Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages

Abstract

For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red (R), and near-infrared (NIR) wavebands have been used to estimate rice N status due to its high spatial resolution, daily revisit capability, and relatively lower cost. This study aimed to evaluate the potential improvements of RapidEye and WorldView-2 data over FORMOSAT-2 for rice N status monitoring, as the former two sensors provide additional wavelengths besides the traditional four wavebands. Ten site-year N rate experiments were conducted in Jiansanjiang, Heilongjiang Province of Northeast China from 2008 to 2011. Plant samples and field hyperspectral data were collected at three growth stages: panicle initiation (PI), stem elongation (SE), and heading (HE). The canopy-scale hyperspectral data were upscaled to simulate the satellite bands. Vegetation index (VI) analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to derive plant N status indicators. The results indicated that the best-performed VIs calculated from the simulated RapidEye and WorldView-2 bands, especially those based on the red edge (RE) bands, explained significantly more variability for above ground biomass (AGB), plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations than their FORMOSAT-2-based counterparts did, especially at the PI and SE stages. The SMLR and PLSR models based on the WorldView-2 bands generally had the best performance, followed by the ones based on the RapidEye bands. The SMLR results revealed that both the NIR and RE bands were important for N status estimation. In particular, the NIR1 band (760-900 nm from RapidEye or 770-895 nm from WorldView-2) was most important for estimating all the N status indicators. The RE band (690-730 nm or 705-745 nm) improved AGB, PNU, and NNI estimations at all three stages, especially at the PI and SE stages. AGB and PNU were best estimated using data across the stages while plant N concentration (PNC) and NNI were best estimated at the HE stage. The PLSR analysis confirmed the significance of the NIR1 band for AGB, PNU, and NNI estimations at all stages except for the HE stage. It also showed the importance of including extra bands (coastal, yellow, and NIR2) from the WorldView-2 sensor for N status estimation. Overall, both the RapidEye and WorldView-2 data with RE bands improved the results relative to FORMOSAT-2 data. However, the WorldView-2 data with three extra bands in the visible and NIR regions showed the highest potential in estimating rice N status.

Estimation of Mangrove Forest Aboveground Biomass Using Multispectral Bands, Vegetation Indices and Biophysical Variables Derived From Optical Satellite Imageries: Rapideye, Planetscope and Sentinel-2

“…The three important variables of RapidEye are NIR (100%), Blue (42%) and Red bands (38%). This conforms to the results of Huang et al (2017) which reported that NIR was the most important RapidEye band for biomass estimation.…”

A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

“…When the hue component and the intensity component are normalized into the range of [0,1], a conclusion can be deduced for the difference values between the hue and the intensity components, i.e.,H shadow − I shadow for shadows and H nonshadow − I nonshadow for nonshadows, as shown in Equation (11), according to the unequal relation of the hue component between shadows and nonshadows shown in Equation (8), and the unequal relation of the intensity component between shadows and nonshadows, shown in Equation (10).…”

“…With the development of aerospace techniques, an increasing number of very high-resolution (VHR) satellites have been launched in recent years, such as Ikonos, QuickBird, Pleiades, GeoEye, RapidEye, Skysat-1, WorldView-2, WorldView-3, Jilin-1, and Kompsat [1][2][3][4][5][6][7][8][9][10]. The VHR multispectral remote sensing images captured by these satellites can depict more details of typical land cover, including buildings, vegetation and roads.…”

A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle

“…The main RS platforms currently in use include satellite, manned airborne, and ground-based approaches, which can all be equipped with various kinds of sensors. Although satellite images can be used to monitor N status across large areas [5,6], they cannot provide sufficient accuracy because of their low spatio-temporal resolution. Even though manned airborne platforms are able to capture images at high spatio-temporal resolution, this approach is limited by both high operational complexity and cost [7].…”