Recent reviews stated that the complex and context-dependent nature of human decisionmaking resulted in ad-hoc representations of human decision in agent-based land use change models (LUCC ABMs) and that these representations are often not explicitly grounded in theory. However, a systematic survey on the characteristics (e.g. uncertainty, adaptation, learning, interactions and heterogeneities of agents) of the representation of human decision in LUCC ABMs is missing. To inform this debate we performed a quantitative review of 134 LUCC ABM papers using a standardised questionnaire with a particular focus on the characteristics and the theoretical foundation of human decision-making. Thereby, we investigated whether implementations of human decision-making in current LUCC ABMs are theory based. Additionally, we assessed to which degree key factors such as learning, interaction or economic, environmental or social influence factors are considered in human decision making sub-models. We show that most human decision sub-models are not explicitly based on a specific theory and if so they are mostly based on economic theories. In contrast, promising psychological theories such as the theory of planned behaviour are the exception. The key factors of human decision sub-models showed a huge diversity and are not strongly related to neither the characteristics of the specific studied systems (e.g. rural vs. urban or its geographic location) nor the applied theoretical paradigm. We finish by presenting approaches for consolidating and enlarging the theoretical basis for modelling human decision-making.

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Abstract. Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of normalized difference vegetation index (NDVI) of forest understory with multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 meter daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were hence tested across diverse forest conditions and phenological phases during the growing season, to examine its applicability on ecosystems other than boreal forests. Here we report the method can deliver good retrievals especially over different forest types with open canopies (low foliage cover). The performance of the method was found limited over forests with closed canopies (high foliage cover), where the signal from understory gets much attenuated. The spatial heterogeneity of individual field sites as well as the limitations and documented quality of the MODIS BRDF product are shown to be important for correct assessment and validation of the retrievals obtained with remote sensing.