2021, Harfenmeister, Katharina, Itzerott, Sibylle, Weltzien, Cornelia, Spengler, Daniel

The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.

2018-4-9, Maaß, Oliver, Grundmann, Philipp

Reusing wastewater in agriculture has attracted increasing attention as a strategy to support the transition towards the circular economy in the water and agriculture sector. As a consequence, there is great interest in solutions for governing the transactions and interdependences between the associated value chains. This paper explores the institutions and governance structures for coordinating transactions and interdependences between actors in linked value chains of wastewater treatment and crop production. It aims to analyze how transactions and interdependences shape the governance structures for reusing wastewater at the local level. A transaction costs analysis based on data from semi-structured interviews and a questionnaire is applied to the agricultural wastewater reuse scheme of the Wastewater Association Braunschweig (Germany). The results show that different governance structures are needed to match with the different properties and requirements of the transactions and activities between linked value chains of wastewater treatment and crop production. Interdependences resulting from transactions between wastewater providers and farmers increase the need for hybrid and hierarchical elements in the governance structures for wastewater reuse. The authors conclude that aligning governance structures with transactions and interdependences is key to efficiently governing transactions and interdependences between linked value chains in a circular economy. © 2018 by the authors.