A Grid-Based Spatiotemporal Deviation Framework for Agricultural Landscape Monitoring Using Remote Sensing

A Grid-Based Spatiotemporal Deviation Framework for Agricultural Landscape Monitoring Using Remote Sensing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Grid-Based Spatiotemporal Deviation Framework for Agricultural Landscape Monitoring Using Remote Sensing Ayashi Das Majumder This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8430837/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Agricultural monitoring systems increasingly rely on satellite-derived indices to assess crop and field conditions; however, many existing approaches depend on absolute index values or static thresholds, limiting their ability to capture localized and temporal variability. This paper presents a grid-based spatiotemporal deviation framework for agricultural landscape monitoring that emphasizes relative performance assessment against historical baselines rather than absolute measurements. The proposed framework partitions agricultural regions into fine-resolution spatial grids and constructs multi-year temporal baselines for each grid using satellite-derived vegetation and environmental indicators. Current observations are evaluated using standardized deviation metrics to identify significant departures from historical norms while accounting for contextual interactions among multiple indices. The system incorporates strict data quality controls, including cloud-cover filtering and conditional temporal interpolation, to ensure analytical robustness under real-world data constraints. Outputs are designed to support interpretability through grid-level anomaly maps, temporal trend visualizations and aggregated field indicators. A representative demonstration is presented to illustrate how the framework enables early detection of spatial and temporal variability in agricultural conditions. The proposed approach offers a modular and extensible foundation for decision-support applications in precision agriculture, sustainability assessment and risk monitoring. Precision agriculture Remote sensing Spatiotemporal analysis Anomaly detection GeoAI Decision support systems Satellite imagery Figures Figure 1 Figure 2 Introduction Satellite remote sensing has become a cornerstone of modern agricultural monitoring, enabling large-scale observation of vegetation dynamics, water availability and environmental conditions (Tucker, 1979; Mulla, 2013; Zhang & Kovacs, 2012). Vegetation indices derived from multispectral imagery—such as the Normalized Difference Vegetation Index (NDVI) and its variants—are widely used to assess crop health, seasonal growth patterns and stress conditions (Rouse et al., 1974; Huete et al., 2002; Baret & Guyot, 1991). Despite their extensive adoption, many operational agricultural monitoring systems continue to rely on absolute index values or static threshold-based interpretations, which often fail to capture localized variability and temporal context at the field scale (Verbesselt et al., 2010; Lhermitte et al., 2011). Agricultural landscapes are inherently heterogeneous, with crop performance influenced by site-specific factors such as soil properties, microclimate, management practices and historical land use (Lobell et al., 2009). As a result, identical index values may represent markedly different conditions across fields, seasons, or regions. Systems that evaluate current observations without reference to localized historical baselines risk misinterpreting normal seasonal fluctuations as stress events, or conversely, overlooking gradual degradation trends that remain within nominal threshold ranges (Bégué et al., 2018). This limitation is particularly pronounced in precision agriculture, where decision-making increasingly depends on fine-scale, temporally aware insights rather than regional averages (Mulla, 2013; Schimmelpfennig, 2016). Recent research has highlighted the importance of time-series analysis and anomaly detection techniques for improving agricultural monitoring (Verbesselt et al., 2010; Chandola et al., 2009). Approaches based on temporal normalization, trend analysis, and deviation detection offer a means to contextualize current observations relative to historical behavior (Lhermitte et al., 2011; Forkel et al., 2013). However, many existing methods operate at coarse spatial resolutions, apply uniform baselines across large regions, or lack mechanisms to integrate multiple indices in a context-aware manner (Atzberger, 2013). Furthermore, practical challenges such as cloud contamination, missing observations and inconsistent data availability remain significant barriers to reliable operational deployment of satellite-based agricultural monitoring systems (Zhu & Woodcock, 2012; Roy et al., 2014). To address these challenges, this paper presents a grid-based spatiotemporal deviation framework for agricultural landscape monitoring. The proposed approach partitions agricultural regions into fine-resolution spatial grids and constructs grid-specific temporal baselines using multi-year satellite-derived data. Rather than relying on absolute index thresholds, current observations are evaluated through standardized deviation metrics that quantify departures from historical norms at the grid level, following established anomaly detection principles (Chandola et al., 2009). This enables localized and temporally grounded interpretation of vegetation and environmental dynamics. A key aspect of the framework is its emphasis on contextual interpretation across multiple indicators. Deviations in individual indices are evaluated in relation to complementary signals to reduce false positives and improve robustness under complex field conditions, consistent with findings from multi-indicator agricultural monitoring studies (Bégué et al., 2018; Atzberger, 2013). In addition, the framework incorporates strict data quality controls, including cloud-cover filtering and conditional temporal interpolation, to ensure analytical reliability when working with real-world satellite datasets (Zhu & Woodcock, 2012; Gorelick et al., 2017). The contribution of this work lies in the design and implementation of a modular GeoAI-based framework that integrates fine-scale spatial representation, temporal normalization, deviation analysis and interpretable outputs for agricultural decision support. Rather than focusing on predictive accuracy or crop-specific calibration, the paper emphasizes methodological design choices and system architecture that enable scalable, extensible and context-aware agricultural monitoring. A representative demonstration is included to illustrate the practical application of the framework and its potential to support precision agriculture, sustainability assessment and agricultural risk monitoring workflows. 5. Related Work Remote sensing has long been used for agricultural monitoring, with vegetation indices derived from multispectral satellite imagery serving as primary indicators of crop health and productivity (Tucker, 1979; Baret & Guyot, 1991; Mulla, 2013). Indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI) have been widely applied for assessing biomass, canopy vigor and seasonal growth dynamics across a range of cropping systems (Rouse et al., 1974; Huete et al., 2002; Atzberger, 2013). Numerous studies have demonstrated the utility of these indices for crop monitoring, drought assessment and yield-related analysis across diverse agro-ecological regions (Lobell et al., 2009; Bégué et al., 2018; Bolton & Friedl, 2013). Building on these foundations, time-series analysis techniques have been increasingly adopted to capture temporal dynamics in vegetation signals. Methods based on seasonal trend extraction, phenological metrics and temporal smoothing have improved the interpretation of satellite-derived observations by reducing noise and highlighting long-term patterns (Verbesselt et al., 2010; Forkel et al., 2013; de Beurs & Henebry, 2010). More recent work has explored anomaly detection approaches, where deviations from expected temporal behavior are used to identify stress events, yield losses, or abnormal growth conditions (Lhermitte et al., 2011; Chandola et al., 2009). Such approaches have shown promise in enhancing early warning capabilities compared to static, single-date assessments, particularly under variable climatic conditions (Zscheischler et al., 2014; Peters et al., 2015). Despite these advances, several limitations persist in existing agricultural monitoring systems. Many approaches operate at coarse spatial resolutions or rely on regional averages, limiting their applicability for field-level decision-making (Atzberger, 2013; Mulla, 2013). Even when higher-resolution imagery is available, temporal baselines are often constructed at aggregated spatial or administrative scales, which can obscure localized variability driven by soil heterogeneity, management practices, or microclimatic effects (Lobell & Burke, 2010; Bégué et al., 2018). As a result, absolute index values or uniform thresholds may lead to misinterpretation when applied across diverse fields or seasons (Baret & Guyot, 1991; Verbesselt et al., 2010). Decision-support platforms that integrate satellite data into operational tools have further expanded the accessibility of remote sensing for agriculture. These systems commonly provide dashboards, maps and index-based indicators to support farm management and planning (Zhang & Kovacs, 2012). However, many such platforms emphasize visualization over methodological transparency and often lack mechanisms for contextual interpretation across multiple indices (Atzberger, 2013). Additionally, practical challenges such as cloud contamination, missing observations and inconsistent temporal coverage are frequently handled with limited or undocumented preprocessing strategies, affecting analytical robustness and reproducibility (Zhu & Woodcock, 2012; Roy et al., 2014). In response to these gaps, recent research has emphasized the need for localized, temporally normalized analysis frameworks that combine fine-scale spatial representation with robust data quality controls (Verbesselt et al., 2010; Forkel et al., 2013). Approaches that evaluate relative performance against historical baselines, rather than relying solely on absolute index values, have been identified as particularly important for precision agriculture and sustainability monitoring (Lhermitte et al., 2011; Bégué et al., 2018). However, there remains a need for integrated frameworks that systematically combine grid-level spatial analysis, multi-year temporal baselining, contextual multi-index interpretation and interpretable outputs within a single, extensible system. The framework presented in this paper builds on existing remote sensing and time-series analysis research by focusing on grid-level spatiotemporal deviation analysis tailored to agricultural landscapes. By emphasizing localized historical normalization, contextual interpretation of multiple indicators and explicit handling of real-world data constraints, the proposed approach addresses several limitations of existing methods while remaining compatible with operational decision-support applications. 6. System Overview The proposed framework is designed as a modular GeoAI-based system for grid-level spatiotemporal monitoring of agricultural landscapes. Its primary objective is to enable localized, temporally contextual interpretation of satellite-derived indicators while remaining robust to real-world data constraints such as cloud contamination and irregular temporal coverage, which are well-documented challenges in optical remote sensing (Zhu & Woodcock, 2012; Roy et al., 2014). The system follows a layered architecture that separates data acquisition, analytical processing, deviation analysis and interpretability-oriented outputs, consistent with best practices in environmental decision-support system design (Atzberger, 2013). At the highest level, the framework consists of four functional layers: (i) data acquisition and quality control, (ii) spatial–temporal structuring, (iii) deviation analysis and contextual interpretation and (iv) visualization and decision-support outputs. Such modular, layered architectures are commonly adopted in geospatial analytics and agricultural monitoring systems to support scalability, transparency and extensibility (Mulla, 2013; Gorelick et al., 2017). Each layer is designed to operate independently while contributing to a coherent analytical pipeline. The data acquisition layer is responsible for retrieving multispectral satellite imagery and associated environmental data over user-defined regions of interest. Data are filtered using strict quality criteria, including cloud-cover thresholds and completeness checks, to ensure that only analytically reliable observations are passed to downstream processes. When suitable imagery is unavailable for a given time step, the system applies controlled temporal backdating to identify the nearest valid observation, a strategy commonly employed to mitigate data gaps in satellite time series without introducing artificial observations (Zhu & Woodcock, 2012; Verbesselt et al., 2010). In the spatial–temporal structuring layer, the region of interest is partitioned into uniform spatial grids corresponding to the spatial resolution of the satellite imagery. Each grid cell is treated as an independent analytical unit, enabling fine-scale representation of spatial heterogeneity within agricultural fields, which is critical for precision agriculture applications (Mulla, 2013). For each grid, multi-year historical time series are constructed for selected vegetation and environmental indicators, forming the basis for localized temporal baselines as recommended in spatiotemporal agricultural monitoring studies (Verbesselt et al., 2010; Forkel et al., 2013). The analytical core of the framework performs spatiotemporal deviation analysis by comparing current observations against grid-specific historical baselines. Rather than evaluating absolute index values, the system quantifies standardized deviations to identify significant departures from expected temporal behavior, following established principles in time-series anomaly detection (Chandola et al., 2009; Lhermitte et al., 2011). This design allows the framework to distinguish between normal seasonal variability and meaningful anomalies that may indicate stress, underperformance or favorable conditions. To improve robustness and reduce false interpretations, the framework incorporates a contextual interpretation layer that evaluates deviations across multiple indicators jointly. Deviations detected in individual indices are interpreted in relation to complementary signals, allowing the system to account for interdependencies among vegetation vigor, water availability and environmental conditions. Multi-indicator interpretation has been shown to improve reliability in agricultural remote sensing by reducing ambiguity associated with single-index analysis (Atzberger, 2013; Bégué et al., 2018). The final layer focuses on interpretability and decision support. Analytical outputs are translated into grid-level maps, temporal trend visualizations and aggregated indicators that summarize spatial performance patterns across fields or regions. Such visualization-driven decision-support approaches are widely recognized as essential for bridging complex geospatial analytics and operational agricultural decision-making (Zhang & Kovacs, 2012; Schimmelpfennig, 2016). Overall, the system architecture emphasizes modularity, transparency and extensibility. Individual components can be adapted or extended to incorporate additional data sources, indicators, or analytical modules without altering the core framework. This design enables the proposed approach to serve as a generalizable foundation for precision agriculture, sustainability assessment and agricultural risk monitoring applications, in line with current directions in applied agricultural intelligence systems (Mulla, 2013; Bégué et al., 2018). 7. Data Sources and Preprocessing The proposed framework relies on multispectral satellite imagery and derived environmental indicators to construct grid-level spatiotemporal representations of agricultural landscapes. Data sources and preprocessing steps are designed to ensure analytical consistency, spatial alignment and robustness to common limitations of satellite-based observations, such as cloud contamination and irregular temporal coverage, which are widely recognized challenges in optical remote sensing (Zhu & Woodcock, 2012; Roy et al., 2014). 7.1 Satellite Data Sources Multispectral satellite imagery is obtained from the Sentinel-2 mission, which provides high-resolution optical data suitable for field-scale agricultural monitoring (Drusch et al., 2012). Sentinel-2 imagery offers a spatial resolution of approximately 10 m for key spectral bands relevant to vegetation and environmental analysis, making it well suited for precision agriculture and intra-field variability assessment (Atzberger, 2013; Mulla, 2013). Data retrieval is conducted over user-defined regions of interest and spans multiple growing seasons to support the construction of historical temporal baselines. From the retrieved imagery, a set of vegetation and environmental indices is computed to capture complementary aspects of crop and field conditions. These include indicators related to vegetation vigor and density, soil-adjusted canopy response, water content, pigmentation and photosynthetic efficiency (Rouse et al., 1974; Baret & Guyot, 1991; Huete et al., 2002). The selection of indices is intended to support multi-dimensional interpretation rather than reliance on a single proxy for crop health, consistent with recommendations from agricultural remote sensing studies (Atzberger, 2013; Bégué et al., 2018). 7.2 Temporal Coverage and Baseline Construction For each region of interest, multi-year historical data are collected to establish temporal baselines at the grid level. Historical observations consist of all available satellite data prior to the current analysis year, enabling the characterization of typical seasonal behavior and interannual variability for each grid cell (Verbesselt et al., 2010; Forkel et al., 2013). Current-year observations are treated separately to preserve the integrity of real-time or near-real-time analysis and to avoid introducing bias through inappropriate temporal smoothing. The framework requires a minimum level of historical data sufficiency to ensure reliable baseline estimation. Grid-level baselines are constructed only when adequate temporal coverage is available, reducing the risk of unstable deviation estimates arising from sparse or incomplete historical records, a concern frequently highlighted in time-series–based agricultural analyses (Lhermitte et al., 2011). 7.3 Cloud Cover Filtering and Data Quality Control To address the impact of cloud contamination on optical satellite imagery, strict cloud-cover filtering is applied during data acquisition. Imagery exceeding a predefined cloud-cover threshold is excluded from analysis to maintain data quality, following established best practices in satellite preprocessing (Zhu & Woodcock, 2012; Roy et al., 2014). When no suitable imagery is available for a given time step due to cloud constraints, the system applies controlled temporal backdating to identify the nearest valid observation that meets quality criteria, a strategy commonly used to mitigate gaps in satellite time series (Verbesselt et al., 2010). Additional data validation checks are performed to ensure the availability of required spectral bands and index inputs before further processing. Observations that fail to meet these quality requirements are excluded to prevent propagation of unreliable data into downstream analyses. 7.4 Spatial Grid Generation and Alignment Each region of interest is partitioned into uniform spatial grids aligned with the spatial resolution of the satellite imagery. Grid cells serve as the fundamental analytical units throughout the framework, allowing localized representation of spatial heterogeneity within agricultural fields, which is critical for precision agriculture applications (Mulla, 2013). All index computations, temporal baselines and deviation analyses are performed independently for each grid cell to preserve fine-scale spatial detail. Spatial alignment procedures ensure consistency across time by maintaining fixed grid boundaries for all historical and current observations. This approach enables direct temporal comparison of grid-level indicators without the need for spatial resampling or aggregation that could obscure localized patterns, as noted in spatiotemporal monitoring literature (Verbesselt et al., 2010). 7.5 Handling Missing Observations Missing observations arising from cloud cover or acquisition gaps are addressed through controlled preprocessing procedures. Temporal interpolation is applied selectively to historical data only when predefined data sufficiency conditions are met. These conditions include minimum thresholds for historical coverage and completeness, ensuring that interpolation does not introduce artificial trends or distort baseline behavior, in line with recommendations from time-series remote sensing studies (Forkel et al., 2013; Lhermitte et al., 2011). Current-year data are not interpolated to preserve the integrity of real-time analysis and avoid masking true anomalies. When data sufficiency conditions are not satisfied, affected grid cells are excluded from deviation analysis for the corresponding time steps, prioritizing analytical reliability over completeness. Methodology 8.1 Grid-Based Spatial Representation To capture fine-scale spatial variability within agricultural landscapes, the proposed framework adopts a grid-based spatial representation aligned with the spatial resolution of the underlying satellite imagery. Each region of interest is partitioned into uniform grid cells corresponding approximately to 10 m × 10 m ground units. This resolution balances spatial detail with computational feasibility and is well suited for field-level agricultural monitoring, as demonstrated in precision agriculture and high-resolution remote sensing studies (Mulla, 2013; Atzberger, 2013). Each grid cell is treated as an independent analytical unit throughout the pipeline. All satellite-derived indicators, temporal statistics and deviation metrics are computed at the grid level rather than aggregated across the entire field. This design allows the framework to represent intra-field heterogeneity arising from soil variability, irrigation patterns, management practices or microclimatic effects that are often obscured in field-averaged analyses (Lobell et al., 2009; Bégué et al., 2018). Grid boundaries are fixed for the duration of the analysis period to ensure spatial consistency across time. This enables direct temporal comparison of indicator values within each grid cell and supports the construction of grid-specific historical baselines, consistent with best practices in spatiotemporal environmental monitoring (Verbesselt et al., 2010). 8.2 Temporal Baseline Construction For each grid cell, historical temporal baselines are constructed using multi-year satellite-derived time series. Historical data consist of all observations prior to the current analysis year and are used to characterize typical seasonal behavior and interannual variability for each indicator (Forkel et al., 2013; de Beurs & Henebry, 2010). For a given indicator and grid cell, the temporal baseline is defined by the historical mean and standard deviation computed over the available multi-year record. These statistics serve as reference parameters against which current observations are evaluated. Baselines are computed independently for each grid cell to preserve localized temporal characteristics and avoid the use of regional or global averages, which have been shown to mask site-specific dynamics (Verbesselt et al., 2010; Atzberger, 2013). This localized baseline construction enables the framework to account for site-specific conditions, such that deviations are interpreted relative to a grid’s own historical behavior rather than against generalized thresholds. As a result, normal seasonal fluctuations are distinguished from meaningful departures that may indicate stress, underperformance, or favorable conditions (Lhermitte et al., 2011). 8.3 Spatiotemporal Deviation Analysis Spatiotemporal deviation analysis serves as the transition point between baseline-referenced signal comparison and downstream interpretive responses within the framework. Spatiotemporal deviation analysis forms the analytical core of the framework. For each grid cell and indicator, current observations are compared against the corresponding historical baseline using standardized deviation metrics. Deviations are quantified by computing the normalized difference between the current value and the historical mean, scaled by the historical standard deviation, following established anomaly detection principles (Chandola et al., 2009). This standardization allows deviations to be interpreted consistently across indicators with different value ranges and variances. Positive deviations indicate values exceeding typical historical behavior, while negative deviations reflect underperformance relative to the established baseline. To reduce sensitivity to minor fluctuations and noise, deviation thresholds are applied to identify only statistically and operationally meaningful departures, as commonly recommended in time-series remote sensing studies (Lhermitte et al., 2011; Zscheischler et al., 2014). These thresholds are treated as conservative, configurable parameters rather than fixed global cutoffs, allowing sensitivity to be adjusted based on local variability, indicator behavior and monitoring objectives. By evaluating deviations independently at the grid level, the framework captures both spatial and temporal variability, enabling the identification of localized anomaly patterns that may evolve gradually or emerge abruptly over time (Verbesselt et al., 2010). 8.4 Context-Aware Rule Logic To improve robustness and reduce false interpretations, the framework incorporates a context-aware rule logic that evaluates deviations across multiple indicators jointly rather than in isolation. Deviations detected in individual indicators are interpreted in the context of complementary signals to account for interdependencies among vegetation vigor, water availability and environmental conditions, an approach widely advocated in multi-index agricultural monitoring (Atzberger, 2013; Bégué et al., 2018). For example, deviations in water-related indicators are assessed alongside vegetation indices to avoid misclassifying healthy vegetation under transient moisture conditions as stressed. Conversely, simultaneous negative deviations across multiple complementary indicators are treated as stronger evidence of potential stress or underperformance, consistent with findings in multi-variable anomaly detection studies (Chandola et al., 2009). This contextual evaluation layer does not replace the deviation analysis but refines its interpretation by embedding domain-informed logical relationships among indicators. The result is a more stable and interpretable assessment of grid-level conditions under complex and variable agricultural environments (Lhermitte et al., 2011). 8.5 Missing Data Handling and Quality Control Satellite-derived time series frequently contain gaps due to cloud cover, acquisition constraints, or data quality issues. To address this, the framework implements strict data quality control and conditional handling of missing observations, reflecting best practices in optical remote sensing analysis (Zhu & Woodcock, 2012; Roy et al., 2014). Cloud-contaminated observations are excluded based on predefined quality thresholds. When gaps occur within historical data, temporal interpolation is applied selectively and only when data sufficiency conditions are satisfied. These conditions include minimum levels of historical completeness and temporal continuity to ensure that interpolation does not distort baseline characteristics or introduce artificial trends, as cautioned in time-series vegetation studies (Forkel et al., 2013; Lhermitte et al., 2011). Interpolation is restricted to historical data only. Current-year observations are not interpolated in order to preserve the integrity of real-time or near-real-time deviation analysis. If data sufficiency criteria are not met, deviation analysis is suspended for the affected grid cell and time step rather than forcing imputation. This conservative approach prioritizes analytical reliability over completeness, ensuring that detected deviations are driven by genuine signal behavior rather than preprocessing artifacts (Verbesselt et al., 2010). 9. Visualization and Decision-Support Outputs Effective agricultural monitoring systems must not only generate reliable analytical outputs but also present results in forms that support interpretation and decision-making across diverse user contexts. The proposed framework translates grid-level spatiotemporal deviation analyses into interpretable visual representations and summary indicators designed to support exploratory analysis, situational awareness and operational planning. The importance of interpretable geospatial visualization for decision support in agriculture has been widely emphasized in prior work (Atzberger, 2013; Zhang & Kovacs, 2012). 9.1 Grid-Level Spatial Visualization Grid-level outputs are visualized as spatial maps that represent standardized deviation metrics and aggregated performance indicators across the region of interest. Each grid cell is assigned a color-coded representation reflecting its deviation status relative to historical baselines. Such spatial visualization approaches are commonly used to support rapid identification of localized variability and anomaly patterns in agricultural and environmental monitoring (Mulla, 2013; Bégué et al., 2018). By maintaining a consistent spatial grid across time, visualizations support temporal comparison of spatial patterns, allowing users to observe the emergence, persistence, or resolution of localized anomalies over the course of a season. Preserving spatial consistency across time is a recognized best practice for spatiotemporal visualization and interpretation (Verbesselt et al., 2010). Spatial visualizations are designed to preserve geospatial context while minimizing visual complexity, ensuring clarity even at fine spatial resolutions. 9.2 Temporal Trend Visualization In addition to spatial representations, the framework provides temporal visualizations that depict historical and current trajectories of selected indicators at the grid or aggregated field level. Time-series plots illustrate the evolution of indicator values alongside historical baselines, enabling users to assess seasonal dynamics and identify deviations in a temporal context. Time-series visualization has been shown to play a critical role in interpreting vegetation dynamics and detecting abnormal behavior in remote sensing applications (Forkel et al., 2013; Lhermitte et al., 2011). Temporal visualizations support interpretation by distinguishing short-term fluctuations from sustained trends and by highlighting periods where observations depart meaningfully from expected behavior. When combined with spatial maps, these plots enable complementary spatial–temporal analysis, linking localized patterns to their temporal drivers, as advocated in spatiotemporal monitoring literature (Verbesselt et al., 2010). 9.3 Aggregated Field-Level Indicators To support high-level decision-making, grid-level deviation metrics are aggregated into summary indicators that characterize overall field or region performance. Aggregation is performed in a manner that preserves visibility into underlying spatial variability while providing concise metrics suitable for comparative analysis across time or between fields, consistent with approaches used in precision agriculture decision-support systems (Schimmelpfennig, 2016). These aggregated indicators serve as interpretive aids rather than standalone performance scores. They are intended to support monitoring of general trends, identification of periods requiring closer inspection and prioritization of areas for further analysis or intervention, rather than replacing localized analysis (Atzberger, 2013). 9.4 Interpretability and Decision Support All visual outputs are designed to emphasize interpretability and transparency rather than predictive certainty. By explicitly linking visual representations to standardized deviation metrics and localized historical baselines, the framework enables users to understand not only where anomalies occur, but also why they are identified. Interpretability has been repeatedly highlighted as a critical requirement for effective adoption of data-driven decision-support tools in agriculture (Mulla, 2013; Schimmelpfennig, 2016). Visualization outputs are structured to support decision-support workflows by enabling users to explore grid-level conditions, examine temporal context and synthesize information across multiple indicators. Rather than prescribing specific actions, the framework provides structured insights that can inform domain-specific decision-making in precision agriculture, sustainability assessment and risk monitoring contexts, aligning with best practices in agricultural decision-support system design (Zhang & Kovacs, 2012). 10. Demonstration Use Case To illustrate the application of the proposed framework, a representative agricultural region was selected for demonstration purposes. The objective of this use case is not to quantitatively validate predictive performance, but to demonstrate how grid-level spatiotemporal deviation analysis can be applied in practice to reveal spatial and temporal variability within an agricultural landscape. 10.1 Study Area and Data Configuration The demonstration region consists of an agricultural plot characterized by heterogeneous vegetation patterns and seasonal variability. The region of interest was defined using a spatial bounding box and partitioned into uniform grid cells aligned with the spatial resolution of the satellite imagery. Multi-year historical satellite data were retrieved for the region to construct grid-specific temporal baselines, while current-season observations were analyzed to identify deviations from historical norms. Satellite-derived vegetation and environmental indicators were computed for each grid cell following the preprocessing and quality-control procedures described in Section 7 . Only observations meeting cloud-cover and data completeness criteria were included in the analysis. 10.2 Grid-Level Spatiotemporal Patterns Application of the framework produced grid-level spatial representations of deviation behavior relative to historical baselines. Spatial visualizations illustrate how deviation signals, when present, may vary across individual grid cells within the region of interest, enabling localized inspection without assuming a predefined spatial structure. Depending on prevailing field conditions, grid-level deviations may appear sparse, widespread, or absent during a given observation period. Temporal visualizations further contextualize grid-level behavior by showing how deviations evolve over the course of the season. In some cases, deviations emerge gradually across successive observation windows, while in others they appear as short-lived departures that subsequently return toward baseline conditions. This temporal perspective supports differentiation between transient variability and sustained departures, without imposing assumptions regarding the spatial extent or persistence of deviations. 10.3 Contextual Interpretation Across Indicators The demonstration also illustrates the role of context-aware interpretation in refining deviation analysis. In selected grid cells, deviations detected in water-related indicators were evaluated alongside vegetation indices to assess whether observed changes were consistent with stress conditions or reflected normal adaptive behavior. In cases where vegetation indicators remained within typical ranges despite transient deviations in water-related signals, the framework suppressed isolated stress interpretations. Conversely, grid cells exhibiting concurrent deviations across multiple complementary indicators were identified as areas of potential concern. This multi-indicator interpretation reduced the likelihood of false positives and provided a more nuanced understanding of spatial variability under real-world conditions. 10.4 Interpretive Outputs for Decision Support The resulting spatial maps, temporal plots and aggregated indicators collectively demonstrate how the framework supports exploratory analysis and decision-support workflows. Grid-level outputs enable users to identify where deviations occur, while temporal context clarifies when deviations emerge and how they evolve. Aggregated indicators provide a high-level overview while preserving the ability to drill down into localized patterns. This demonstration highlights the framework’s capacity to translate complex spatiotemporal data into interpretable insights without relying on absolute thresholds or field-averaged metrics. The example use case illustrates how localized historical normalization and contextual interpretation can enhance understanding of agricultural landscape dynamics. Discussion The proposed grid-based spatiotemporal deviation framework addresses several limitations commonly observed in satellite-driven agricultural monitoring systems. By emphasizing localized historical normalization rather than absolute index values or static thresholds, the framework provides a more context-aware interpretation of agricultural dynamics at the field scale. This approach is particularly relevant in heterogeneous agricultural landscapes, where spatial variability, management practices and microclimatic effects can significantly influence observed satellite signals (Lobell et al., 2009; Atzberger, 2013). One of the key strengths of the framework lies in its grid-level temporal baselining strategy. Constructing historical baselines independently for each spatial unit enables the system to distinguish between normal seasonal behavior and meaningful deviations specific to local conditions. This localized perspective reduces the risk of misclassification that can arise when regional averages or generalized thresholds are applied across diverse fields or cropping systems, a limitation frequently noted in agricultural remote sensing studies (Verbesselt et al., 2010; Bégué et al., 2018). In addition, the use of standardized deviation metrics allows consistent interpretation across indicators with different ranges and variances, following established anomaly detection principles (Chandola et al., 2009). The integration of context-aware rule logic further enhances robustness by accounting for interdependencies among vegetation, water and environmental indicators. Agricultural systems often exhibit compensatory responses, where short-term deviations in one indicator may not correspond to adverse conditions if supported by complementary signals. Multi-indicator interpretation has been shown to reduce ambiguity and false positives compared to single-index approaches, particularly under variable environmental conditions (Atzberger, 2013; Lhermitte et al., 2011). By evaluating deviations jointly rather than in isolation, the framework supports more nuanced interpretation under complex field conditions. Interpretation of deviation magnitude and its impact on scoring and flagging is treated as a conservative and context-aware design choice rather than relying on fixed global cutoffs. Thresholds governing the influence of positive and negative deviations are applied to prioritize robustness and false-positive avoidance and are not assumed to be universally optimal across crops, regions, or monitoring objectives. This design reflects the inherent heterogeneity of agricultural systems and supports flexible sensitivity adjustment under differing environmental and operational contexts. The framework also explicitly prioritizes data quality and analytical reliability. Conservative cloud-cover filtering, controlled temporal backdating and strict conditions for interpolation are essential for maintaining the integrity of satellite-derived time series, as emphasized in prior work on optical satellite preprocessing (Zhu & Woodcock, 2012; Roy et al., 2014). Rather than forcing complete coverage through aggressive imputation, the framework opts to suspend analysis when data sufficiency criteria are not met. While this may reduce temporal completeness in certain scenarios, it enhances confidence in detected deviations and avoids introducing artifacts that could mislead interpretation, a trade-off widely discussed in time-series remote sensing literature (Forkel et al., 2013). Despite these strengths, several limitations should be acknowledged. The framework currently relies on optical satellite imagery, which remains sensitive to persistent cloud cover and atmospheric conditions in certain regions. Although quality-control measures mitigate this issue to some extent, extended data gaps can limit temporal continuity, particularly in tropical and monsoon-dominated environments (Roy et al., 2014). Additionally, the framework is designed as a general-purpose monitoring system and does not incorporate crop-specific calibration or phenological models, which may be required for detailed yield estimation or crop-type–specific interpretation (Mulla, 2013; Bolton & Friedl, 2013). Another limitation relates to the absence of large-scale quantitative validation in the present study. The demonstration use case is intended to illustrate methodological behavior rather than assess predictive performance. At the current stage, validation is primarily qualitative and operational in nature, informed through iterative feedback from pilot deployments and field users who assess detected deviations against observed field conditions. In this setting, inconsistencies between deviation signals and on-ground observations are surfaced through user feedback, enabling refinement of data quality controls, rule logic and interpretation layers. While such feedback does not substitute for formal quantitative validation against ground-based measurements, it provides an important mechanism for identifying systematic misinterpretations and operational edge cases during early-stage deployment. Deviation-based approaches, particularly those grounded directly in established satellite-derived indices, are inherently constrained by the physical meaning and empirical robustness of the underlying indicators. As a result, the likelihood of structurally implausible signals is reduced relative to black-box predictive models, though it is not eliminated. Comprehensive quantitative validation against field measurements, agronomic records and operational outcomes remains an important area for future work, as emphasized in precision agriculture research (Schimmelpfennig, 2016; Bégué et al., 2018). Overall, the discussion highlights a deliberate design trade-off between generality, interpretability and validation depth. By focusing on methodological transparency, localized normalization, and robustness to real-world data constraints, the proposed framework provides a solid foundation for operational decision-support applications. As additional ground-truth data and structured pilot deployments become available, the framework can be further refined and extended to support validation-driven analyses and domain-specific optimization, consistent with current directions in applied agricultural intelligence systems (Atzberger, 2013; Mulla, 2013). Conclusion This paper presented a grid-based spatiotemporal deviation framework for agricultural landscape monitoring that emphasizes localized historical normalization and context-aware interpretation of satellite-derived indicators. By moving beyond absolute index values and static thresholds, the proposed approach enables more nuanced and spatially explicit assessment of agricultural dynamics at the field scale. The framework integrates fine-resolution spatial representation, multi-year temporal baselining, standardized deviation analysis and conservative data quality controls into a modular GeoAI-based system. Through grid-level analysis and contextual evaluation across multiple indicators, the approach supports robust interpretation of spatiotemporal variability while remaining resilient to common limitations of optical satellite data. Rather than focusing on predictive accuracy or crop-specific calibration, this work emphasizes methodological design and system architecture as a foundation for decision-support applications in precision agriculture, sustainability assessment and agricultural risk monitoring. A representative demonstration illustrated how localized deviation analysis can reveal patterns that may be obscured in field-averaged or threshold-based monitoring systems. The proposed framework provides a transparent and extensible basis for operational agricultural monitoring under real-world data constraints. As additional ground-based observations and pilot deployments become available, the approach can be further evaluated and adapted to support validation-driven studies and domain-specific extensions. Future Work Several directions for future research emerge from the proposed framework. A primary focus of future work will be quantitative validation using ground-based observations and operational farm data across diverse agro-ecological regions. Such validation will enable systematic assessment of deviation-based indicators in relation to observed crop performance, stress events and management outcomes, addressing a widely acknowledged gap between satellite-based monitoring and field-level validation in precision agriculture research (Mulla, 2013; Bégué et al., 2018). Future extensions of the framework will also explore crop-specific and phenology-aware adaptations. Incorporating crop calendars, growth-stage segmentation and phenological markers may improve temporal baseline construction and contextual interpretation, particularly for multi-cropping systems and regions with irregular planting schedules. Phenology-aware analysis has been shown to enhance the interpretability of vegetation time series and reduce ambiguity in seasonal comparisons (de Beurs & Henebry, 2010; Bolton & Friedl, 2013). Integration of additional data sources represents another important avenue for development. The inclusion of weather reanalysis products, soil property datasets and management records has the potential to enhance deviation interpretation and support more comprehensive agricultural intelligence. Previous studies have highlighted the value of combining satellite-derived indicators with climatic and management data to better attribute observed variability to underlying drivers (Lobell et al., 2009; Atzberger, 2013). Methodological extensions may also investigate adaptive deviation thresholds and learning-based rule refinement. While the current framework employs standardized deviation metrics and rule-based contextual logic to prioritize interpretability, future research could explore hybrid approaches that combine these principles with data-driven calibration as larger datasets become available. Such hybrid strategies have been suggested as a means to balance robustness, transparency and adaptability in agricultural monitoring systems (Chandola et al., 2009). Finally, future work will focus on scaling the framework for broader operational deployment and longitudinal studies. This includes evaluating computational efficiency, cross-region transferability and long-term stability of grid-level baselines under changing climatic conditions. Long-term consistency and scalability are increasingly recognized as critical requirements for operational agricultural monitoring under climate variability and change (Verbesselt et al., 2010; Atzberger, 2013). Together, these efforts aim to extend the framework from a foundational monitoring system toward a validated and adaptable platform for decision-support in sustainable agriculture. Declarations Competing Interests The author is affiliated with Sensegrass, which develops applied geospatial intelligence systems. The work presented in this paper focuses on methodological design and does not evaluate or benchmark any commercial implementation. Author Contribution A.D.M. conceived the study, designed the analytical framework, developed and implemented the methodology, conducted the analysis, interpreted the results and wrote the manuscript. Data Availability The analyses in this study are based on publicly available satellite imagery and derived indices accessed through established remote sensing platforms. Data availability is subject to the terms and access conditions of the respective data providers. No proprietary or field-collected datasets were used. Acknowledgements The author gratefully acknowledges Sensegrass for providing the institutional support, technical resources and research environment necessary to conduct this research and development work. The study benefited from the opportunity to explore, design and iterate on the proposed framework within a real-world applied context. The author would like to thank Lalit Gautam, Hem Raj Pandey and Saurabh Dixit for their constructive discussions, technical input and support throughout the development of the system. Their contributions helped inform the practical and methodological aspects of the work. The author also acknowledges the continued support and encouragement of friends and family, which contributed to the successful completion of this research. References Atzberger, C. (2013). Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs . Remote Sensing , 5(2), 949–981. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs Baret, F. & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment . Remote Sensing of Environment , 35(2–3), 161–173. Potentials and limits of vegetation indices for LAI and APAR assessment - ScienceDirect Bégué, A., Arvor, D., Bellón, B., Betbeder, J., de Abelleyra, D., Ferraz, R.P.D., Lebourgeois, V., Lelong, C., Simões, M. & Verón, S.R. (2018). Remote sensing and cropping practices: A review . Remote Sensing , 10(1), 99. Remote Sensing and Cropping Practices: A Review Bolton, D.K. & Friedl, M.A. (2013). Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics . Agricultural and Forest Meteorology , 173, 74–84. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics - ScienceDirect Chandola, V., Banerjee, A. & Kumar, V. (2009). Anomaly detection: A survey . ACM Computing Surveys , 41(3), 1–58. (PDF) Anomaly Detection: A Survey de Beurs, K.M. & Henebry, G.M. (2010). Spatio-temporal statistical methods for modelling land surface phenology . International Journal of Geographical Information Science , 24(2), 199–218. Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology | SpringerLink Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F. & Bargellini, P. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services . Remote Sensing of Environment , 120, 25–36. Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services - ScienceDirect Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M.D., Neigh, C.S.R. & Reichstein, M. (2013). Trend change detection in NDVI time series: Effects of inter-annual variability and methodology . Remote Sensing , 5(5), 2113–2144. Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone . Remote Sensing of Environment , 202, 18–27. Google Earth Engine: Planetary-scale geospatial analysis for everyone - ScienceDirect Huete, A.R., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices . Remote Sensing of Environment , 83(1–2), 195–213. Overview of the radiometric and biophysical performance of the MODIS vegetation indices - ScienceDirect Lhermitte, S., Verbesselt, J., Verstraeten, W.W. & Coppin, P. (2011). A comparison of time series similarity measures for classification and change detection of ecosystem dynamics . Remote Sensing of Environment , 115(12), 3129–3152. A comparison of time series similarity measures for classification and change detection of ecosystem dynamics - ScienceDirect Lobell, D.B., Cassman, K.G & Field, C.B. (2009). Crop yield gaps: Their importance, magnitudes, and causes . Annual Review of Environment and Resources , 34, 179–204. Crop Yield Gaps: Their Importance, Magnitudes, and Causes | Annual Reviews Mulla, D.J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps . Biosystems Engineering , 114(4), 358–371. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps - ScienceDirect Peters, A.J., Walter-Shea, E.A., Ji, L., Viña, A., Hayes, M. & Svoboda, M.D. (2015). Drought monitoring with NDVI-based standardized vegetation index . Remote Sensing of Environment , 86(3), 392–402. (PDF) Drought monitoring with NDVI-based Standardized Vegetation Index Rouse, J.W., Haas, R.H., Schell, J.A. & Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS . In: Proceedings of the Third Earth Resources Technology Satellite-1 Symposium , NASA SP-351, 309–317. Monitoring vegetation systems in the Great Plains with ERTS - NASA Technical Reports Server (NTRS) Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., et al. (2014). Landsat-8: Science and product vision for terrestrial global change research . Remote Sensing of Environment , 145, 154–172. Landsat-8: Science and product vision for terrestrial global change research - ScienceDirect Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture . Economic Research Report No. 217 . United States Department of Agriculture. (PDF) Farm Profits and Adoption of Precision Agriculture Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation . Remote Sensing of Environment , 8(2), 127–150. DOI: https://doi.org/10.1016/0034-4257(79)90013-0 Verbesselt, J., Hyndman, R., Newnham, G. & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series . Remote Sensing of Environment , 114(1), 106–115. DOI: 10.1016/j.rse.2009.08.014 research.wur.nl Zhang, C. & Kovacs, J.M. (2012). The application of small unmanned aerial systems for precision agriculture: A review . Precision Agriculture , 13(6), 693–712. The application of small unmanned aerial systems for precision agriculture: a review Zhu, Z. & Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery . Remote Sensing of Environment , 118, 83–94. DOI: Object-based cloud and cloud shadow detection in Landsat imagery - ScienceDirect Zscheischler, J., Mahecha, M.D., Harmeling, S. & Reichstein, M. (2014). Detection and attribution of large spatiotemporal extreme events in Earth observation data . Remote Sensing of Environment , 150, 148–160. DOI: 10.1016/j.ecoinf.2013.03.004 Additional Declarations Competing interest reported. The author is affiliated with Sensegrass, which develops applied geospatial intelligence systems. The work presented in this paper focuses on methodological design and does not evaluate or benchmark any commercial implementation. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8430837","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566487448,"identity":"eca1faae-1e98-4a8c-8abd-ddedf19b1078","order_by":0,"name":"Ayashi Das Majumder","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYJACZgYDG9K1pJGsheEwCcr5ZyQ//lxQcN6eX7r94cOve+zs5RuYHz66gUeLxI00M+kZBrcTZ845Y2ws8yw5ccMBNmPjHHzW3EgwY+YxuJ1gcCOHTVriAHOCAQMPmzQ+LfI30j9/5jE4Z29/I/35b4kD9UCHEdACNNxAmsfgAOMGiQQzxg8HDjM2HCCgxfDMmzKgluTEGTdyjKUZDhxP3HCYgF/kjqdv/szzx86ef0b6w48/DlTby7c3P3yM1/sCCQg2Mw+YxKccBPgPINiMPwipHgWjYBSMghEJAJ9fS4LcTg7KAAAAAElFTkSuQmCC","orcid":"","institution":"Sensegrass Inc.","correspondingAuthor":true,"prefix":"","firstName":"Ayashi","middleName":"Das","lastName":"Majumder","suffix":""}],"badges":[],"createdAt":"2025-12-23 06:38:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8430837/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8430837/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99162313,"identity":"b4a07ff9-795d-4318-b3aa-75901aefb204","added_by":"auto","created_at":"2025-12-29 13:18:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3712851,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchArticleManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/12171d39f277c9f4020654ed.docx"},{"id":99315973,"identity":"20ab8feb-41fa-438c-9570-ba5fb846b5a2","added_by":"auto","created_at":"2025-12-31 16:27:30","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4397,"visible":true,"origin":"","legend":"","description":"","filename":"558d5f64a7824136b2333408896f7de8.json","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/d684ac0411e69997be6226d7.json"},{"id":99316524,"identity":"1d796701-6e35-4546-9f0f-3bfc27b66fee","added_by":"auto","created_at":"2025-12-31 16:28:33","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86942,"visible":true,"origin":"","legend":"","description":"","filename":"558d5f64a7824136b2333408896f7de81enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/a4e6bf008c15b06d9b67b00a.xml"},{"id":99317207,"identity":"64845d17-4a3d-4c17-9a6f-1bfe5e80b714","added_by":"auto","created_at":"2025-12-31 16:29:45","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41196,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/7c2e9c56d5ac30e80ed5ef90.png"},{"id":99162316,"identity":"ebd25ba0-6789-47ff-9509-5407b4f24055","added_by":"auto","created_at":"2025-12-29 13:18:41","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":231270,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/509aa620a9cbb7c421061a34.png"},{"id":99162317,"identity":"32a4b22f-69b5-4450-a862-a41e3f27a357","added_by":"auto","created_at":"2025-12-29 13:18:41","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82128,"visible":true,"origin":"","legend":"","description":"","filename":"558d5f64a7824136b2333408896f7de81structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/9b4c91ffe75e544452022a78.xml"},{"id":99315930,"identity":"03244687-ccbd-4338-b589-5746e772c85f","added_by":"auto","created_at":"2025-12-31 16:27:29","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96766,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/1c72da3f22699f65914290b4.html"},{"id":99162309,"identity":"d7c3ebd5-cf27-4209-8e12-510fe5511f06","added_by":"auto","created_at":"2025-12-29 13:18:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic overview of the proposed analytical framework, showing the end-to-end workflow from satellite data ingestion and grid-level processing to deviation analysis, rule-based interpretation and visualization outputs.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/14af1831f8de33fa7ab259d4.png"},{"id":99162311,"identity":"573638d4-989f-4d3f-a41a-8721748fd0a2","added_by":"auto","created_at":"2025-12-29 13:18:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1399037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGrid-level spatial visualization of derived grid scores for a representative application site. The figure illustrates how grid-based partitioning and deviation-derived scores are spatially represented over satellite imagery for a specific region of interest, serving as a demonstration of the framework’s spatial interpretability.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/32d267a4eeb756aa3d848abb.png"},{"id":101204989,"identity":"1a5674b9-6392-4eb4-a346-dc054cb6498e","added_by":"auto","created_at":"2026-01-27 09:45:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3666731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8430837/v1/7733e87c-45d2-4247-a838-32327e9e23ed.pdf"}],"financialInterests":"Competing interest reported. The author is affiliated with Sensegrass, which develops applied geospatial intelligence systems. The work presented in this paper focuses on methodological design and does not evaluate or benchmark any commercial implementation.","formattedTitle":"A Grid-Based Spatiotemporal Deviation Framework for Agricultural Landscape Monitoring Using Remote Sensing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSatellite remote sensing has become a cornerstone of modern agricultural monitoring, enabling large-scale observation of vegetation dynamics, water availability and environmental conditions (Tucker, 1979; Mulla, 2013; Zhang \u0026amp; Kovacs, 2012). Vegetation indices derived from multispectral imagery\u0026mdash;such as the Normalized Difference Vegetation Index (NDVI) and its variants\u0026mdash;are widely used to assess crop health, seasonal growth patterns and stress conditions (Rouse et al., 1974; Huete et al., 2002; Baret \u0026amp; Guyot, 1991). Despite their extensive adoption, many operational agricultural monitoring systems continue to rely on absolute index values or static threshold-based interpretations, which often fail to capture localized variability and temporal context at the field scale (Verbesselt et al., 2010; Lhermitte et al., 2011).\u003c/p\u003e \u003cp\u003eAgricultural landscapes are inherently heterogeneous, with crop performance influenced by site-specific factors such as soil properties, microclimate, management practices and historical land use (Lobell et al., 2009). As a result, identical index values may represent markedly different conditions across fields, seasons, or regions. Systems that evaluate current observations without reference to localized historical baselines risk misinterpreting normal seasonal fluctuations as stress events, or conversely, overlooking gradual degradation trends that remain within nominal threshold ranges (B\u0026eacute;gu\u0026eacute; et al., 2018). This limitation is particularly pronounced in precision agriculture, where decision-making increasingly depends on fine-scale, temporally aware insights rather than regional averages (Mulla, 2013; Schimmelpfennig, 2016).\u003c/p\u003e \u003cp\u003eRecent research has highlighted the importance of time-series analysis and anomaly detection techniques for improving agricultural monitoring (Verbesselt et al., 2010; Chandola et al., 2009). Approaches based on temporal normalization, trend analysis, and deviation detection offer a means to contextualize current observations relative to historical behavior (Lhermitte et al., 2011; Forkel et al., 2013). However, many existing methods operate at coarse spatial resolutions, apply uniform baselines across large regions, or lack mechanisms to integrate multiple indices in a context-aware manner (Atzberger, 2013). Furthermore, practical challenges such as cloud contamination, missing observations and inconsistent data availability remain significant barriers to reliable operational deployment of satellite-based agricultural monitoring systems (Zhu \u0026amp; Woodcock, 2012; Roy et al., 2014).\u003c/p\u003e \u003cp\u003eTo address these challenges, this paper presents a grid-based spatiotemporal deviation framework for agricultural landscape monitoring. The proposed approach partitions agricultural regions into fine-resolution spatial grids and constructs grid-specific temporal baselines using multi-year satellite-derived data. Rather than relying on absolute index thresholds, current observations are evaluated through standardized deviation metrics that quantify departures from historical norms at the grid level, following established anomaly detection principles (Chandola et al., 2009). This enables localized and temporally grounded interpretation of vegetation and environmental dynamics.\u003c/p\u003e \u003cp\u003eA key aspect of the framework is its emphasis on contextual interpretation across multiple indicators. Deviations in individual indices are evaluated in relation to complementary signals to reduce false positives and improve robustness under complex field conditions, consistent with findings from multi-indicator agricultural monitoring studies (B\u0026eacute;gu\u0026eacute; et al., 2018; Atzberger, 2013). In addition, the framework incorporates strict data quality controls, including cloud-cover filtering and conditional temporal interpolation, to ensure analytical reliability when working with real-world satellite datasets (Zhu \u0026amp; Woodcock, 2012; Gorelick et al., 2017).\u003c/p\u003e \u003cp\u003eThe contribution of this work lies in the design and implementation of a modular GeoAI-based framework that integrates fine-scale spatial representation, temporal normalization, deviation analysis and interpretable outputs for agricultural decision support. Rather than focusing on predictive accuracy or crop-specific calibration, the paper emphasizes methodological design choices and system architecture that enable scalable, extensible and context-aware agricultural monitoring. A representative demonstration is included to illustrate the practical application of the framework and its potential to support precision agriculture, sustainability assessment and agricultural risk monitoring workflows.\u003c/p\u003e\n\u003ch3\u003e5. Related Work\u003c/h3\u003e\n\u003cp\u003eRemote sensing has long been used for agricultural monitoring, with vegetation indices derived from multispectral satellite imagery serving as primary indicators of crop health and productivity (Tucker, 1979; Baret \u0026amp; Guyot, 1991; Mulla, 2013). Indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI) have been widely applied for assessing biomass, canopy vigor and seasonal growth dynamics across a range of cropping systems (Rouse et al., 1974; Huete et al., 2002; Atzberger, 2013). Numerous studies have demonstrated the utility of these indices for crop monitoring, drought assessment and yield-related analysis across diverse agro-ecological regions (Lobell et al., 2009; B\u0026eacute;gu\u0026eacute; et al., 2018; Bolton \u0026amp; Friedl, 2013).\u003c/p\u003e \u003cp\u003eBuilding on these foundations, time-series analysis techniques have been increasingly adopted to capture temporal dynamics in vegetation signals. Methods based on seasonal trend extraction, phenological metrics and temporal smoothing have improved the interpretation of satellite-derived observations by reducing noise and highlighting long-term patterns (Verbesselt et al., 2010; Forkel et al., 2013; de Beurs \u0026amp; Henebry, 2010). More recent work has explored anomaly detection approaches, where deviations from expected temporal behavior are used to identify stress events, yield losses, or abnormal growth conditions (Lhermitte et al., 2011; Chandola et al., 2009). Such approaches have shown promise in enhancing early warning capabilities compared to static, single-date assessments, particularly under variable climatic conditions (Zscheischler et al., 2014; Peters et al., 2015).\u003c/p\u003e \u003cp\u003eDespite these advances, several limitations persist in existing agricultural monitoring systems. Many approaches operate at coarse spatial resolutions or rely on regional averages, limiting their applicability for field-level decision-making (Atzberger, 2013; Mulla, 2013). Even when higher-resolution imagery is available, temporal baselines are often constructed at aggregated spatial or administrative scales, which can obscure localized variability driven by soil heterogeneity, management practices, or microclimatic effects (Lobell \u0026amp; Burke, 2010; B\u0026eacute;gu\u0026eacute; et al., 2018). As a result, absolute index values or uniform thresholds may lead to misinterpretation when applied across diverse fields or seasons (Baret \u0026amp; Guyot, 1991; Verbesselt et al., 2010).\u003c/p\u003e \u003cp\u003eDecision-support platforms that integrate satellite data into operational tools have further expanded the accessibility of remote sensing for agriculture. These systems commonly provide dashboards, maps and index-based indicators to support farm management and planning (Zhang \u0026amp; Kovacs, 2012). However, many such platforms emphasize visualization over methodological transparency and often lack mechanisms for contextual interpretation across multiple indices (Atzberger, 2013). Additionally, practical challenges such as cloud contamination, missing observations and inconsistent temporal coverage are frequently handled with limited or undocumented preprocessing strategies, affecting analytical robustness and reproducibility (Zhu \u0026amp; Woodcock, 2012; Roy et al., 2014).\u003c/p\u003e \u003cp\u003eIn response to these gaps, recent research has emphasized the need for localized, temporally normalized analysis frameworks that combine fine-scale spatial representation with robust data quality controls (Verbesselt et al., 2010; Forkel et al., 2013). Approaches that evaluate relative performance against historical baselines, rather than relying solely on absolute index values, have been identified as particularly important for precision agriculture and sustainability monitoring (Lhermitte et al., 2011; B\u0026eacute;gu\u0026eacute; et al., 2018). However, there remains a need for integrated frameworks that systematically combine grid-level spatial analysis, multi-year temporal baselining, contextual multi-index interpretation and interpretable outputs within a single, extensible system.\u003c/p\u003e \u003cp\u003eThe framework presented in this paper builds on existing remote sensing and time-series analysis research by focusing on grid-level spatiotemporal deviation analysis tailored to agricultural landscapes. By emphasizing localized historical normalization, contextual interpretation of multiple indicators and explicit handling of real-world data constraints, the proposed approach addresses several limitations of existing methods while remaining compatible with operational decision-support applications.\u003c/p\u003e\n\u003ch3\u003e6. System Overview\u003c/h3\u003e\n\u003cp\u003eThe proposed framework is designed as a modular GeoAI-based system for grid-level spatiotemporal monitoring of agricultural landscapes. Its primary objective is to enable localized, temporally contextual interpretation of satellite-derived indicators while remaining robust to real-world data constraints such as cloud contamination and irregular temporal coverage, which are well-documented challenges in optical remote sensing (Zhu \u0026amp; Woodcock, 2012; Roy et al., 2014). The system follows a layered architecture that separates data acquisition, analytical processing, deviation analysis and interpretability-oriented outputs, consistent with best practices in environmental decision-support system design (Atzberger, 2013).\u003c/p\u003e \u003cp\u003eAt the highest level, the framework consists of four functional layers: (i) data acquisition and quality control, (ii) spatial\u0026ndash;temporal structuring, (iii) deviation analysis and contextual interpretation and (iv) visualization and decision-support outputs. Such modular, layered architectures are commonly adopted in geospatial analytics and agricultural monitoring systems to support scalability, transparency and extensibility (Mulla, 2013; Gorelick et al., 2017). Each layer is designed to operate independently while contributing to a coherent analytical pipeline.\u003c/p\u003e \u003cp\u003eThe data acquisition layer is responsible for retrieving multispectral satellite imagery and associated environmental data over user-defined regions of interest. Data are filtered using strict quality criteria, including cloud-cover thresholds and completeness checks, to ensure that only analytically reliable observations are passed to downstream processes. When suitable imagery is unavailable for a given time step, the system applies controlled temporal backdating to identify the nearest valid observation, a strategy commonly employed to mitigate data gaps in satellite time series without introducing artificial observations (Zhu \u0026amp; Woodcock, 2012; Verbesselt et al., 2010).\u003c/p\u003e \u003cp\u003eIn the spatial\u0026ndash;temporal structuring layer, the region of interest is partitioned into uniform spatial grids corresponding to the spatial resolution of the satellite imagery. Each grid cell is treated as an independent analytical unit, enabling fine-scale representation of spatial heterogeneity within agricultural fields, which is critical for precision agriculture applications (Mulla, 2013). For each grid, multi-year historical time series are constructed for selected vegetation and environmental indicators, forming the basis for localized temporal baselines as recommended in spatiotemporal agricultural monitoring studies (Verbesselt et al., 2010; Forkel et al., 2013).\u003c/p\u003e \u003cp\u003eThe analytical core of the framework performs spatiotemporal deviation analysis by comparing current observations against grid-specific historical baselines. Rather than evaluating absolute index values, the system quantifies standardized deviations to identify significant departures from expected temporal behavior, following established principles in time-series anomaly detection (Chandola et al., 2009; Lhermitte et al., 2011). This design allows the framework to distinguish between normal seasonal variability and meaningful anomalies that may indicate stress, underperformance or favorable conditions.\u003c/p\u003e \u003cp\u003eTo improve robustness and reduce false interpretations, the framework incorporates a contextual interpretation layer that evaluates deviations across multiple indicators jointly. Deviations detected in individual indices are interpreted in relation to complementary signals, allowing the system to account for interdependencies among vegetation vigor, water availability and environmental conditions. Multi-indicator interpretation has been shown to improve reliability in agricultural remote sensing by reducing ambiguity associated with single-index analysis (Atzberger, 2013; B\u0026eacute;gu\u0026eacute; et al., 2018).\u003c/p\u003e \u003cp\u003eThe final layer focuses on interpretability and decision support. Analytical outputs are translated into grid-level maps, temporal trend visualizations and aggregated indicators that summarize spatial performance patterns across fields or regions. Such visualization-driven decision-support approaches are widely recognized as essential for bridging complex geospatial analytics and operational agricultural decision-making (Zhang \u0026amp; Kovacs, 2012; Schimmelpfennig, 2016).\u003c/p\u003e \u003cp\u003eOverall, the system architecture emphasizes modularity, transparency and extensibility. Individual components can be adapted or extended to incorporate additional data sources, indicators, or analytical modules without altering the core framework. This design enables the proposed approach to serve as a generalizable foundation for precision agriculture, sustainability assessment and agricultural risk monitoring applications, in line with current directions in applied agricultural intelligence systems (Mulla, 2013; B\u0026eacute;gu\u0026eacute; et al., 2018).\u003c/p\u003e\n\u003ch3\u003e7. Data Sources and Preprocessing\u003c/h3\u003e\n\u003cp\u003eThe proposed framework relies on multispectral satellite imagery and derived environmental indicators to construct grid-level spatiotemporal representations of agricultural landscapes. Data sources and preprocessing steps are designed to ensure analytical consistency, spatial alignment and robustness to common limitations of satellite-based observations, such as cloud contamination and irregular temporal coverage, which are widely recognized challenges in optical remote sensing (Zhu \u0026amp; Woodcock, 2012; Roy et al., 2014).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Satellite Data Sources\u003c/h2\u003e \u003cp\u003eMultispectral satellite imagery is obtained from the Sentinel-2 mission, which provides high-resolution optical data suitable for field-scale agricultural monitoring (Drusch et al., 2012). Sentinel-2 imagery offers a spatial resolution of approximately 10 m for key spectral bands relevant to vegetation and environmental analysis, making it well suited for precision agriculture and intra-field variability assessment (Atzberger, 2013; Mulla, 2013). Data retrieval is conducted over user-defined regions of interest and spans multiple growing seasons to support the construction of historical temporal baselines.\u003c/p\u003e \u003cp\u003eFrom the retrieved imagery, a set of vegetation and environmental indices is computed to capture complementary aspects of crop and field conditions. These include indicators related to vegetation vigor and density, soil-adjusted canopy response, water content, pigmentation and photosynthetic efficiency (Rouse et al., 1974; Baret \u0026amp; Guyot, 1991; Huete et al., 2002). The selection of indices is intended to support multi-dimensional interpretation rather than reliance on a single proxy for crop health, consistent with recommendations from agricultural remote sensing studies (Atzberger, 2013; B\u0026eacute;gu\u0026eacute; et al., 2018).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Temporal Coverage and Baseline Construction\u003c/h2\u003e \u003cp\u003eFor each region of interest, multi-year historical data are collected to establish temporal baselines at the grid level. Historical observations consist of all available satellite data prior to the current analysis year, enabling the characterization of typical seasonal behavior and interannual variability for each grid cell (Verbesselt et al., 2010; Forkel et al., 2013). Current-year observations are treated separately to preserve the integrity of real-time or near-real-time analysis and to avoid introducing bias through inappropriate temporal smoothing.\u003c/p\u003e \u003cp\u003eThe framework requires a minimum level of historical data sufficiency to ensure reliable baseline estimation. Grid-level baselines are constructed only when adequate temporal coverage is available, reducing the risk of unstable deviation estimates arising from sparse or incomplete historical records, a concern frequently highlighted in time-series\u0026ndash;based agricultural analyses (Lhermitte et al., 2011).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Cloud Cover Filtering and Data Quality Control\u003c/h2\u003e \u003cp\u003eTo address the impact of cloud contamination on optical satellite imagery, strict cloud-cover filtering is applied during data acquisition. Imagery exceeding a predefined cloud-cover threshold is excluded from analysis to maintain data quality, following established best practices in satellite preprocessing (Zhu \u0026amp; Woodcock, 2012; Roy et al., 2014). When no suitable imagery is available for a given time step due to cloud constraints, the system applies controlled temporal backdating to identify the nearest valid observation that meets quality criteria, a strategy commonly used to mitigate gaps in satellite time series (Verbesselt et al., 2010).\u003c/p\u003e \u003cp\u003eAdditional data validation checks are performed to ensure the availability of required spectral bands and index inputs before further processing. Observations that fail to meet these quality requirements are excluded to prevent propagation of unreliable data into downstream analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Spatial Grid Generation and Alignment\u003c/h2\u003e \u003cp\u003eEach region of interest is partitioned into uniform spatial grids aligned with the spatial resolution of the satellite imagery. Grid cells serve as the fundamental analytical units throughout the framework, allowing localized representation of spatial heterogeneity within agricultural fields, which is critical for precision agriculture applications (Mulla, 2013). All index computations, temporal baselines and deviation analyses are performed independently for each grid cell to preserve fine-scale spatial detail.\u003c/p\u003e \u003cp\u003eSpatial alignment procedures ensure consistency across time by maintaining fixed grid boundaries for all historical and current observations. This approach enables direct temporal comparison of grid-level indicators without the need for spatial resampling or aggregation that could obscure localized patterns, as noted in spatiotemporal monitoring literature (Verbesselt et al., 2010).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e7.5 Handling Missing Observations\u003c/h2\u003e \u003cp\u003eMissing observations arising from cloud cover or acquisition gaps are addressed through controlled preprocessing procedures. Temporal interpolation is applied selectively to historical data only when predefined data sufficiency conditions are met. These conditions include minimum thresholds for historical coverage and completeness, ensuring that interpolation does not introduce artificial trends or distort baseline behavior, in line with recommendations from time-series remote sensing studies (Forkel et al., 2013; Lhermitte et al., 2011).\u003c/p\u003e \u003cp\u003eCurrent-year data are not interpolated to preserve the integrity of real-time analysis and avoid masking true anomalies. When data sufficiency conditions are not satisfied, affected grid cells are excluded from deviation analysis for the corresponding time steps, prioritizing analytical reliability over completeness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e8.1 Grid-Based Spatial Representation\u003c/h2\u003e \u003cp\u003eTo capture fine-scale spatial variability within agricultural landscapes, the proposed framework adopts a grid-based spatial representation aligned with the spatial resolution of the underlying satellite imagery. Each region of interest is partitioned into uniform grid cells corresponding approximately to 10 m \u0026times; 10 m ground units. This resolution balances spatial detail with computational feasibility and is well suited for field-level agricultural monitoring, as demonstrated in precision agriculture and high-resolution remote sensing studies (Mulla, 2013; Atzberger, 2013).\u003c/p\u003e \u003cp\u003eEach grid cell is treated as an independent analytical unit throughout the pipeline. All satellite-derived indicators, temporal statistics and deviation metrics are computed at the grid level rather than aggregated across the entire field. This design allows the framework to represent intra-field heterogeneity arising from soil variability, irrigation patterns, management practices or microclimatic effects that are often obscured in field-averaged analyses (Lobell et al., 2009; B\u0026eacute;gu\u0026eacute; et al., 2018).\u003c/p\u003e \u003cp\u003eGrid boundaries are fixed for the duration of the analysis period to ensure spatial consistency across time. This enables direct temporal comparison of indicator values within each grid cell and supports the construction of grid-specific historical baselines, consistent with best practices in spatiotemporal environmental monitoring (Verbesselt et al., 2010).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e8.2 Temporal Baseline Construction\u003c/h2\u003e \u003cp\u003eFor each grid cell, historical temporal baselines are constructed using multi-year satellite-derived time series. Historical data consist of all observations prior to the current analysis year and are used to characterize typical seasonal behavior and interannual variability for each indicator (Forkel et al., 2013; de Beurs \u0026amp; Henebry, 2010).\u003c/p\u003e \u003cp\u003eFor a given indicator and grid cell, the temporal baseline is defined by the historical mean and standard deviation computed over the available multi-year record. These statistics serve as reference parameters against which current observations are evaluated. Baselines are computed independently for each grid cell to preserve localized temporal characteristics and avoid the use of regional or global averages, which have been shown to mask site-specific dynamics (Verbesselt et al., 2010; Atzberger, 2013).\u003c/p\u003e \u003cp\u003eThis localized baseline construction enables the framework to account for site-specific conditions, such that deviations are interpreted relative to a grid\u0026rsquo;s own historical behavior rather than against generalized thresholds. As a result, normal seasonal fluctuations are distinguished from meaningful departures that may indicate stress, underperformance, or favorable conditions (Lhermitte et al., 2011).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e8.3 Spatiotemporal Deviation Analysis\u003c/h2\u003e \u003cp\u003eSpatiotemporal deviation analysis serves as the transition point between baseline-referenced signal comparison and downstream interpretive responses within the framework. Spatiotemporal deviation analysis forms the analytical core of the framework. For each grid cell and indicator, current observations are compared against the corresponding historical baseline using standardized deviation metrics. Deviations are quantified by computing the normalized difference between the current value and the historical mean, scaled by the historical standard deviation, following established anomaly detection principles (Chandola et al., 2009).\u003c/p\u003e \u003cp\u003eThis standardization allows deviations to be interpreted consistently across indicators with different value ranges and variances. Positive deviations indicate values exceeding typical historical behavior, while negative deviations reflect underperformance relative to the established baseline. To reduce sensitivity to minor fluctuations and noise, deviation thresholds are applied to identify only statistically and operationally meaningful departures, as commonly recommended in time-series remote sensing studies (Lhermitte et al., 2011; Zscheischler et al., 2014). These thresholds are treated as conservative, configurable parameters rather than fixed global cutoffs, allowing sensitivity to be adjusted based on local variability, indicator behavior and monitoring objectives.\u003c/p\u003e \u003cp\u003eBy evaluating deviations independently at the grid level, the framework captures both spatial and temporal variability, enabling the identification of localized anomaly patterns that may evolve gradually or emerge abruptly over time (Verbesselt et al., 2010).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e8.4 Context-Aware Rule Logic\u003c/h2\u003e \u003cp\u003eTo improve robustness and reduce false interpretations, the framework incorporates a context-aware rule logic that evaluates deviations across multiple indicators jointly rather than in isolation. Deviations detected in individual indicators are interpreted in the context of complementary signals to account for interdependencies among vegetation vigor, water availability and environmental conditions, an approach widely advocated in multi-index agricultural monitoring (Atzberger, 2013; B\u0026eacute;gu\u0026eacute; et al., 2018).\u003c/p\u003e \u003cp\u003eFor example, deviations in water-related indicators are assessed alongside vegetation indices to avoid misclassifying healthy vegetation under transient moisture conditions as stressed. Conversely, simultaneous negative deviations across multiple complementary indicators are treated as stronger evidence of potential stress or underperformance, consistent with findings in multi-variable anomaly detection studies (Chandola et al., 2009).\u003c/p\u003e \u003cp\u003eThis contextual evaluation layer does not replace the deviation analysis but refines its interpretation by embedding domain-informed logical relationships among indicators. The result is a more stable and interpretable assessment of grid-level conditions under complex and variable agricultural environments (Lhermitte et al., 2011).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e8.5 Missing Data Handling and Quality Control\u003c/h2\u003e \u003cp\u003eSatellite-derived time series frequently contain gaps due to cloud cover, acquisition constraints, or data quality issues. To address this, the framework implements strict data quality control and conditional handling of missing observations, reflecting best practices in optical remote sensing analysis (Zhu \u0026amp; Woodcock, 2012; Roy et al., 2014).\u003c/p\u003e \u003cp\u003eCloud-contaminated observations are excluded based on predefined quality thresholds. When gaps occur within historical data, temporal interpolation is applied selectively and only when data sufficiency conditions are satisfied. These conditions include minimum levels of historical completeness and temporal continuity to ensure that interpolation does not distort baseline characteristics or introduce artificial trends, as cautioned in time-series vegetation studies (Forkel et al., 2013; Lhermitte et al., 2011).\u003c/p\u003e \u003cp\u003eInterpolation is restricted to historical data only. Current-year observations are not interpolated in order to preserve the integrity of real-time or near-real-time deviation analysis. If data sufficiency criteria are not met, deviation analysis is suspended for the affected grid cell and time step rather than forcing imputation. This conservative approach prioritizes analytical reliability over completeness, ensuring that detected deviations are driven by genuine signal behavior rather than preprocessing artifacts (Verbesselt et al., 2010).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e9. Visualization and Decision-Support Outputs\u003c/h3\u003e\n\u003cp\u003eEffective agricultural monitoring systems must not only generate reliable analytical outputs but also present results in forms that support interpretation and decision-making across diverse user contexts. The proposed framework translates grid-level spatiotemporal deviation analyses into interpretable visual representations and summary indicators designed to support exploratory analysis, situational awareness and operational planning. The importance of interpretable geospatial visualization for decision support in agriculture has been widely emphasized in prior work (Atzberger, 2013; Zhang \u0026amp; Kovacs, 2012).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e9.1 Grid-Level Spatial Visualization\u003c/h2\u003e \u003cp\u003eGrid-level outputs are visualized as spatial maps that represent standardized deviation metrics and aggregated performance indicators across the region of interest. Each grid cell is assigned a color-coded representation reflecting its deviation status relative to historical baselines. Such spatial visualization approaches are commonly used to support rapid identification of localized variability and anomaly patterns in agricultural and environmental monitoring (Mulla, 2013; B\u0026eacute;gu\u0026eacute; et al., 2018).\u003c/p\u003e \u003cp\u003eBy maintaining a consistent spatial grid across time, visualizations support temporal comparison of spatial patterns, allowing users to observe the emergence, persistence, or resolution of localized anomalies over the course of a season. Preserving spatial consistency across time is a recognized best practice for spatiotemporal visualization and interpretation (Verbesselt et al., 2010). Spatial visualizations are designed to preserve geospatial context while minimizing visual complexity, ensuring clarity even at fine spatial resolutions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e9.2 Temporal Trend Visualization\u003c/h2\u003e \u003cp\u003eIn addition to spatial representations, the framework provides temporal visualizations that depict historical and current trajectories of selected indicators at the grid or aggregated field level. Time-series plots illustrate the evolution of indicator values alongside historical baselines, enabling users to assess seasonal dynamics and identify deviations in a temporal context. Time-series visualization has been shown to play a critical role in interpreting vegetation dynamics and detecting abnormal behavior in remote sensing applications (Forkel et al., 2013; Lhermitte et al., 2011).\u003c/p\u003e \u003cp\u003eTemporal visualizations support interpretation by distinguishing short-term fluctuations from sustained trends and by highlighting periods where observations depart meaningfully from expected behavior. When combined with spatial maps, these plots enable complementary spatial\u0026ndash;temporal analysis, linking localized patterns to their temporal drivers, as advocated in spatiotemporal monitoring literature (Verbesselt et al., 2010).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e9.3 Aggregated Field-Level Indicators\u003c/h2\u003e \u003cp\u003eTo support high-level decision-making, grid-level deviation metrics are aggregated into summary indicators that characterize overall field or region performance. Aggregation is performed in a manner that preserves visibility into underlying spatial variability while providing concise metrics suitable for comparative analysis across time or between fields, consistent with approaches used in precision agriculture decision-support systems (Schimmelpfennig, 2016).\u003c/p\u003e \u003cp\u003eThese aggregated indicators serve as interpretive aids rather than standalone performance scores. They are intended to support monitoring of general trends, identification of periods requiring closer inspection and prioritization of areas for further analysis or intervention, rather than replacing localized analysis (Atzberger, 2013).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e9.4 Interpretability and Decision Support\u003c/h2\u003e \u003cp\u003eAll visual outputs are designed to emphasize interpretability and transparency rather than predictive certainty. By explicitly linking visual representations to standardized deviation metrics and localized historical baselines, the framework enables users to understand not only where anomalies occur, but also why they are identified. Interpretability has been repeatedly highlighted as a critical requirement for effective adoption of data-driven decision-support tools in agriculture (Mulla, 2013; Schimmelpfennig, 2016).\u003c/p\u003e \u003cp\u003eVisualization outputs are structured to support decision-support workflows by enabling users to explore grid-level conditions, examine temporal context and synthesize information across multiple indicators. Rather than prescribing specific actions, the framework provides structured insights that can inform domain-specific decision-making in precision agriculture, sustainability assessment and risk monitoring contexts, aligning with best practices in agricultural decision-support system design (Zhang \u0026amp; Kovacs, 2012).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e10. Demonstration Use Case\u003c/h3\u003e\n\u003cp\u003eTo illustrate the application of the proposed framework, a representative agricultural region was selected for demonstration purposes. The objective of this use case is not to quantitatively validate predictive performance, but to demonstrate how grid-level spatiotemporal deviation analysis can be applied in practice to reveal spatial and temporal variability within an agricultural landscape.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e10.1 Study Area and Data Configuration\u003c/h2\u003e \u003cp\u003eThe demonstration region consists of an agricultural plot characterized by heterogeneous vegetation patterns and seasonal variability. The region of interest was defined using a spatial bounding box and partitioned into uniform grid cells aligned with the spatial resolution of the satellite imagery. Multi-year historical satellite data were retrieved for the region to construct grid-specific temporal baselines, while current-season observations were analyzed to identify deviations from historical norms.\u003c/p\u003e \u003cp\u003eSatellite-derived vegetation and environmental indicators were computed for each grid cell following the preprocessing and quality-control procedures described in Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Only observations meeting cloud-cover and data completeness criteria were included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e10.2 Grid-Level Spatiotemporal Patterns\u003c/h2\u003e \u003cp\u003eApplication of the framework produced grid-level spatial representations of deviation behavior relative to historical baselines. Spatial visualizations illustrate how deviation signals, when present, may vary across individual grid cells within the region of interest, enabling localized inspection without assuming a predefined spatial structure. Depending on prevailing field conditions, grid-level deviations may appear sparse, widespread, or absent during a given observation period.\u003c/p\u003e \u003cp\u003eTemporal visualizations further contextualize grid-level behavior by showing how deviations evolve over the course of the season. In some cases, deviations emerge gradually across successive observation windows, while in others they appear as short-lived departures that subsequently return toward baseline conditions. This temporal perspective supports differentiation between transient variability and sustained departures, without imposing assumptions regarding the spatial extent or persistence of deviations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e10.3 Contextual Interpretation Across Indicators\u003c/h2\u003e \u003cp\u003eThe demonstration also illustrates the role of context-aware interpretation in refining deviation analysis. In selected grid cells, deviations detected in water-related indicators were evaluated alongside vegetation indices to assess whether observed changes were consistent with stress conditions or reflected normal adaptive behavior. In cases where vegetation indicators remained within typical ranges despite transient deviations in water-related signals, the framework suppressed isolated stress interpretations.\u003c/p\u003e \u003cp\u003eConversely, grid cells exhibiting concurrent deviations across multiple complementary indicators were identified as areas of potential concern. This multi-indicator interpretation reduced the likelihood of false positives and provided a more nuanced understanding of spatial variability under real-world conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e10.4 Interpretive Outputs for Decision Support\u003c/h2\u003e \u003cp\u003eThe resulting spatial maps, temporal plots and aggregated indicators collectively demonstrate how the framework supports exploratory analysis and decision-support workflows. Grid-level outputs enable users to identify where deviations occur, while temporal context clarifies when deviations emerge and how they evolve. Aggregated indicators provide a high-level overview while preserving the ability to drill down into localized patterns.\u003c/p\u003e \u003cp\u003eThis demonstration highlights the framework\u0026rsquo;s capacity to translate complex spatiotemporal data into interpretable insights without relying on absolute thresholds or field-averaged metrics. The example use case illustrates how localized historical normalization and contextual interpretation can enhance understanding of agricultural landscape dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe proposed grid-based spatiotemporal deviation framework addresses several limitations commonly observed in satellite-driven agricultural monitoring systems. By emphasizing localized historical normalization rather than absolute index values or static thresholds, the framework provides a more context-aware interpretation of agricultural dynamics at the field scale. This approach is particularly relevant in heterogeneous agricultural landscapes, where spatial variability, management practices and microclimatic effects can significantly influence observed satellite signals (Lobell et al., 2009; Atzberger, 2013).\u003c/p\u003e \u003cp\u003eOne of the key strengths of the framework lies in its grid-level temporal baselining strategy. Constructing historical baselines independently for each spatial unit enables the system to distinguish between normal seasonal behavior and meaningful deviations specific to local conditions. This localized perspective reduces the risk of misclassification that can arise when regional averages or generalized thresholds are applied across diverse fields or cropping systems, a limitation frequently noted in agricultural remote sensing studies (Verbesselt et al., 2010; B\u0026eacute;gu\u0026eacute; et al., 2018). In addition, the use of standardized deviation metrics allows consistent interpretation across indicators with different ranges and variances, following established anomaly detection principles (Chandola et al., 2009).\u003c/p\u003e \u003cp\u003eThe integration of context-aware rule logic further enhances robustness by accounting for interdependencies among vegetation, water and environmental indicators. Agricultural systems often exhibit compensatory responses, where short-term deviations in one indicator may not correspond to adverse conditions if supported by complementary signals. Multi-indicator interpretation has been shown to reduce ambiguity and false positives compared to single-index approaches, particularly under variable environmental conditions (Atzberger, 2013; Lhermitte et al., 2011). By evaluating deviations jointly rather than in isolation, the framework supports more nuanced interpretation under complex field conditions.\u003c/p\u003e \u003cp\u003eInterpretation of deviation magnitude and its impact on scoring and flagging is treated as a conservative and context-aware design choice rather than relying on fixed global cutoffs. Thresholds governing the influence of positive and negative deviations are applied to prioritize robustness and false-positive avoidance and are not assumed to be universally optimal across crops, regions, or monitoring objectives. This design reflects the inherent heterogeneity of agricultural systems and supports flexible sensitivity adjustment under differing environmental and operational contexts.\u003c/p\u003e \u003cp\u003eThe framework also explicitly prioritizes data quality and analytical reliability. Conservative cloud-cover filtering, controlled temporal backdating and strict conditions for interpolation are essential for maintaining the integrity of satellite-derived time series, as emphasized in prior work on optical satellite preprocessing (Zhu \u0026amp; Woodcock, 2012; Roy et al., 2014). Rather than forcing complete coverage through aggressive imputation, the framework opts to suspend analysis when data sufficiency criteria are not met. While this may reduce temporal completeness in certain scenarios, it enhances confidence in detected deviations and avoids introducing artifacts that could mislead interpretation, a trade-off widely discussed in time-series remote sensing literature (Forkel et al., 2013).\u003c/p\u003e \u003cp\u003eDespite these strengths, several limitations should be acknowledged. The framework currently relies on optical satellite imagery, which remains sensitive to persistent cloud cover and atmospheric conditions in certain regions. Although quality-control measures mitigate this issue to some extent, extended data gaps can limit temporal continuity, particularly in tropical and monsoon-dominated environments (Roy et al., 2014). Additionally, the framework is designed as a general-purpose monitoring system and does not incorporate crop-specific calibration or phenological models, which may be required for detailed yield estimation or crop-type\u0026ndash;specific interpretation (Mulla, 2013; Bolton \u0026amp; Friedl, 2013).\u003c/p\u003e \u003cp\u003eAnother limitation relates to the absence of large-scale quantitative validation in the present study. The demonstration use case is intended to illustrate methodological behavior rather than assess predictive performance. At the current stage, validation is primarily qualitative and operational in nature, informed through iterative feedback from pilot deployments and field users who assess detected deviations against observed field conditions. In this setting, inconsistencies between deviation signals and on-ground observations are surfaced through user feedback, enabling refinement of data quality controls, rule logic and interpretation layers. While such feedback does not substitute for formal quantitative validation against ground-based measurements, it provides an important mechanism for identifying systematic misinterpretations and operational edge cases during early-stage deployment.\u003c/p\u003e \u003cp\u003eDeviation-based approaches, particularly those grounded directly in established satellite-derived indices, are inherently constrained by the physical meaning and empirical robustness of the underlying indicators. As a result, the likelihood of structurally implausible signals is reduced relative to black-box predictive models, though it is not eliminated. Comprehensive quantitative validation against field measurements, agronomic records and operational outcomes remains an important area for future work, as emphasized in precision agriculture research (Schimmelpfennig, 2016; B\u0026eacute;gu\u0026eacute; et al., 2018).\u003c/p\u003e \u003cp\u003eOverall, the discussion highlights a deliberate design trade-off between generality, interpretability and validation depth. By focusing on methodological transparency, localized normalization, and robustness to real-world data constraints, the proposed framework provides a solid foundation for operational decision-support applications. As additional ground-truth data and structured pilot deployments become available, the framework can be further refined and extended to support validation-driven analyses and domain-specific optimization, consistent with current directions in applied agricultural intelligence systems (Atzberger, 2013; Mulla, 2013).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper presented a grid-based spatiotemporal deviation framework for agricultural landscape monitoring that emphasizes localized historical normalization and context-aware interpretation of satellite-derived indicators. By moving beyond absolute index values and static thresholds, the proposed approach enables more nuanced and spatially explicit assessment of agricultural dynamics at the field scale.\u003c/p\u003e \u003cp\u003eThe framework integrates fine-resolution spatial representation, multi-year temporal baselining, standardized deviation analysis and conservative data quality controls into a modular GeoAI-based system. Through grid-level analysis and contextual evaluation across multiple indicators, the approach supports robust interpretation of spatiotemporal variability while remaining resilient to common limitations of optical satellite data.\u003c/p\u003e \u003cp\u003eRather than focusing on predictive accuracy or crop-specific calibration, this work emphasizes methodological design and system architecture as a foundation for decision-support applications in precision agriculture, sustainability assessment and agricultural risk monitoring. A representative demonstration illustrated how localized deviation analysis can reveal patterns that may be obscured in field-averaged or threshold-based monitoring systems.\u003c/p\u003e \u003cp\u003eThe proposed framework provides a transparent and extensible basis for operational agricultural monitoring under real-world data constraints. As additional ground-based observations and pilot deployments become available, the approach can be further evaluated and adapted to support validation-driven studies and domain-specific extensions.\u003c/p\u003e\n\u003ch3\u003eFuture Work\u003c/h3\u003e\n\u003cp\u003eSeveral directions for future research emerge from the proposed framework. A primary focus of future work will be quantitative validation using ground-based observations and operational farm data across diverse agro-ecological regions. Such validation will enable systematic assessment of deviation-based indicators in relation to observed crop performance, stress events and management outcomes, addressing a widely acknowledged gap between satellite-based monitoring and field-level validation in precision agriculture research (Mulla, 2013; B\u0026eacute;gu\u0026eacute; et al., 2018).\u003c/p\u003e \u003cp\u003eFuture extensions of the framework will also explore crop-specific and phenology-aware adaptations. Incorporating crop calendars, growth-stage segmentation and phenological markers may improve temporal baseline construction and contextual interpretation, particularly for multi-cropping systems and regions with irregular planting schedules. Phenology-aware analysis has been shown to enhance the interpretability of vegetation time series and reduce ambiguity in seasonal comparisons (de Beurs \u0026amp; Henebry, 2010; Bolton \u0026amp; Friedl, 2013).\u003c/p\u003e \u003cp\u003eIntegration of additional data sources represents another important avenue for development. The inclusion of weather reanalysis products, soil property datasets and management records has the potential to enhance deviation interpretation and support more comprehensive agricultural intelligence. Previous studies have highlighted the value of combining satellite-derived indicators with climatic and management data to better attribute observed variability to underlying drivers (Lobell et al., 2009; Atzberger, 2013).\u003c/p\u003e \u003cp\u003eMethodological extensions may also investigate adaptive deviation thresholds and learning-based rule refinement. While the current framework employs standardized deviation metrics and rule-based contextual logic to prioritize interpretability, future research could explore hybrid approaches that combine these principles with data-driven calibration as larger datasets become available. Such hybrid strategies have been suggested as a means to balance robustness, transparency and adaptability in agricultural monitoring systems (Chandola et al., 2009).\u003c/p\u003e \u003cp\u003eFinally, future work will focus on scaling the framework for broader operational deployment and longitudinal studies. This includes evaluating computational efficiency, cross-region transferability and long-term stability of grid-level baselines under changing climatic conditions. Long-term consistency and scalability are increasingly recognized as critical requirements for operational agricultural monitoring under climate variability and change (Verbesselt et al., 2010; Atzberger, 2013). Together, these efforts aim to extend the framework from a foundational monitoring system toward a validated and adaptable platform for decision-support in sustainable agriculture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe author is affiliated with Sensegrass, which develops applied geospatial intelligence systems. The work presented in this paper focuses on methodological design and does not evaluate or benchmark any commercial implementation.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.D.M. conceived the study, designed the analytical framework, developed and implemented the methodology, conducted the analysis, interpreted the results and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe analyses in this study are based on publicly available satellite imagery and derived indices accessed through established remote sensing platforms. Data availability is subject to the terms and access conditions of the respective data providers. No proprietary or field-collected datasets were used.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eThe author gratefully acknowledges Sensegrass for providing the institutional support, technical resources and research environment necessary to conduct this research and development work. The study benefited from the opportunity to explore, design and iterate on the proposed framework within a real-world applied context.\u003c/p\u003e \u003cp\u003eThe author would like to thank Lalit Gautam, Hem Raj Pandey and Saurabh Dixit for their constructive discussions, technical input and support throughout the development of the system. Their contributions helped inform the practical and methodological aspects of the work.\u003c/p\u003e \u003cp\u003eThe author also acknowledges the continued support and encouragement of friends and family, which contributed to the successful completion of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u003cb\u003eAtzberger, C.\u003c/b\u003e (2013). \u003cem\u003eAdvances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs\u003c/em\u003e. \u003cem\u003eRemote Sensing\u003c/em\u003e, 5(2), 949\u0026ndash;981. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAdvances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eBaret, F. \u0026amp; Guyot, G.\u003c/b\u003e (1991). \u003cem\u003ePotentials and limits of vegetation indices for LAI and APAR assessment\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 35(2\u0026ndash;3), 161\u0026ndash;173. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePotentials and limits of vegetation indices for LAI and APAR assessment - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eB\u0026eacute;gu\u0026eacute;, A., Arvor, D., Bell\u0026oacute;n, B., Betbeder, J., de Abelleyra, D., Ferraz, R.P.D., Lebourgeois, V., Lelong, C., Sim\u0026otilde;es, M. \u0026amp; Ver\u0026oacute;n, S.R.\u003c/b\u003e (2018). \u003cem\u003eRemote sensing and cropping practices: A review\u003c/em\u003e. \u003cem\u003eRemote Sensing\u003c/em\u003e, 10(1), 99. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRemote Sensing and Cropping Practices: A Review\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eBolton, D.K. \u0026amp; Friedl, M.A.\u003c/b\u003e (2013). \u003cem\u003eForecasting crop yield using remotely sensed vegetation indices and crop phenology metrics\u003c/em\u003e. \u003cem\u003eAgricultural and Forest Meteorology\u003c/em\u003e, 173, 74\u0026ndash;84. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eForecasting crop yield using remotely sensed vegetation indices and crop phenology metrics - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eChandola, V., Banerjee, A. \u0026amp; Kumar, V.\u003c/b\u003e (2009). \u003cem\u003eAnomaly detection: A survey\u003c/em\u003e. \u003cem\u003eACM Computing Surveys\u003c/em\u003e, 41(3), 1\u0026ndash;58. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(PDF) Anomaly Detection: A Survey\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003ede Beurs, K.M. \u0026amp; Henebry, G.M.\u003c/b\u003e (2010). \u003cem\u003eSpatio-temporal statistical methods for modelling land surface phenology\u003c/em\u003e. \u003cem\u003eInternational Journal of Geographical Information Science\u003c/em\u003e, 24(2), 199\u0026ndash;218. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSpatio-Temporal Statistical Methods for Modelling Land Surface Phenology | SpringerLink\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eDrusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F. \u0026amp; Bargellini, P.\u003c/b\u003e (2012). \u003cem\u003eSentinel-2: ESA\u0026rsquo;s optical high-resolution mission for GMES operational services\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 120, 25\u0026ndash;36. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eForkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M.D., Neigh, C.S.R. \u0026amp; Reichstein, M.\u003c/b\u003e (2013). \u003cem\u003eTrend change detection in NDVI time series: Effects of inter-annual variability and methodology\u003c/em\u003e. \u003cem\u003eRemote Sensing\u003c/em\u003e, 5(5), 2113\u0026ndash;2144. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTrend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eGorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. \u0026amp; Moore, R.\u003c/b\u003e (2017). \u003cem\u003eGoogle Earth Engine: Planetary-scale geospatial analysis for everyone\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 202, 18\u0026ndash;27. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGoogle Earth Engine: Planetary-scale geospatial analysis for everyone - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eHuete, A.R., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. \u0026amp; Ferreira, L.G.\u003c/b\u003e (2002). \u003cem\u003eOverview of the radiometric and biophysical performance of the MODIS vegetation indices\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 83(1\u0026ndash;2), 195\u0026ndash;213. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOverview of the radiometric and biophysical performance of the MODIS vegetation indices - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eLhermitte, S., Verbesselt, J., Verstraeten, W.W. \u0026amp; Coppin, P.\u003c/b\u003e (2011). \u003cem\u003eA comparison of time series similarity measures for classification and change detection of ecosystem dynamics\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 115(12), 3129\u0026ndash;3152. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA comparison of time series similarity measures for classification and change detection of ecosystem dynamics - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eLobell, D.B., Cassman, K.G \u0026amp; Field, C.B.\u003c/b\u003e (2009). \u003cem\u003eCrop yield gaps: Their importance, magnitudes, and causes\u003c/em\u003e. \u003cem\u003eAnnual Review of Environment and Resources\u003c/em\u003e, 34, 179\u0026ndash;204. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCrop Yield Gaps: Their Importance, Magnitudes, and Causes | Annual Reviews\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eMulla, D.J.\u003c/b\u003e (2013). \u003cem\u003eTwenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps\u003c/em\u003e. \u003cem\u003eBiosystems Engineering\u003c/em\u003e, 114(4), 358\u0026ndash;371. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTwenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003ePeters, A.J., Walter-Shea, E.A., Ji, L., Vi\u0026ntilde;a, A., Hayes, M. \u0026amp; Svoboda, M.D.\u003c/b\u003e (2015). \u003cem\u003eDrought monitoring with NDVI-based standardized vegetation index\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 86(3), 392\u0026ndash;402. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(PDF) Drought monitoring with NDVI-based Standardized Vegetation Index\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eRouse, J.W., Haas, R.H., Schell, J.A. \u0026amp; Deering, D.W.\u003c/b\u003e (1974). \u003cem\u003eMonitoring vegetation systems in the Great Plains with ERTS\u003c/em\u003e. In: \u003cem\u003eProceedings of the Third Earth Resources Technology Satellite-1 Symposium\u003c/em\u003e, NASA SP-351, 309\u0026ndash;317. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMonitoring vegetation systems in the Great Plains with ERTS - NASA Technical Reports Server (NTRS)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eRoy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., et al.\u003c/b\u003e (2014). \u003cem\u003eLandsat-8: Science and product vision for terrestrial global change research\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 145, 154\u0026ndash;172. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLandsat-8: Science and product vision for terrestrial global change research - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eSchimmelpfennig, D.\u003c/b\u003e (2016). \u003cem\u003eFarm profits and adoption of precision agriculture\u003c/em\u003e. \u003cem\u003eEconomic Research Report No. 217\u003c/em\u003e. United States Department of Agriculture. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(PDF) Farm Profits and Adoption of Precision Agriculture\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eTucker, C.J.\u003c/b\u003e (1979). \u003cem\u003eRed and photographic infrared linear combinations for monitoring vegetation\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 8(2), 127\u0026ndash;150. DOI: https://doi.org/10.1016/0034-4257(79)90013-0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eVerbesselt, J., Hyndman, R., Newnham, G. \u0026amp; Culvenor, D.\u003c/b\u003e (2010). \u003cem\u003eDetecting trend and seasonal changes in satellite image time series\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 114(1), 106\u0026ndash;115. DOI: 10.1016/j.rse.2009.08.014 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eresearch.wur.nl\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eZhang, C. \u0026amp; Kovacs, J.M.\u003c/b\u003e (2012). \u003cem\u003eThe application of small unmanned aerial systems for precision agriculture: A review\u003c/em\u003e. \u003cem\u003ePrecision Agriculture\u003c/em\u003e, 13(6), 693\u0026ndash;712. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe application of small unmanned aerial systems for precision agriculture: a review\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eZhu, Z. \u0026amp; Woodcock, C.E.\u003c/b\u003e (2012). \u003cem\u003eObject-based cloud and cloud shadow detection in Landsat imagery\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 118, 83\u0026ndash;94. DOI: Object-based \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ecloud and cloud shadow detection in Landsat imagery - ScienceDirect\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eZscheischler, J., Mahecha, M.D., Harmeling, S. \u0026amp; Reichstein, M.\u003c/b\u003e (2014). \u003cem\u003eDetection and attribution of large spatiotemporal extreme events in Earth observation data\u003c/em\u003e. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 150, 148\u0026ndash;160. DOI: 10.1016/j.ecoinf.2013.03.004\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Precision agriculture, Remote sensing, Spatiotemporal analysis, Anomaly detection, GeoAI, Decision support systems, Satellite imagery","lastPublishedDoi":"10.21203/rs.3.rs-8430837/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8430837/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgricultural monitoring systems increasingly rely on satellite-derived indices to assess crop and field conditions; however, many existing approaches depend on absolute index values or static thresholds, limiting their ability to capture localized and temporal variability. This paper presents a grid-based spatiotemporal deviation framework for agricultural landscape monitoring that emphasizes relative performance assessment against historical baselines rather than absolute measurements.\u003c/p\u003e\n\u003cp\u003eThe proposed framework partitions agricultural regions into fine-resolution spatial grids and constructs multi-year temporal baselines for each grid using satellite-derived vegetation and environmental indicators. Current observations are evaluated using standardized deviation metrics to identify significant departures from historical norms while accounting for contextual interactions among multiple indices.\u003c/p\u003e\n\u003cp\u003eThe system incorporates strict data quality controls, including cloud-cover filtering and conditional temporal interpolation, to ensure analytical robustness under real-world data constraints. Outputs are designed to support interpretability through grid-level anomaly maps, temporal trend visualizations and aggregated field indicators.\u003c/p\u003e\n\u003cp\u003eA representative demonstration is presented to illustrate how the framework enables early detection of spatial and temporal variability in agricultural conditions. The proposed approach offers a modular and extensible foundation for decision-support applications in precision agriculture, sustainability assessment and risk monitoring.\u003c/p\u003e","manuscriptTitle":"A Grid-Based Spatiotemporal Deviation Framework for Agricultural Landscape Monitoring Using Remote Sensing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 13:18:31","doi":"10.21203/rs.3.rs-8430837/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3986d3f0-d0f6-40dc-aa8c-bba5bbcf8b54","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T15:41:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 13:18:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8430837","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8430837","identity":"rs-8430837","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}