Georg Bareth

Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging

Crop monitoring is important in precision agriculture. Estimating above-ground biomass helps to monitor crop vitality and to predict yield. In this study, we estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N)-treatments using the plant height (PH) from crop surface models (CSMs). The super-high resolution, multi-temporal (1 cm/pixel) CSMs were derived from red, green, blue (RGB) images captured from a small unmanned aerial vehicle (UAV). Comparison with PH reference measurements yielded an R2 of 0.92. The test site with different cultivars and treatments was monitored during “Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie” (BBCH) Stages 24–89. A high correlation was found between PH from CSMs and fresh biomass (R2 = 0.81) and dry biomass (R2 = 0.82). Five models for above-ground fresh and dry biomass estimation were tested by cross-validation. Modelling biomass between different N-treatments for fresh biomass produced the best results (R2 = 0.71). The main limitation was the influence of lodging cultivars in the later growth stages, producing irregular plant heights. The method has potential for future application by non-professionals, i.e., farmers.

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.

Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice

Abstract Appropriate field management requires methods of measuring plant height with high precision, accuracy, and resolution. Studies show that terrestrial laser scanning (TLS) is suitable for capturing small objects like crops. In this contribution, the results of multitemporal TLS surveys for monitoring plant height on paddy rice fields in China are presented. Three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. The high density of measurement points allows us to establish crop surface models with a resolution of 1 cm, which can be used for deriving plant heights. For both sites, strong correlations (each R 2 = 0.91 between TLS-derived and manually measured plant heights confirm the accuracy of the scan data. A biomass regression model was established based on the correlation between plant height and biomass samples from the field experiment ( R 2 = 0.86 ). The transferability to the farmer’s field was supported with a strong correlation between simulated and measured values ( R 2 = 0.90 ). Independent biomass measurements were used for validating the temporal transferability. The study demonstrates the advantages of TLS for deriving plant height, which can be used for modeling biomass. Consequently, laser scanning methods are a promising tool for precision agriculture.

Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer

In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments. The pointing and positioning accuracy are assessed using structure from motion and vary from σ = 1° to 8° in pointing and σ = 0.7 to 0.8 m in positioning. We use a wheat dataset to investigate the influence of angular effects on the NDVI, TCARI and REIP vegetation indices. Angular effects caused significant variations on the indices: NDVI = 0.83–0.95; TCARI = 0.04–0.116; REIP = 729–735 nm. Our analysis highlights the necessity to consider angular effects in optical sensors when observing vegetation. We compare the measurements of the UAV goniometer to the angular modules of the SCOPE radiative transfer model. Model and measurements are in high accordance (r2 = 0.88) in the infrared region at angles close to nadir; in contrast the comparison show discrepancies at low tilt angles (r2 = 0.25). This study demonstrates that the UAV goniometer is a promising approach for the fast and flexible assessment of angular effects.

High-resolution Crop Surface Models (CSM) and Crop Volume Models (CVM) on field level by terrestrial laser scanning

The interdisciplinary Transregional Collaborative Research Center 32 (CRC/TR 32) works on exchange processes between soil, vegetation, and the adjacent atmospheric boundary layer (SVA). Within this research project a terrestrial laser scanning sensor is used in a multitemporal approach for determining agricultural plant parameters. In contrast to other studies with phase-change or optical probe sensors, time-of-flight measurements are used. On three dates in the year 2008 a sugar beet field (4.3 ha) in Western Germany was surveyed by a terrestrial laser scanner (Riegl LMS-Z420i). Point clouds are georeferenced, trimmed, and compared with official elevation data. The estimated plant parameters are (i) surface model comparison between different crop surfaces and (ii) crop volumes as well as (iii) soil roughness parameters for SVA-Modelling. The results show, that the estimation of these parameters is possible and the method should be validated and extended.

Beachrock-type calcarenitic tsunamites along the shores of the eastern Ionian Sea (western Greece) – case studies from Akarnania, the Ionian Islands and the western Peloponnese

Th is paper presents geo-scientifi c evidence of beachrock-type calcarenitic tsunamites from three study areas in western Greece, namely from the Bays of Aghios Nikolaos (Akarnania), Langadakia (Cefalonia Island) and Aghios Andreas (Peloponnese). Geomorphological, sedimentological, micromorphological and geochemical studies were conducted to clarify depositional processes and the post-sedimentary evolution. Calcarenitic and locally conglomeratic carbonate crusts were studied in natural outcrops along the seafront and in vibracores. High-resolution topographic surveys and 3D-visualisation were carried out by diff erential GPS and LIDAR measurements. Tsunami impact was dated by a combined approach of radiocarbon, OSL and archaeological age determination and compared to local tsunami and earthquake chronologies. We found sedimentary structures such as basal unconformities, rip-up and intra-clasts, evidence of fi ning upward, thinning landward and upward increase in sorting as well as bi-to multimodal deposits and injection structures all of which are described as features typical of recent or historic tsunami deposits. Typically non-littoral sedimentary features such as load casts and convolute bedding further indicate gravity driven processes in water-saturated sheets of allochthonous deposits and are well known from, for example, turbidites. Moreover, thin section analyses revealed highenergy shockand impact-borne cracking and shearing eff ects. Our results show that cementation of tsunami deposits may occur by post-depositional pedogenetic decalcifi cation of higher sections and subsequent secondary carbonate precipitation in lower sections of tsunami deposits provided that they were deposited above sea level. Th e calcarenitic tsunamites encountered in the three study areas match the defi nition of beachrock sensu stricto. Th is is thus the fi rst paper giving examples of beachrock sequences that are interpreted as partially cemented tsunami deposits. Consequently, beachrock is recommended not to be used as sea level indicator in future studies unless a tsunamigenic formation can be defi nitely excluded. Dating results brought to light young, mostly Holocene ages of tsunami sediments. In the Bay of Aghios Andreas, western Peloponnese, we found spectacular traces that Olympia’s ancient harbour site Pheia was destroyed by tsunami impact in the 6th cent. AD and covered by a rapidly cemented, up to 3 m-thick beachrock-type tsunami deposit.

Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices

Leaf diseases, such as powdery mildew and leaf rust, frequently infect barley plants and severely affect the economic value of malting barley. Early detection of barley diseases would facilitate the timely application of fungicides. In a field experiment, we investigated the performance of fluorescence and reflectance indices on (1) detecting barley disease risks when no fungicide is applied and (2) estimating leaf chlorophyll concentration (LCC). Leaf fluorescence and canopy reflectance were weekly measured by a portable fluorescence sensor and spectroradiometer, respectively. Results showed that vegetation indices recorded at canopy level performed well for the early detection of slightly-diseased plants. The combined reflectance index, MCARI/TCARI, yielded the best discrimination between healthy and diseased plants across seven barley varieties. The blue to far-red fluorescence ratio (BFRR_UV) and OSAVI were the best fluorescence and reflectance indices for estimating LCC, respectively, yielding R 2 of 0.72 and 0.79. Partial

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.

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications. Our study site, the Qixing farm in Heilongjiang province, China, is especially suitable to demonstrate this. As in most rice growing regions, there is a high cloud cover during the growing season, making LULC from optical images unreliable. From the study year 2009, we obtained nine TerraSAR-X, two Radarsat-2, one Envisat-ASAR, and an optical FORMOSAT-2 image, which is mainly used for comparison, but also for a combination. To evaluate the potential of the input images and derive LULC with the highest possible precision, two classifiers were used: the well-established Maximum Likelihood classifier, which was optimized to find those input bands, yielding the highest precision, and the random forest classifier. The resulting highly accurate LULC-maps for the whole farm with a spatial resolution as high as 8 m demonstrate the beneficial use of a combination of x- and c-band microwave data, the potential of multitemporal very high resolution multi-polarization TerraSAR-X data, and the profitable integration and comparison of microwave and optical remote sensing images for LULC classifications.

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.