Monitoring, Assessment, and Prediction of Agroecosystem Dynamics for Sustainable Agriculture: Applications of Photogrammetry, Remote Sensing, and Geographic Information Systems

Dear Colleagues,

Contemporary agriculture is undergoing a paradigm shift toward sustainability, driven by the pressing need to address challenges to food security, environmental conservation, and economic sustainability. Given the complex and dynamic nature of agroecosystems, understanding and managing them necessitates advanced tools capable of monitoring, assessing, and predicting their dynamics accurately and efficiently. In this context, technologies such as photogrammetry, remote sensing, and geographic information systems (GISs) have emerged as indispensable tools in this realm. Unlike traditional methods, which often fall short of providing the scalability and efficiency required for effective management and decision-making, these advanced technologies can offer scalable, cost-efficient, and highly accurate tools for comprehensive agroecosystem analysis. Further research in this area is vital for developing sustainable agricultural practices and solutions that can ensure long-term food production, protect biodiversity, and mitigate the impacts of climate change.

In this Special Issue, “Monitoring, Assessment, and Prediction of Agroecosystem Dynamics for Sustainable Agriculture: Applications of Photogrammetry, Remote Sensing, and Geographic Information Systems”, we aim to foster the advancement of sustainable agriculture through technological innovation. We invite submissions of original research articles, comprehensive review papers, and insightful case studies that showcase applications of photogrammetry, remote sensing, and GISs, as well as interdisciplinary approaches, in agricultural contexts.

Potential themes include, but are not limited to, the following:

• Precision agriculture and farm management: Innovative use of advanced methodologies for sustainable agricultural management, such as optimized nitrogen fertilization, efficient irrigation practices, and reduced tillage.
• Crop productivity: Analysis and enhancement strategies for crop monitoring under varying environmental conditions, including real-time growth tracking, yield prediction, and decision-making.
• Greenhouse gas emissions: Applications related to the measurement, reporting, and verification of emissions of three major greenhouse gases (CO₂, CH₄, and N₂O) and their mitigation in agriculture.
• Crop health and disease surveillance: Development and application of predictive models for the early detection and management of crop diseases and pests, leveraging GISs and remote sensing data.
• Impacts of multiple environmental changes on agroecosystem dynamics: Comprehensive analyses on how multiple environmental changes (e.g., climate change, agricultural management practices, land-use changes, and extreme climate events) affect agricultural systems.
• Agricultural adaptation: Analyses assessing the resilience and adaptability of agricultural systems to environmental and climatic changes.
• Agricultural water resource management: Advanced techniques for assessing, mapping, and optimizing water use in agriculture.
• Socio-economic impacts of agroecosystem dynamics: Exploring the socio-economic implications of changing agroecosystems, including impacts on livelihoods, food security, and rural development, using remote sensing and GIS analyses.

Dr. Yongfa You
Dr. Naiqing Pan
Dr. Dipankar Mandal
Guest Editors

Manuscript Submission Information

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• sustainable agriculture
• remote sensing
• photogrammetry
• GIS
• agroecosystem dynamics

Title: Soil salinization and soil moisture across layers exhibit differentiated time-lag correlation in Northeast Tibetan Plateau agroecosystem
Authors: Di Wei; Ziqi Zhang; Lin Yan; Yun Zhang; Bo Wang
Affiliation: Lanzhou University
Abstract: The evaporation of soil moisture drives the upward movement of salt and its accumulation on the surface, which is regulated by climate change and ultimately leads to soil salinization in agroe-cosystem. Unlike previous in-situ experiments, the rapid development of geological analysis methods such as remote sensing technology has made it possible to describe the spatiotemporal characteristics of soil salinization and soil moisture at the watershed scale of agricultural eco-systems. Based on Landsat 8 satellite imagery and ERA5 reanalysis datasets, this study obtained the longtime variation characteristics of soil water and salt elements in Northeast Tibetan Plateau watershed. Combining ridge regression and windowed cross correlation, the differential time-lag correlation of soil moisture across layers on soil salinization was determined, meanwhile the dominant factors of soil water and salt transport were identified using the random forest algorithm and SHapley Additive exPlanations. The results show that soil moisture values increase with soil depth from 2013 to 2023, while soil salinization has slightly increased and shows a spatial dis-tribution that increases from southeast to northwest. The negative correlation effect between Level 4 soil moisture (below 100 cm) and soil salinization is stronger, in addition to instant response, soil water and salt also have a time lag effect, and the regulation of soil salinization by deep soil moisture may require a long transport time. When considering the comprehensive regulation of soil water and salt by climate factors (runoff, surface solar radiation) and soil factors (soil organic carbon, cation exchange capacity), the result proves that an increase in soil organic carbon and runoff is beneficial for alleviating salinization, while abundant runoff also promotes soil hu-midification. This study aims to elucidate the complex interaction between soil moisture across layers and soil salinization, which is of great significance for understanding the ecological regu-lation mechanism of soil water and salt transport in agroecosystem dynamic changes.

Title: Multi-indicator deterministic model based on time series of Sentinel-2, to assess the degree of natural succession on the abandoned arable areas
Authors: Małgorzata Kozak; Anna Jędrejek; Rafał Pudełko
Affiliation: Department of Geomatics, Institute of Soil Science and Plant Cultivation—State Research Institute
Abstract: The article presents the concept of a deterministic model for assessing the degree of natural succession on long-term abandoned land in the agricultural production area of Poland. The model was implemented as a geographic information system tool. It is based on two basic sources of information: cadastral maps, which can suggest if the agricultural land is agriculturally used, and seasonal time series of satellite images. The following working hypothesis was adopted in this study: "based on the data from the Sentinel-2 sensor, it is possible to assess the degree of natural succession on small and medium agricultural plots – by separating at least three classes of land cover, representing: early succession consisting mainly grass and ruderal vegetation, advanced succession represented by complexes of shrubs and young trees, mature succession - which is a transitional phase preceding the afforestation phase, or can already be a functional forest area”. The obtained results confirmed the above hypothesis. In the case of early succession, the classification efficiency was about 94% in the winter period, for advanced succession about 75% in the autumn period, and for mature succession about 78% in the summer period. In the classification process, 8 vegetation indices were examined. In the end, the model algorithms were based on the GNDVI index, whose properties allowed for the best differentiation between the above-mentioned succession classes. NDVI, NDRE, NDVIre1, NDVIre2, NDVIre3 were used as auxiliary indices, which, as shown in the research, can improve the classification accuracy at a higher uncertainty threshold in case of weaker separation of classes with the GNDVI index.