Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014)(2015)(2016)(2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product's performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to -Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product's performance and open a door to future studies regarding hydrometeorological applications of these products in Iran.Remote Sens. 2020, 12, 48 2 of 23 subject to different errors and uncertainties, such as ground clutter, anomalous propagation, signal attenuation, beam blockage, and bright band contamination [6].Rain gauges are limited in describing the spatial distribution of precipitation depending on the arrangement and density of the rain gauge network [7,8]. In order to spatially characterize precipitation, gauge measurements are transformed to a gridded precipitation dataset. This is carried out through interpolation of rain gauge measurements, using spatial interpolation and geo-statistical methods [9]. These may be prone to missing values, wind effects, insufficient numbers of rain gauges, and a sparse network, especially in less accessible mountainous and oceanic areas [4].In view of the above, the spatial limitations, resolution, and coverage of ground-based measurements highlight the importance of satellite-based precipitation estimates at both the regional and global scale. Satellite-based precipitation estimates are also subject to uncertainties through cloud top reflectance, thermal radiance, infrequent satellite overpasses, and retrieval algorithm related to the nature of indirect measurement [10]. Therefore, a thorough validation of satellite precipitation data in any given area is necessary to achieve insight regarding is accuracy...

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Farmers know much more than we think, and they are keen to improve their knowledge in order to improve their farms and increase their income. On the other hand, decision-makers, organizations, and researchers are increasing their use of citizen volunteers to strengthen their outcomes, enhance project implementation, and approach ecosystem sustainability. This paper assesses the role of citizen science relating to agricultural practices and covers citizen science literature on agriculture and farmers’ participation during the period 2007–2019. The literature was examined for the role of citizen science in supporting sustainable agriculture activities, pointing to opportunities, challenges, and recommendations. The study identified the following gaps: insufficient attention to (1) long-term capacity building and dialogue between academics and farming communities; (2) developing countries in the global South and smallholders; (3) agriculture trading and marketing; (4) the rationales of selecting target groups; (5) contributing to accelerated sustainability transitions. The main aim of the research projects reviewed in this study tended to focus on the research outcomes from an academic perspective, not sustainable solutions in practice or sustainability in general. More research is needed to address these gaps and to widen the benefits of citizen science in sustainable agricultural practices.

This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge.