 7 11327

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

“…Smoothing or curve fitting methods have been applied by using various statistical algorithms based on spectral information [10][11][12]. Although all of these approaches have been proven to be practically applicable, it is difficult to ensure reliable imagery data in the case of prolonged cloudy days [13,14].…”

“…These have a higher temporal resolution than Earth orbit satellite sensors such as MODIS and AVHRR due to their continuous observation characteristic in specified regions [15,16]. This characteristic can increase the chances for obtaining clear satellite imagery by acquiring many images in a single day than polar orbit satellites [14,17]. However, the imageries from these types of satellite sensors have rarely been used to estimate crop yield due to a low spatial resolution and insufficient spectral bands for monitoring crop spectral information.…”

“…However, the imageries from these types of satellite sensors have rarely been used to estimate crop yield due to a low spatial resolution and insufficient spectral bands for monitoring crop spectral information. On the other hand, the Communication, Ocean, and Meteorological Satellite (COMS), which is one of the geostationary orbit satellites that observe the sea around the Korean Peninsula, is equipped with multi-spectral sensors that can monitor spectral crop information with a moderate spatial resolution of 500 m. The previous study compared Geostationary Ocean Color Imager (GOCI) products from COMS with MODIS images [14]. They showed that the COMS data had a more stable seasonal variation and recurrent image acquisition with less cloud cover than the MODIS data during a paddy growing season.…”

An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea

“…Among the atmospheric constituents, AOD has more variation and makes a critical contribution to the atmospheric effects when estimating surface reflectance. Based on ground station particulate matter 2.5 data, the overall root‐mean‐square error (RMSE) of the AOD during the daytime was 0.123 according to Green et al [], indicating that the expected error in the surface reflectance using the MODIS daily AOD would be less than 3% in the 6S radiative transfer model [ Yeom and Kim , ]. The remaining atmospheric constituents, including water vapor and total ozone, made less contribution to the atmospheric effects.…”

Preface: Recent Advances in Remote Sensing for Crop Growth Monitoring

“…The use of spectral indices for nitrogen status monitoring and diagnosis of rice crops at field scale is illustrated by Huang et al [4] with FORMOSAT-2 satellite imagery. With more frequent samplings, spectral indices can be used to optimize the estimation of fruit yield and quality [5] and to improve the assessment of crop temporal dynamics [6]. Spectral indices are also employed by Guo et al [7] for characterizing canopy structure and light radiation at different depths within the canopy for rice crops.…”