Martin L. Gnyp

Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data

Abstract This study assesses the use of TerraSAR-X data for monitoring rice cultivation in the Sanjiang Plain in Heilongjiang Province, Northeast China. The main objective is the understanding of the coherent co-polarized X-band backscattering signature of rice at different phenological stages in order to retrieve growth status. For this, multi-temporal dual polarimetric TerraSAR-X High Resolution SpotLight data (HH/VV) as well as single polarized StripMap (VV) data were acquired over the test site. In conjunction with the satellite data acquisition, a ground truth field campaign was carried out. The backscattering coefficients at HH and VV of the observed fields were extracted on the different dates and analysed as a function of rice phenology to provide a physical interpretation for the co-polarized backscatter response in a temporal and spatial manner. Then, a correlation analysis was carried out between TerraSAR-X backscattering signal and rice biomass of stem, leaf and head to evaluate the relationship with different vertical layers within the rice vegetation. HH and VV signatures show two phases of backscatter increase, one at the beginning up to 46 days after transplanting and a second one from 80 days after transplanting onwards. The first increase is related to increasing double bounce reflection from the surface–stem interaction. Then, a decreasing trend of both polarizations can be observed due to signal attenuation by increasing leaf density. A second slight increase is observed during senescence. Correlation analysis showed a significant relationship with different vertical layers at different phenological stages which prove the physical interpretation of X-band backscatter of rice. The seasonal backscatter coefficient showed that X-band is highly sensitive to changes in size, orientation and density of the dominant elements in the upper canopy.

In-Season Estimation of Rice Nitrogen Status With an Active Crop Canopy Sensor

Timely nondestructive estimation of crop nitrogen (N) status is crucial for in-season site-specific N management. Active crop canopy sensors are the promising tools to obtain the needed information without being affected by environmental light conditions. The objective of this study was to evaluate the potential for the GreenSeeker active crop canopy sensor to estimate rice (Oryza sativa L.) N status. Nine N rate experiments were conducted from 2008 to 2012 in Jiansanjiang, Heilongjiang Province in Northeast China. The results indicated that across site-years and growth stages, normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) obtained with the GreenSeeker sensor could explain 73%-76% and 70%-73% of rice aboveground biomass and plant N uptake variability in this study, respectively. The NDVI index became saturated when biomass reached about 4 t ha-1 or when plant N uptake reached about 100 kg ha-1, whereas RVI did not show obvious saturation effect. The validation results, however, indicated that both indices performed similarly, and their relative errors (RE) were still large (> 40%). Although the two indices only explained less than 40% of plant N concentration or N nutrition index (NNI) variability, the RE values were acceptable (<; 26%). The results indicated some potentials of using the GreenSeeker sensor to estimate rice N status nondestructively, but more studies are needed to further evaluate and improve its performance for practical applications.

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

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 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 (R2 = 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.

Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with HyperCor

(Accepted: photogrammetric engineering & remote sensing, forthcoming August 2014) Helge Aasen; Martin Leon Gnyp; Yuxin Miao; Georg Bareth 1 Institute of Geography, University of Cologne, Albertus.Magnus-Platz, 50923 Cologne, Germany (helge.aasen, mgnyp1, g.bareth @uni-koeln.de). 2 College of Resources and Environmental Science, China Agricultural University, 100193 Beijing, China (ymiao2007@gmail.com). 3 International Center for Agro-Informatics and Sustainable Development (www.icasd.org). * Corresponding author

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

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.

CampusGIS of the University of Cologne: a tool for orientation, navigation, and management

The working group for GIS and Remote Sensing at the Department of Geography at the University of Cologne has established a WebGIS called CampusGIS of the University of Cologne. The overall task of the CampusGIS is the connection of several existing databases at the University of Cologne with spatial data. These existing databases comprise data about staff, buildings, rooms, lectures, and general infrastructure like bus stops etc. These information were yet not linked to their spatial relation. Therefore, a GIS-based method is developed to link all the different databases to spatial entities. Due to the philosophy of the CampusGIS, an online-GUI is programmed which enables users to search for staff, buildings, or institutions. The query results are linked to the GIS database which allows the visualization of the spatial location of the searched entity. This system was established in 2005 and is operational since early 2006. In this contribution, the focus is on further developments. First results of (i) including routing services in, (ii) programming GUIs for mobile devices for, and (iii) including infrastructure management tools in the CampusGIS are presented. Consequently, the CampusGIS is not only available for spatial information retrieval and orientation. It also serves for on-campus navigation and administrative management.

Nitrogen Status Estimation of Winter Wheat by Using an IKONOS Satellite Image in the North China Plain

The objective of this study was to determine relationship between high resolution satellite image and wheat N status, and develop a methodology to predict wheat N status in the farmers’ fields. Field experiment with 5 different N rates was conducted in Huimin County in the North China Plain, and farmers’ fields in 3 separated sites were selected as validation plots. The IKONOS image covering all research sites was obtained at shooting stage in 2006. The results showed that single band reflectance of NIR, Red and Green and vegetation indices of NDVI, GNDVI, RVI and OSAVI all well correlated with wheat N status parameters. Field validation results indicated that the prediction models using OSAVI performed well in predicting N uptake in the farmers’ fields (R2 = 0.735). We conclude that high resolution satellite images like IKONOS are useful tools in N fertilization management in the North China Plain.

Towards robust vegetation indices: The multi-correlation matrix strategy

Hyperspectral vegetation indices (HVIs) have shown great potential for characterizing and monitoring vegetation and agricultural crops. Additionally, hyperspectral data becomes more commonly available. Latter may be used to address varying annual crop growth. In this paper we describe the multi-correlation matrix strategy as a new approach to derive robust HVIs from multiple hyperspectral field spectrometers datasets. The approach combines the information from multiple correlation matrices (CMs). The software HyperCor is used to automate the data pre-processing and CMs computation. In this study we use data from three growth stages (tillering, stem elongation, heading) in five years (2007–2009, 2011 and 2012) to estimate rice biomass. The new approach is validated through leave-one-out cross-validation and compared to results from a direct approach. On average the multi-correlation matrix approach showed 15% better performance and could reduce the RMSE compared to the direct approach.

Hyperspactral data analysis of nitrogen fertilization effects on winter wheat using spectrometer in North China Plain

This article presents results from hyperspectral analysis for winter wheat (Tricitum Aestivum L.) in the North China Plain during a research study in 2006. In the first part the focus was set on canopy spectral reflectance during the vegetation period under different N supplies. Four different experiments with variable N-inputs and winter wheat cultivars were set up in the study area of Huimin County, Shandong Province. Spectral reflectance data and agronomic data like biomass, plant height, N-uptake and LAI were collected at different phenological stages. In the second part of the study a spectral and agronomic library was set up. For this purpose, spectral reflectance was related to agronomic parameters. The results indicated significant difference in spectra characteristics, cultivars and N-inputs. Vegetation indices like NDVI, HNDVI, RVI, HVI, OSAVI and MCARI2 had the best performance in estimating agronomic parameters among the vegetation indices evaluated.

Deriving winter wheat characteristics from combined radar and hyperspectral data analysis

The main objective of this study is to derive plant nitrogen (N) status and aboveground biomass via satellite remote sensing. To understand canopy spectral reflectance, the focus of the first part was set on the analysis of spectral signatures of winter wheat during its vegetation period under different N treatments. Spectral reflectance at different phenological stages, measured by a spectroradiometer (ASD HandHeld), is related to agronomy parameters like plant N, aboveground biomass and leaf area index (LAI). For this purpose, an extensive field survey was carried out in Huimin County in the North China Plain. For detection of plant N status of winter wheat and biomass on regional scale, hyperspectral (EO-1 Hyperion) and radar (Envisat ASAR) remote sensing data were obtained. First results of preprocessing of remote sensing data are presented in this contribution.