30-m Resolution Research Articles

Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data

The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. A total of 432,230 Landsat scenes were processed, enhancing data quality and accuracy. The dataset was validated using 255,000 samples across 6 geographical regions, showing strong performance in capturing spatiotemporal NDVI variations. Additionally, the dataset effectively addresses Scan Line Corrector-off stripes in Landsat 7 imagery. This dataset enables reliable annual NDVI estimates for China at a 30-m resolution and is available for reuse through an open data repository.

Exploring the Driving Factors of Land Use Change and Spatial Distribution in Coastal Cities: A Case Study of Xiamen City

This study focuses on the coastal city of Xiamen, examining the factors and driving mechanisms influencing land use changes and spatial patterns. Spatial logistic regression and Statistical Package for the Social Sciences (SPSS) software were employed using grid data with a resolution of 100m to analyze the spatial relationships between six driving factors (such as elevation and slope) and five land use types within the study area. Regression models were established for each factor, and all Relative Operating Characteristic (ROC) tests were passed. Based on the results of the logistic regression analysis, land use changes and spatial distribution were simulated using the updated Conversion of Land Use and its Effects (CLUE) model so as to validate the driving mechanisms. The findings indicate that the six driving factors effectively explain the spatial patterns of land use in the study area. The distance to the coastline is the primary influencing factor in the evolution of spatial patterns, particularly impacting built-up land and farmland, while for forest land, slope is the main factor affecting the spatial distribution. The simulation and accuracy analysis revealed an overall simulation accuracy ranging from 73% to 90.1%, demonstrating that the selected driving factors have effective explanatory power for the spatial distribution of land use. Thus, this study’s results provide valuable insights into the complexity of land use changes and serve as a reference for relevant departments in land use management and planning.

GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020

The coastline reflects coastal environmental processes and dynamic changes, serving as a fundamental parameter for coast. Although several global coastline datasets have been developed, they mainly focus on coastal morphology, the typology of coastlines are still lacking. We produced a Global CoastLine Dataset (GCL_FCS30) with a detailed classification system. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index and an adaptive threshold segmentation method. The coastline classification was performed a hybrid transect classifier that integrates a random forest algorithm with stable training samples derived from multi-source geophysical data. The GCL_FCS30 offers significant advantages in capturing artificial coastlines, reflecting strong alignment with location validation data. The GCL_FCS30 classification was found to achieve an overall accuracy and Kappa coefficient over 85% and 0.75. Each coastline category accurately covered the majority of the area represented in third-party data and exhibited a high degree of spatial relevance. Therefore, the GCL_FCS30 is the first global coastline category dataset covering the high latitudes in a continuous and smooth line vector format.

Monitoring changes of forest height in California

Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities. Mapping and monitoring the structure of these forests at high spatial resolution provides the necessary data to better manage forest health, mitigate wildfire risks, and improve carbon sequestration. Here, we use LiDAR measurements of top of canopy height metric (RH98) from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission to map vegetation height across the entire California for two different time periods (2019–2020 and 2021–2022) and explore the impact of disturbance. Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. Model validation against independent airborne LiDAR data showed a R2≥0.65 for the top of canopy height outperforming existing GEDI-based height maps and with improved sensitivity for mapping tall trees (RH98 ≥ 50 m) across California. Height showed distinct spatial variations across forest types offering quantitative and spatial information to evaluate forest conditions. The model, trained on data from 2019 to 2020, showed a similar accuracy when applied to satellite imagery acquired in 2021–2022 allowing a robust detection of changes caused by natural and man-made disturbances of forest. Changes of height captured impacts of tree mortality and fire intensity, pointing to the influence of wildfire across landscapes. Fires caused more than 60% of the large forest disturbances between the two time periods. This study demonstrates the benefits of using locally trained ML models to rapidly modernize forest management techniques in the age of increasing climate risks.

An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series

The leaf area index (LAI) serves as a key metric for tracking crop growth and can be integrated into crop models for yield estimation. Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable errors due to the pixel-level heterogeneity. To improve the applicability and inversion accuracy of MODIS LAI products in the Northeast China (NEC) region, this study upscaled the 500-m resolution MODIS LAI product to a 5-km resolution by initially calculating the mean value. Then, the scale factors were estimated based on the observed LAI data of spring maize. To further refine the accuracy of the remotely sensed LAI, 1-km resolution land use data were resampled to 500-m resolution, and the pixel purity of spring maize was calculated for each 5-km grid cell. The scale factor time series was fitted with and without consideration of pixel purity, and the accuracy of the adjusted LAI using these two methods was compared. Our findings demonstrate that the optimal method for fitting scale factors for spring maize LAI data is piecewise function method which combines Gaussian and quadratic polynomial functions. The time series of scale factors derived from high- and low-purity pixels, differentiated by a 50% purity threshold, resulted in improved performance in adjusting the spring maize LAI compared to traditional remote sensing LAI data. The adjusted LAI performed better in reflecting the growth characteristics of spring maize in the NEC region, with the relative mean square errors between observed and adjusted LAI of spring maize during 2016 and 2020 below 1 m2/m2. This study provides crucial support for monitoring the growth process and estimating the yield of spring maize in the NEC region and also offers valuable scientific references for the optimization and application of remote sensing data.

Utilizing Remote Sensing to Quantify Evapotranspiration: A Case Study of the Saiss Basin (Morocco)

In areas highly affected by drought in a changing climate, evapotranspiration (ET) becomes a significant parameter affecting soils and vegetation. ET estimation on a large scale remains a problem in understanding the hydrologic system; it is complicated over heterogeneous land use and land cover. However, the availability of remote sensing imagery and local weather data has made ET mapping possible through the application of models. In this study, the METRIC model was applied using Landsat satellite imagery and climatic data. This involves resolving the surface energy balance equation by calculating net radiation, soil heat flux, and sensible heat flux. Two satellite images were used at a resolution of 15m: one for January as a wet season and another for July as a dry period. ET ranges between 2 and 12 mm/day. Higher values are estimated during the dry period, and lower values are attributed to the wet period. Furthermore, water bodies and vegetation are characterized by significant ET compared to bare soils and urbanized areas. The comparison of METRIC ET and reference ET showed a strong correlation with an R², RMSE, and MAE of 0.68, 1.0, and 0.78, respectively. This study can assist managers in their water and agricultural adaptation strategies against climate change.

Integrated analysis of marked and count data to characterize fine-scale stream fish movement.

Immigration and emigration are key demographic processes of animal population dynamics. However, we have limited knowledge on how fine-scale movement varies over space and time. We developed a Bayesian integrated population model using individual mark-recapture and count data to characterize fine-scale movement of stream fish at 20-m resolution in a 740-m study areaevery two months for 28months. Our study targeted small-bodied fish, for which imperfect capture was accounted for (bluehead chub Nocomis leptocephalus, creek chub Semotilus atromaculatus and mottled sculpin Cottus bairdii). Based on data from 2021 individuals across all species, we found that proportions of immigrants in 20-m sectionsaveraged 30-42% among the study species, but they varied over space and time. Creek chub immigrants increased during warmer intervals when individuals grew more and transitioned between body size classes, suggesting that immigration was due to ontogenetic habitat shifts. There was a weak pattern across the species that individuals were more likely to leave 20-m sections when flow was higher. Water-column species (bluehead chub and creek chub) were more likely to immigrate into and stay in deeper sections with more pool area. Across all species and occasions, number of immigrants to stream sections did not decrease with number of individuals that survived and stayed in the same sections. Thus, the habitat did not appear saturated, and our data provided no evidence that intra-specific interactions affected fine-scale movement at our fish densities. In conclusion, high turnover rates characterized fish movement among stream sections and their variation was associated with temporal and spatial shifts in abiotic conditions.

High-resolution spatiotemporal prediction of PM2.5 concentration based on mobile monitoring and deep learning.

Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional fixed monitoring methods. However, the sparsity of mobile monitoring data still makes it a challenge to recover the high-resolution pollutant concentration across an entire area. To tackle the sparsity issue and fulfill a prediction of the spatiotemporal distribution of PM2.5, a high-resolution urban PM2.5 prediction method was proposed based on mobile monitoring data in this study. This method enables prediction with a spatial resolution of 500m×500m and a temporal resolution of 1h. First, a Light Gradient Boosting Machine (LightGBM) was trained using mobile monitoring of PM2.5 concentration and exogenous features to obtain complete spatiotemporal PM2.5 concentration. Second, a model consisting of Convolutional Neural Network and Transformer (CNN-Transformer) with a customised loss function was established to predict high-resolution PM2.5 concentration based on complete spatiotemporal data. The method was validated using real-world data collected from Cangzhou, China. The numerical results from cross-validation showed an R2 of 0.925 for imputation and 0.887 for prediction, demonstrating this method is suitable for high-resolution spatiotemporal prediction of PM2.5 concentration based on mobile monitoring data.

Radar versus optical: The impact of cloud cover when mapping seasonal surface water for health applications in monsoon-affected India.

Surface water plays a vital role in the spread of infectious diseases. Information on the spatial and temporal dynamics of surface water availability is thus critical to understanding, monitoring and forecasting disease outbreaks. Before the launch of Sentinel-1 Synthetic Aperture Radar (SAR) missions, surface water availability has been captured at various spatial scales through approaches based on optical remote sensing data. A critical drawback of the latter is data loss due to cloud cover, however few studies have quantified this. This study evaluated data loss due to clouds in three Western Ghats (India) districts. These forest-agricultural mosaic landscapes, where water-related diseases are prevalent, experience the Indian monsoon. We compared surface water areas mapped by thresholding 10m Sentinel-1A SAR data with the optical 30m Landsat-derived Joint Research Centre (JRC) Global Surface Water product, currently the only globally available long-term monthly surface water data product. Backscatter thresholds were identified manually, and our Bayesian algorithm found these thresholds were very likely (>97%). While the Sentinel-1 SAR-based and JRC's optical-based approach mapped surface water extent with high overall accuracy (> 98%) when the cloud cover was low, the unmapped surface water area was substantial in the JRC product during the monsoon months. Across the districts, the average cloud cover in the July-August period was 92% or 90% for 2017 and 2018 respectively, resulting in 25% or 23% of the surface water area being unmapped. Also, the more detailed 10m resolution of Sentinel-1A SAR helped detect the many small water features missed by 30m JRC. Thus, for predicting water-related disease risks linked to small water features or monsoon rainfall, Sentinel-1A SAR is more effective. Finally, automatic backscatter thresholding for unvegetated surface water mapping can be effective if threshold values are adapted to regional-specific backscatter spatial and temporal variations.

The dataset of main grain land changes in China over 1985-2020.

Continuous, Accurate, and detailed information on main grain land (MGL) areas is crucial for provisioning food security and making policies affecting sustainable agricultural production. It still lacks a long-term MGL distribution dataset with fine spatial resolution. This study aimed to produce a long-term, high-resolution MGL distribution map for China. Here, we developed the change map of MGL with resolution 30 m in China for the period 1985-2020 using the Landsat image-based random forest algorithm on the GEE platform. Finally, the planting intensity, gain time and loss time of MGL was calculated. Results indicate that our mapping results are highly consistent with the annual planting area of various grain crops according to national statistics. A validation based on 3113 field survey samples with a 30-m resolution showed that the overall accuracy of MGL were 93.57%. The full MGL dataset is freely available at https://doi.org/10.6084/m9.figshare.26212643.v2 .

Hybrid model approach for hilly sub-watershed prioritization using morphometric parameters: a case study from Bakkhali river watershed in Cox’s Bazar, Bangladesh

ABSTRACT This study investigates the morphometry of the Bakkhali watershed (BW) in the hilly districts of Cox’s Bazar and Bandarban, Bangladesh, to analyze its hydrological characteristics. Morphometric parameters were utilized to prioritize sub-watersheds (SWs) through a hybrid approach combining principal component analysis (PCA) and the weighted-sum approach (WSA). Using ArcGIS Pro 2.7.0, streams, the watershed, and SWs were delineated from a 30-m resolution COP30 DEM. Preliminary priority ranks (PPR) were determined based on direct and inverse relationships of morphometric parameters to soil erodibility. Weighted compound factors (CF) were calculated from PCA results for final prioritization. The BW, a fifth-order watershed with a drainage area of 571.52 km², shows a consistent decrease in the number of streams with increasing stream order, indicating an erosional landform. The mean bifurcation ratio of 4.09 suggests a higher tendency for soil erosion. Shape factors indicate an elongated watershed with less pronounced peak flow characteristics. The analysis identified SW2 and SW3 as high-erosion zones, SW4 and SW9 as medium-erosion zones, and SW1 as a low-erosion zone. The study demonstrates the efficacy of combining geospatial and statistical tools for SW prioritization.

The mortality impacts of greening Italy

Green spaces have been shown to be beneficial to humans, but quantifying these benefits is a challenge for epidemiology. In this health impact assessment study, we exploit satellites to estimate for the whole of Italy the number of deaths that could be prevented in the 49 million adult population by greening residential areas. The exposure was assessed by calculating the normalized difference vegetation index at 10-m resolution within a 300-m distance from homes in 7904 municipalities. In this study we estimate, by achieving nationwide the level of residential greenness currently reached by the 25% of the population, a total of 28,433 (95% confidence interval: 21,400–42,350) preventable deaths and 279,324 (210,247–415,980) preventable years of life lost in Italy in 2022, representing the 5% of the total mortality burden. More green means fewer deaths, thus strong action is needed to increase the amount and accessibility of green spaces in all human settlements.

Habitat suitability modelling for restoration of intertidal seagrass, Zostera noltei: A case study from the Greater Thames Estuary, UK

Seagrasses are marine flowering plants that play crucial roles in blue carbon capture and coastal protection, as well as providing a critical feeding and nursery habitat for several species. However, the extent of seagrass meadows has drastically decreased around the UK coastline since the 1930s. As such, restoration of Zostera spp. seagrasses is a growing field for academics and practitioners, yet for Zostera noltei (dwarf eelgrass) in particular, restoration is hampered by knowledge of current distributions and where to restore. Habitat suitability modelling is a valuable tool for mapping and can be applied by practitioners at a site-specific scale to identify potential areas for restoration. Here we have created a Maximum Entropy (maxent) habitat suitability model to predict Z. noltei suitability in the Greater Thames Estuary based on seven environmental variables at a 10-m resolution within an intertidal boundary. Using areas of higher suitability to identify restoration potential, our results indicate 602.9 Ha of “good” suitable habitat for potential restoration around existing Z. noltei beds in the Thames, Medway and Swale Estuaries. Despite reasonable predictive accuracy, our model was limited by the availability, resolution and extent of important environmental variables such as sediment type. Nonetheless, we believe this is a valuable tool for practitioners in the initial stage of site selection at a local scale for Z. noltei restoration projects in the UK.

Annual winter wheat mapping dataset in China from 2001 to 2020

Winter wheat is a major staple food grown worldwide. China contributes about 19% of the winter wheat production in the world. Therefore, quickly and accurately acquiring winter wheat distribution in China over long time periods is critical to achieve grain safety and understand the spatiotemporal patterns of winter wheat. However, there is a lack of widely available, high spatial resolution, long-term, and large-scale remotely sensed maps on winter wheat cultivation in China. In this study, we utilized a phenology-based algorithm to identify winter wheat location. The algorithm integrated three key growing features of winter wheat into one variable to calculate the planting probability of winter wheat. Here, we produced a long-term winter wheat map dataset at 30-m resolution for China from 2001 to 2020 using the fusion dataset. The resultant maps achieved high accuracy based on field survey samples, with user’s, producer’s, and overall accuracies of 91.17%, 90.92%, and 91.6%, respectively. Furthermore, the mapped winter wheat area aggregated to municipal- and county-levels exhibited good correlations with agricultural statistical data.

Spatial heterogeneity response of soil salinization inversion cotton field expansion based on deep learning.

Soil salinization represents a significant challenge to the ecological environment in arid areas, and digital mapping of soil salinization as well as exploration of its spatial heterogeneity with crop growth have important implications for national food security and salinization management. However, the machine learning models currently used are deficient in mining local information on salinity and do not explore the spatial heterogeneity of salinity impacts on crops. This study developed soil salinization inversion models using CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory Network), and RF (Random Forest) models based on 97 field samples and feature variables extracted from Landsat-8 imagery. By evaluating the accuracy, the best-performing model was selected to map soil salinity at a 30m resolution for the years 2013 and 2022, and to explore the relationship between soil electrical conductivity (EC) values and the expansion of cotton fields as well as their spatial correlation. The results indicate that:(1) The CNN performs best in prediction, with an R2 of 0.84 for the training set and 0.73 for the test set, capable of capturing more local salinity information. (2) The expansion of cotton fields has reduced the level of soil salinization, with the area of severely salinized and saline soils in newly added cotton fields decreasing from 177.91 km2 and 381.46 km2 to 19.49 km2 and 1.12 km2, respectively. (3) Regions with long-term cotton cultivation and newly reclaimed cotton fields exhibit high sensitivity and vulnerability to soil salinity. This study explores the excellent performance of deep learning in salinity mapping and visualizes the spatial distribution of cotton fields that are highly sensitive to soil salinity, providing a scientific theoretical basis for accurate salinity management.

Geospatial Capacity Building for Flood Resilience in the Sahel: the SLAPIS project case study

Abstract. This study focuses on the development of a people-centred early warning system (EWS) against floods in the Sirba River basin between Niger and Burkina Faso. This densely populated area has witnessed an increase in extreme flooding events in recent years. Several flood forecasting systems in the Sahel exist, although there is no EWS that integrates the four components of people-centred EWSs, namely risk knowledge, monitoring and warning service, dissemination and communication, and response capacity. The proposed EWS, named SLAPIS, includes a risk knowledge component that involves defining four levels of vigilance. Its monitoring and alert component involves a user-friendly web application containing real-time data collected through automatic stations. The EWS communicate seamlessly with the national alert system. The response capacity is strengthened through the creation of a flood zone atlas. In this framework, the EWS integrates significant geoinformatics in preparation of local risk reduction plans and the awareness of local communities. In the SLAPIS case study, multi-temporal classifications were conducted using Sentinel-2 data and high-resolution images (approximately 10 cm) generated through Structure from Motion (SfM) techniques. Digital Terrain Model (DTM) creation for hydraulic model calibration employed a multiscale approach, incorporating GNSS survey data processed via Precise Point Positioning (PPP), HydroSHEDS (approximately 100m resolution), and commercial 10m resolution data. All information was calibrated, harmonised, and integrated into the EWS model, which is accessible via a web platform. Capacity building encompassed direct training and field implementation to streamline the primary EWS generation steps.

Modeling soil acidity (pH) dynamics under extreme agroclimatic conditions in Horro Guduru Wallaga Zone, northwestern Ethiopia.

Soil plays a critical role in nutrient availability, microbial activity, and fertility in agriculture. However, the effects of agroclimatic conditions on soil pH are not well understood, particularly in the Horro Guduru Zone of Ethiopia. This study aimed to investigate the soil pH under extremely wet and dry conditions across 3 shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Baseline agroclimatic data (1981-2010) and future projections (2041-2070) were obtained from the European Commission Climate Change Services. Soil pH data at a 250m resolution were extracted from the FAO-UNESCO global soil map. Missing values, multicollinearity, and outliers were addressed before modeling. Predictive models, including neural networks, generalized regression, and bootstrap forests, were validated, with the generalized regression model showing the best performance. The results indicate that soil pH decreases under consecutive dry‒wet conditions and increases with increasing maximum day temperatures across all scenarios. Soil pH is significantly influenced by the number of consecutive dry days, consecutive wet days, and maximum day temperature. The SSP1-2.6 and SSP2-4.5 scenarios resulted in improved pH levels, whereas SSP5-8.5 led to a decrease in soil pH, averaging 5.79 and decreasing to 5.54. These findings suggest that under SSP5-8.5, soil health and farming productivity may be compromised. This study emphasizes the need to adjust soil management practices based on prevailing climatic conditions to ensure soil health and agricultural sustainability.

DSM Quality Assessment of GF-7 Satellite

Abstract. The Gaofen-7 (GF-7) satellite was launched on November 3, 2019. It can achieve 1:10000 scale stereo mapping and serve the application needs of basic surveying and mapping, natural resources monitoring and geographic information resource construction. These application rely on high-precision digital surface model (DSM) products as data support. Therefore, it is of great significance to analyze the quality of DSM products produced by Gaofen-7. This paper uses GF-7 optical stereo images from the regions of Zhaodong and Tianjin in China to extract DSMs. And the difference DSM is produced by subtracting the 0.5m resolution aerial reference DEM and the 30m resolution COP30 product in the same area. The quality of DSM products is evaluated by comparing and analyzing the difference DSM. The results show that when the DSM produced by the GF-7 satellite uses the aerial DEM as a reference, the elevation accuracy can reach 0.97 m in the Zhaodong area and 1.54 m in the Tianjin area, and the details of the objects are well depicted; when the COP30 product is used as a reference, the elevation positioning accuracy can reach 1.29 m in the Zhaodong area and 2.42 m in the Tianjin area, which can correctly show the overall situation of the surface objects. Among them, the elevation accuracy in the Tianjin area is relatively poor, mainly because there are many water areas and buildings in this area, and the COP30 product has data missing in this area, which affects the accuracy. In general, the quality of the DSM products generated by the GF-7 satellite is good. Through effective ground control and further processing, the DSM products can fully meet the needs of practical applications such as 1:10000 scale mapping.

AI-based Validation of Deforestation Using High-Resolution Satellite Imagery in the Brazilian Amazon

Abstract. Forests play an important role in the Earth systems for carbon sequestration and climate change mitigation, yet they have been increasingly disturbed by deforestation and forest degradation at an unprecedented pace. The Brazilian Amazon, for instance, experienced a 140% rise in deforestation from 2012 to 2020, with a record loss of 13,200 km2 between August 2020 and July 2021. Alarmingly, 87% of 2019 deforestation alerts occurred on private properties, with 61% in legally restricted areas. Existing deforestation monitoring systems, such as PRODES and the Global Forest Change dataset, use about 30m resolution satellite imagery, which is insufficient for operational validation at fine scales. The Deforestation Alert System by Imazon leverages high-resolution PlanetScope data (3-4m) but faces challenges due to fewer spectral bands and variations in reflectance values across different satellite sensors and dates. As a result, current validation is based mainly on manual inspection which is highly time-consuming. To address the challenges and automate the validation process, this work develops a system based on deep learning – known for its ability to capture complex texture patterns in high-resolution images – to inspect and confirm new deforestation sites. Specifically, the system takes inputs from potential deforestation sites suggested by coarse-resolution products and uses a pair of PlanetScope images before and after the change at each site to determine new deforestation (excluding existing deforestation). Our results demonstrate that the new system achieves robust and high-quality accuracy under various test conditions.

An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19years, with an RMSE of 9days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.