Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g/m 2 are reached for the validation of predicted vs. observed dry biomass, while Willmott's refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g/m 2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time.

Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually. This system was set up on a barley field experiment with nine different barley cultivars in the growing period of 2014. Images were acquired three times a day for a period of two months. CSMs were successfully generated for 95 out of 98 acquisitions between May 2 and June 30. The best linear regressions of the CSM-derived plot-wise averaged plant-heights compared to manual plant height measurements taken at four dates resulted in a coefficient of determination R 2 of 0.87 and a root-mean-square error (RMSE) of 0.08 m, with Willmott's refined index of model performance d r equaling 0.78. In total, 103 mean plot heights were used in the regression based on the noon acquisition time. The presented system succeeded in semiautomatedly monitoring crop height on a plot scale to field scale. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

The development and application of an algorithm to compute K€ oppen-Geiger climate classifications from the Coupled Model Intercomparison Project (CMIP) and Paleo Model Intercomparison Project (PMIP) climate model simulation data is described in this study. The classification algorithm was applied to data from the PMIP III paleoclimate experiments for the Last Glacial Maximum, 21k years before present (yBP), Mid-Holocene (6k yBP) and the Pre-Industrial (0k yBP, control run) time slices. To infer detailed classification maps, the simulation datasets were interpolated to a higher resolution. The classification method presented is based on the application of Open Source Software, and the implementation is described with attention to detail. The source code and the exact input data sets as well as the resulting data sets are provided to enable the application of the presented approach. an easily reproducible method for deriving K€ oppen-Geiger climate classifications from CMIP/ PMIP climate model simulation outputs using accessible open source tools, the computations and single GIS processing steps are described in detail, to facilitate the reapplication of the presented method discussed. Thus, the aim of this work is not to compare the results of different climate models, for which the presented method would be suitable as well, but to present a reproducible method, application, or tool, to derive the K€ oppen-Geiger classifications as basis or input for further applications and research. Because there is clearly an increasing momentum towards open science (Hey and Payne 2015) in which the authors of this study want to participate, close attention is paid to the reproducibility of the presented method and its application. To support this, in addition to using open source software for the computations, all resulting and source datasets are provided by the authors.This study proceeds by first looking at related research of applying climate classifications to climate model simulations in Section 2. As described and applied in some of these studies, the here presented classification method can also be used to cross-validate and evaluate climate models. In the following sections of this article, we describe the data and methods applied in this study in Section 3. The CMIP/PMIP models and experiments are introduced in Section 3.1, the input variables for the classifications are described in Section 3.1.2 and the exact data sources for the classification maps presented in this article are given in Section 3.1.3. This is followed by a description of the process of creating the land masks needed for time slices with different sea levels in Section 3.2 and the interpolation procedure applied to the input data in Section 3.3. In Section 3.4, we describe the updated K€ oppen-Geiger classification scheme after Peel et al. (2007) and Kottek et al. (2006), which we implemented in this contribution. Section 4 concerns the actual practical implementation details of how the classifications are computed. The resulting classifications...

Nitrous oxide (N 2 O) is a potent greenhouse gas, which has to be included in national inventories because it is contributing to global warming. It primarily originates from agriculturally managed soils. These represent area sources, which are much more difficult to account for than point sources, such as power plants or industrial sources. The Intergovernmental Panel on Climate Change provides a default emission factor but also the plea for more sophisticated ways of calculating N 2 O emissions from agricultural land use, in particular from industrialized nations. To fulfill this plea, already some approaches developed for use in Germany and elsewhere, which are more process based to a certain degree, have been published. However, these predominately require a high information input for model runs and site-specific calibration. In the present paper, we demonstrate the advantage of an empirical approach. This contribution introduces an approach for estimating N 2 O emissions in a regionally disaggregated manner by calculating emission factors based on 86 empirical measurements of N 2 O fluxes. These emission factors are calculated separately for distinct regions of Germany based on climate characteristics (precipitation and days of frost) and soil aeration. By combining these calculated emission factors with datasets on land use, nitrogen input, climate characteristics and soil, the N 2 O emission fluxes for Germany are estimated in a spatially disaggregated manner at the county (Landkreis) level, only accounting for agricultural land use and excluding forest and urban areas. This approach yields an emission estimate of 53.38 Gg N 2 O−N. For comparison purposes, this contribution also estimates N 2 O emissions using a spatially disaggregated version of the IPCC guidelines to determine emissions for the national greenhouse gas inventories, which results in an estimated emission of 35.70 Gg N 2 O−N for Germany. The results of these emission estimates suggest that the N 2 O emissions from agricultural land use are underestimated in the official national greenhouse gas inventory if the simple, single-emission-factor based method is used. Zusammenfassung: Lachgas (N 2 O) ist ein starkes Treibhausgas, das zur Erderwärmung beiträgt und in nationalen Treibhausgasinventaren berücksichtigt werden muss. Es entsteht hauptsächlich in landwirtschaftlich genutzten Böden. Diese Flächenquellen sind deutlich schwerer zu berücksichtigen als Punktquellen wie Industrie oder Kraftwerke. Das Intergovernmental Panel on Climate Change (IPCC) gibt einen Standard-Emissionsfaktor vor, bittet aber auch um ausgefeiltere Methoden, N 2 O-Emissionen aus landwirtschaftlichen Böden zu berechnen, insbesondere für industrialisierte Nationen. Aufgrund dessen gibt es einige Ansätze für Deutschland, die zu einem gewissen Grad prozessorientiert sind, allerdings große Mengen an Eingabeinformationen für Modellläufe und ortsspezifische Kalibrierung benötigen. Hier präsentieren wir die Vorteile eines empirischen Ansatzes. Dieser Beitrag stellt einen Ansatz ...