A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments

Conceptual Architecture and Service-oriented Implementation of a Regional Geoportal for Rice Monitoring

Agricultural monitoring has greatly benefited from the increased availability of a wide variety of remote-sensed satellite imagery, ground-sensed data (e.g., weather station networks) and crop models, delivering a wealth of actionable information to stakeholders to better streamline and improve agricultural practices. Nevertheless, as the degree of sophistication of agriculture monitoring systems increases, significant challenges arise due to the handling and integration of multi-scale data sources to present information to decision-makers in a way which is useful, understandable and user friendly. To address these issues, in this article we present the conceptual architecture and service-oriented implementation of a regional geoportal, specifically focused on rice crop monitoring in order to perform unified monitoring with a supporting system at regional scale. It is capable of storing, processing, managing, serving and visualizing monitoring and generated data products with different granularity and originating from different data sources. Specifically, we focus on data sources and data flow, and their importance for and in relation to different stakeholders. In the context of an EU-funded research project, we present an implementation of the regional geoportal for rice monitoring, which is currently in use in Europe’s three largest rice-producing countries, Italy, Greece and Spain

Internet of Things is a revolutionary approach for future technology enhancement: a review

Abstract Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world

Scalable analysis of multitemporal images using an array database

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesMassive archives of earth observation data are now available and the size of this data is increasing at a tremendous rate. This data is a very important resource and has a variety of applications including monitoring change, forestry application, agricultural application and urban planning. At the same time, they also possess challenge of storage, management, and high computational needs. In this study SciDB, an array-based database is used to store, manage and process multitemporal satellite imagery. The major aim of this study is to investigate the performance of SciDB based scalable solution to run arithmetic operation, simple time series analysis and complex time series analysis on multitemporal satellite imagery. This study provides better insight of SciDB architecture and provides suggestions for better performance in SciDB for remote sensing jobs. The research also compared the performance of time series analysis on SciDB array with file-based analysis using multicore parallelization (Using „Parallel‟ Package of R). It is found that SciDB provides a faster solution for time series analysis. However, SciDB might not be the best solution if the data size is smaller. Also, relative immaturity of SciDB and limited inherent support of remote sensing operations increases effort for the scientist to develop SciDB based solution. Nevertheless, SciDB has the potential to meet the ever increasing storage, management and computational need of big remote sensing data

Mapping Land Coverage in the Kapuas Watershed Using Machine Learning in Google Earth Engine

Land cover information is essential data in the management of watersheds. The challenge in providing land cover information in the Kapuas watershed is the cloud cover and its significant area coverage, thus requiring a large image scene. The presence of a cloud-based spatial data processing platform that is Google Earth Engine (GEE) can be answered these challenges. Therefore this study aims to map land cover in the Kapuas watershed using machine learning-based classification on GEE. The process of mapping land cover in the Kapuas watershed requires about ten scenes of Landsat 8 satellite imagery. The selected year is 2019, with mapped land cover classes consisting of bodies of water, vegetation cover, open land, and built-up area. Machine learning that tested included CART, Random Forest, GMO Max Entropy, SVM Voting, and SVM Margin. The results of this study indicate that the best machine learning in mapping land cover in the Kapuas watershed is GMO Max Entropy, then CART. This research still has many limitations, especially mapped land cover classes. So that research needs to be developed with more detailed land cover classes, more diverse and multi-time input data

Geospatial Artificial Intelligence (GeoAI): Applications in Health Care

GeoAI is a new emerging research area that refers to set of technologies that integrate AI technology with a diversity of GIS (Geographic Information System) techniques. The present study observed that GeoAI goes beyond current GIS expectations and into the domain of possibility in the not-too-distant future. This emerging interdisciplinary science will lead us to sustainable decisions and explore the most suitable solutions to the existing problems. GeoAI has the potential to transform current geography and geomatics programs by incorporating a GeoAI dimension into modern GIS curricula. In this review, we have studied the application GeoAI in various healthcare fields. GeoAI has the potential to revolutionize healthcare, public health, infectious disease control, disaster aid, and the achievements of Sustainable Development Goals (SDG). in healthcare, GeoAI can help with disease diagnosis, treatment planning, and resource allocation. In public health, it can aid in disease surveillance, emergency response planning, and identifying health disparities. In infectious disease control, GeoAI can help predict and track disease outbreaks and support vaccination campaigns. In disaster aid, GeoAI can provide real time data on environmental hazards and their impact on public health. In achieving Sustainable Development Goals, it can support in land use planning, urban development, and resource allocation to promote health and environmental sustainability. Overall GeoAI has the potential to transform multiple sectors and improve the well-being of populations worldwide