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[学术文献 ] LUCST: A novel toolkit for Land Use Land Cover change assessment in SWAT+ to support flood management decisions 进入全文
Environmental Modelling & Software
Land Use Land Cover (LULC) change is widely recognised as one of the most important factors impacting the hydrological response of river basins. SWAT+, the latest version of the Soil and Water Assessment Tool, has been used extensively to assess the hydrological impacts of LULC change. However, the process of making and assessing such changes in SWAT+ is often cumbersome and non-intuitive, thereby reducing its usability amongst a wider pool of applied users. We address this issue by developing a user-friendly toolkit, Land Use Change SWAT + Toolkit (LUCST), that will: (1) allow the end-user to define various LULC change scenarios in their study catchment, (2) run the SWAT+ model with the specified LULC changes, and (3) enable interactive visualisation of the different SWAT+ output variables. A good System Usability Score (79.8) and positive feedback from end-users promises the potential for adopting LUCST in future LULC change studies.
[学术文献 ] Identification and Quantification of Actual Evapotranspiration Using Integrated Satellite Data for Sustainable Water Management in Dry Areas 进入全文
Agronomy
Evapotranspiration (ET) is a significant consumer of irrigation water and precipitation on cropland. Global and regional interest in the sustainable management of limited freshwater supplies to meet the rapidly increasing population and food demands has resulted in advanced scientific research on ET measurement, rapid water accounting, and irrigation schedules in the NENA region. The primary goal of this paper is to compare actual daily evapotranspiration (ET) collected by a remote sensing model and validated by Energy Balance (EB) flux tower field measurements. The flux tower was installed in a wheat field in Sids Agricultural Research Station in Beni Suef Governorate. Through the integration of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Sentinel-2 data, a new remote sensing-based ET model is built on two parties: Thermal condition factor (TCF) and vegetation condition fraction (VCF). The remote sensing-based ET estimation model was evaluated using ET field measurements from the Energy Balance flux tower. The land use and land cover maps were created to assist the interpretation of remotely sensed ET data. Field data for five categories were collected to test the accuracy of the land use and cover maps: Water bodies (93 points), urban areas (252 points), trees (104 points), other field crops (227 points), and wheat (249 points), for a total of 925 ground points. The Google Earth Engine (GEE) imported sentinel-2 datasets and filtered the necessary dates and regions. From 1 October 2020 to 30 May 2021, sentinel-2 data were processed and transformed into the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), which were then combined. The composite layer data were classified using the Random Forest (RF) method on the GEE platform, and the results showed an overall accuracy of 91 percent. The validation factors revealed good indices when RS-based ET results were compared to ground-measured ET. The Root Mean Square Error (RMSE) was 0.84 mm/day. The ‘r’ and ‘d’ values indicated satisfactory results, where ‘r’ yielded a value of 0.785, which indicates that the correlation between predicted and reference results is robust. The analysis of d values revealed a high degree of correlation between predicted (RS-based ET) and reference results (measured ET). The d value was found to be 0.872. Between 21 November 2020 and 30 April 2021, RS-based accumulated ET was 418 mm/season, while ground-measured ET was 376 mm/season. The new RS-based ET model produced acceptable daily and seasonal results.
[学术文献 ] SAMZ-Desert: A Satellite-based agricultural management zoning tool for the desert agriculture region of southern California 进入全文
Computers and Electronics in Agriculture
A GIS and remote sensing-based decision support tool called SAMZ-Desert was developed for management zones (MZs) delineation of a total of 6852 fields in the Imperial County region of southern California using Landsat-8 NDVI data acquired on 27/4 2018. In addition, a total number of 11 cloud-free images in 2018–2020 were statistically analyzed to determine the extent of within-field NDVI variability and temporal stability of MZs at the regional level. A majority (approx. 37%) of the fields had four zones as an optimum number of zones in the region, which could explain>85% of the within-field NDVI variance. Around 13% (n = 873) of the fields in the region were strongly spatially-clustered in at least half the Landsat-8 cloud-free scenes and can benefit from variable rate technologies. Our results suggest that dynamic zoning over time might be necessary for most of these fields. SAMZ-Desert can be accessed from the Haghverdi Water Management Group website: http://www.ucrwater.com/software-and-tools.html.
[学术文献 ] Trusted-auditing chain: A security blockchain prototype used in agriculture traceability 进入全文
Heliyon
Traceability systems have changed the way food safety is managed and data is stored. Blockchain tracking services now provide customers with an infrastructure that allows them to easily access data online. However, there are limitations to these new capabilities, such as a lack of transparency and the existence of privacy and security challenges. Additionally, as the need for more agile, private, and traceability secure data solutions continues to grow exponentially, rethinking the current structure of blockchain agricultural traceability is mission-critical for a country. By leveraging and building upon blockchain's unique attributes, including tamper-evident, security hash crypto-data, and distributed ledger, we have proposed a prototype that allows traceability data to be reliably stored via blockchain while simultaneously being secured, with completeness auditing to enhance credibility. The result, the trusted auditing chain (TA chain), is a flexible solution that assures data security and solves challenges such as scalability and privacy-preserving. The TA chain works through Schnorr-style non-interactive Zero-knowledge proof to support security automatical choose privacy augmented. In addition, The TA chain can audit more than 1000 transactions within 1ms, and its error stabilizes below the 250 μs, which proves a security and fair traceability system to assure that data is distributed and reliably, and provably audited.
[学术文献 ] Agriculture and food security under a changing climate: An underestimated challenge 进入全文
iScience
Pathways to eradicate global hunger while bending the curve of biodiversity loss unanimously suggest changing to less energy-rich diets, closing yield gaps through agroecological principles, adopting modern breeding technologies to foster stress resilience and yields, as well as minimizing harvest losses and food waste. Against the background of a brief history of global agriculture, we review the available evidence on how the global food system might look given a global temperature increase by 3°. We show that a moderate gain in the area suitable for agriculture is confronted with substantial yield losses through strains on crop physiology, multitrophic interactions, and more frequent extreme events. Self-amplifying feedback are unresolved and might lead to further losses. In light of these uncertainties, we see that complexity is underestimated and more systemic research is needed. Efficiency gains in agriculture, albeit indispensable, will not be enough to achieve food security under severe climate change.
[学术文献 ] Cloud and distributed architectures for data management in agriculture 4.0 : Review and future trends 进入全文
Journal of King Saud University - Computer and Information Sciences
The Agriculture 4.0, also called Smart Agriculture or Smart Farming, is at the origin of the production of a huge amount of data that must be collected, stored, and processed in a very short time. Processing this massive quantity of data needs to use specific infrastructure that use adapted IoT architectures. Our review offers a comparative panorama of Central Cloud, Distributed Cloud Architectures, Collaborative Computing Strategies, and new trends used in the context of Agriculture 4.0. In this review, we try to answer 4 research questions: (1) Which storage and processing architectures are best suited to Agriculture 4.0 applications and respond to its peculiarities? (2) Can generic architectures meet the needs of Agriculture 4.0 application cases? (3) What are the horizontal development possibilities that allow the transition from research to industrialization? (4) What are the vertical valuations possibilities to move from algorithms trained in the cloud to embedded or stand-alone products? For this, we compare architectures with 8 criteria (User Proximity, Latency & Jitter, Network stability, high throughput, Reliability, Scalability, Cost Effectiveness, Maintainability), and analyze the advantages and disadvantages of each of them.
