Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

Abstract

Rice farming in Northeast China is crucially important for China's food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N OPEN ACCESS Remote Sens. 2015, 7 10647 application at the stem elongation stage in Northeast China. Five farmers' fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R 2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments.

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

“…After the HE stage, the increase in AGB gains slower, and plant N starts to dominate canopy reflectance [65]. Huang et al (2015) [3] proposed an indirect approach to estimate NNI at the PI stage based on the AGB and PNU values derived from FORMOSAT-2 satellite images for guiding topdressing N application at the SE stage. This indirect method might be tested using RE-based VI models derived from RapidEye and WorldView-2 images in the future.…”

Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice

“…RS data is widely used to monitor crop N status, including satellite imagery [7,8], aerial photographs [9] and canopy reflectance spectra [5,10]. For example, Yao et al [5] used ground-based hyperspectral data to detect the leaf N concentration of various growth stages in winter wheat.…”

“…For example, Yao et al [5] used ground-based hyperspectral data to detect the leaf N concentration of various growth stages in winter wheat. Huang et al [8] used FORMOSAT-2 satellite data to estimate aboveground biomass and plant N uptake at the panicle initiation stage of rice in northeast China. Boegh et al [9] used airborne multispectral (MS) data to quantify canopy N concentration in agriculture.…”

Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions

“…To make better use of spectral data, a large number of spectral VIs have been developed to characterize vegetation canopies, using crop reflectance with different wavelengths. Empirical regression models are often used as a common method for crop biochemical, LAI and AGB estimation by using VIs [16,[36][37][38][39]. Linear or non-linear relationships have been established between VIs and crop parameters for different vegetation types under varying climatic conditions [40,41].…”