Search Results (81)

Rapeseed mapping is crucial for refined agricultural management and food security. However, existing remote sensing-based methods for rapeseed mapping in Southwest China are severely limited by insufficient training samples and persistent cloud cover. To address the above challenges, this study presents an automatic rapeseed mapping framework that integrates multi-source remote sensing data fusion, automated sample generation, and deep learning models. The framework was applied in Santai County, Sichuan Province, Southwest China, which has typical topographical and climatic characteristics. First, MODIS and Landsat data were used to fill the gaps in Sentinel-2 imagery, creating time-series images through the object-level processing version of the spatial and temporal adaptive reflectance fusion model (OL-STARFM). In addition, a novel spectral phenology approach was developed to automatically generate training samples, which were then input into the improved TS-ConvNeXt ECAPA-TDNN (NeXt-TDNN) deep learning model for accurate rapeseed mapping. The results demonstrated that the OL-STARFM approach was effective in rapeseed mapping. The proposed automated sample generation method proved effective in producing reliable rapeseed samples, achieving a low Dynamic Time Warping (DTW) distance (<0.81) when compared to field samples. The NeXt-TDNN model showed an overall accuracy (OA) of 90.12% and a mean Intersection over Union (mIoU) of 81.96% in Santai County, outperforming other models such as random forest, XGBoost, and UNet-LSTM. These results highlight the effectiveness of the proposed automatic rapeseed mapping framework in accurately identifying rapeseed. This framework offers a valuable reference for monitoring other crops in similar environments. Full article

The current research on rocky desertification primarily prioritizes large-scale surveillance, with minimal attention given to internal agricultural areas. This study offers a comprehensive framework for bedrock extraction in agricultural areas, employing spatial constraints and spatio-temporal fusion methodologies. Utilizing the high resolution and capabilities of Gaofen-2 imagery, we first delineate agricultural land, use these boundaries as spatial constraints to compute the agricultural land bedrock response Index (ABRI), and apply the spatial and temporal adaptive reflectance fusion model (STARFM) to achieve spatio-temporal fusion of Gaofen-2 imagery and Sentinel-2 imagery from multiple time periods, resulting in a high-spatio-temporal-resolution bedrock discrimination index (ABRI*) for analysis. This work demonstrates the pronounced rocky desertification phenomenon in the agricultural land in the study area. The ABRI* effectively captures this phenomenon, with the classification accuracy for the bedrock, based on the ABRI* derived from Gaofen-2 imagery, reaching 0.86. The bedrock exposure area in the farmland showed a decreasing trend from 2019 to 2021, a significant increase from 2021 to 2022, and a gradual decline from 2022 to 2024. Cultivation activities have a significant impact on rocky desertification within agricultural land. The ABRI significantly enhances the capabilities for the dynamic monitoring of rocky desertification in agricultural areas, providing data support for the management of specialized farmland. For vulnerable areas, timely adjustments to planting schemes and the prioritization of intervention measures such as soil conservation, vegetation restoration, and water resource management could help to improve the resilience and stability of agriculture, particularly in karst regions. Full article

The demand for freshwater is increasing with population growth and rapid socio-economic development. It is more and more important for refined irrigation water management to conduct research on crop evapotranspiration (ET) data with a high spatiotemporal resolution in agricultural regions. We propose the unmixing–weight ET image fusion model (UWET), which integrates the advantages of the unmixing method in spatial downscaling and the weight-based method in temporal prediction to produce daily ET maps with a high spatial resolution. The Landsat-ET and MODIS-ET datasets for the UWET fusion data are retrieved from Landsat and MODIS images based on the surface energy balance model. The UWET model considers the effects of crop phenology, precipitation, and land cover in the process of the ET image fusion. The precision evaluation is conducted on the UWET results, and the measured ET values are monitored by eddy covariance at the Luancheng station, with average MAE values of 0.57 mm/day. The image results of UWET show fine spatial details and capture the dynamic ET changes. The seasonal ET values of winter wheat from the ET map mainly range from 350 to 660 mm in 2019–2020 and from 300 to 620 mm in 2020–2021. The average seasonal ET in 2019–2020 is 499.89 mm, and in 2020–2021, it is 459.44 mm. The performance of UWET is compared with two other fusion models: the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Spatial and Temporal Reflectance Unmixing Model (STRUM). UWET performs better in the spatial details than the STARFM and is better in the temporal characteristics than the STRUM. The results indicate that UWET is suitable for generating ET products with a high spatial–temporal resolution in agricultural regions. Full article

Jilin Province is located in the northeast of China, and has fragile ecosystems, and a vulnerable environment. Large-scale, long time series, high-precision land-use/cover change (LU/CC) data are important for spatial planning and environmental protection in areas with high surface heterogeneity. In this paper, based on the high temporal and spatial fusion data of Landsat and MODIS and the Google Earth Engine (GEE), long time series LU/CC mapping and spatio-temporal analysis for the period 2000–2023 were realized using the random forest remote sensing image classification method, which integrates remote sensing indices. The prediction results using the OL-STARFM method were very close to the real images and better contained the spatial image information, allowing its application to the subsequent classification. The average overall accuracy and kappa coefficient of the random forest classification products obtained using the fused remote sensing index were 95.11% and 0.9394, respectively. During the study period, the area of cultivated land and unused land decreased as a whole. The area of grassland, forest, and water fluctuated, while building land increased to 13,442.27 km² in 2023. In terms of land transfer, cultivated land was the most important source of transfers, and the total area share decreased from 42.98% to 38.39%. Cultivated land was mainly transferred to grassland, forest land, and building land, with transfer areas of 7682.48 km², 8374.11 km², and 7244.52 km², respectively. Grassland was the largest source of land transfer into cultivated land, and the land transfer among other feature types was relatively small, at less than 3300 km². This study provides data support for the scientific management of land resources in Jilin Province, and the resulting LU/CC dataset is of great significance for regional sustainable development. Full article

Monitoring ecosystems at regional or continental scales is paramount for biodiversity conservation, climate change mitigation, and sustainable land management. Effective monitoring requires satellite imagery with both high spatial resolution and high temporal resolution. However, there is currently no single, freely available data source that fulfills these needs. A seamless fusion of data from the Sentinel-3 and Sentinel-2 optical sensors could meet these monitoring requirements as Sentinel-2 observes at the required spatial resolution (10 m) while Sentinel-3 observes at the required temporal resolution (daily). We introduce the Efficient Fusion Algorithm across Spatio-Temporal scales (EFAST), which interpolates Sentinel-2 data into smooth time series (both spatially and temporally). This interpolation is informed by Sentinel-3’s temporal profile such that the phenological changes occurring between two Sentinel-2 acquisitions at a 10 m resolution are assumed to mirror those observed at Sentinel-3’s resolution. The EFAST consists of a weighted sum of Sentinel-2 images (weighted by a distance-to-clouds score) coupled with a phenological correction derived from Sentinel-3. We validate the capacity of our method to reconstruct the phenological profile at a 10 m resolution over one rangeland area and one irrigated cropland area. The EFAST outperforms classical interpolation techniques over both rangeland (−72% in the mean absolute error, MAE) and agricultural areas (−43% MAE); it presents a performance comparable to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (+5% MAE in both test areas) while being 140 times faster. The computational efficiency of our approach and its temporal smoothing enable the creation of seamless and high-resolution phenology products on a regional to continental scale. Full article

Farmland abandonment monitoring is one of the key aspects of land use and land cover research, as well as being an important prerequisite for ecological environmental protection and food security. A Normalized Difference Vegetation Index (NDVI) time series analysis is a common method used for farmland abandonment data extraction; however, extracting this information using high-resolution data is still difficult due to the limitations caused by cloud influence and data of low temporal resolution. To address this problem, this study used STARFM for GF-6 and Landsat 8 data fusion to enhance the continuity of high-resolution and cloudless images. A dataset was constructed by combining the phenological cycle of crops in the study area and then extracting abandoned farmland data based on an NDVI time series analysis. The overall accuracy of the results based on the NDVI time series analysis using the STARFM-fused dataset was 93.42%, which was 15.5% higher than the accuracy of the results obtained using only GF-6 data and 28.52% higher than those obtained using only Landsat data. Improvements in accuracy were also achieved when using SVM for time series analysis based on the fused dataset, indicating that the method used in this study can effectively improve the accuracy of the results. Then, we analyzed the spatial distribution pattern of abandoned farmland by extracting the results and concluded that the abandonment rate increased with the increase in the road network density and decreased with the increase in the distance to residential areas. This study can provide decision-making guidance and scientific and technological support for the monitoring of farmland abandonment and can facilitate the analysis of abandonment mechanisms in the study area, which is conducive to the sustainable development of farmland. Full article

The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation models, enhancing our understanding of physical and biochemical processes in nature. However, the limitations in swath width and orbit altitude prevent a single sensor from providing LST data with both high spatial and high temporal resolution. To tackle this challenge, the unmixing-based spatiotemporal fusion model (STFM) offers a promising solution by integrating data from multiple sensors. In these models, the surface reflectance is decomposed from coarse pixels to fine pixels using the linear unmixing function combined with fractional coverage. However, when downsizing LST through STFM, the linear mixing hypothesis fails to adequately represent the nonlinear energy mixing process of LST. Additionally, the original weighting function is sensitive to noise, leading to unreliable predictions of the final LST due to small errors in the unmixing function. To overcome these issues, we selected the U-STFM as the baseline model and introduced an updated version called the nonlinear U-STFM. This new model incorporates two deep learning components: the Dynamic Net (DyNet) and the Chang Ratio Net (RatioNet). The utilization of these components enables easy training with a small dataset while maintaining a high generalization capability over time. The MODIS Terra daytime LST products were employed to downscale from 1000 m to 30 m, in comparison with the Landsat7 LST products. Our results demonstrate that the new model surpasses STARFM, ESTARFM, and the original U-STFM in terms of prediction accuracy and anti-noise capability. To further enhance other STFMs, these two deep-learning components can replace the linear unmixing and weighting functions with minor modifications. As a deep learning-based model, it can be pretrained and deployed for online prediction. Full article

Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r²) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm/10 d to 8.48 mm/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP. Full article

Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites with fine-resolution sensors and cloud contamination has resulted in poor spatial and temporal continuity. In this study, we propose to derive a spatially and temporally continuous FVC dataset by comparing multiple methods, including the data-fusion method (STARFM), curve-fitting reconstruction (S-G filtering), and deep learning prediction (Bi-LSTM). By combining Landsat and Sentinel-2 data, the integrated FVC was used to construct the initial input of fine-resolution FVC with gaps. The results showed that the FVC of gaps were estimated and time-series FVC was reconstructed. The Bi-LSTM method was the most effective and achieved the highest accuracy (R² = 0.857), followed by the data-fusion method (R² = 0.709) and curve-fitting method (R² = 0.705), and the optimal time step was 3. The inclusion of relevant variables in the Bi-LSTM model, including LAI, albedo, and FAPAR derived from coarse-resolution products, further reduced the RMSE from 5.022 to 2.797. By applying the optimized Bi-LSTM model to Hubei Province, a time series 30 m FVC dataset was generated, characterized by a spatial and temporal continuity. In terms of the major vegetation types in Hubei (e.g., evergreen and deciduous forests, grass, and cropland), the seasonal trends as well as the spatial details were captured by the reconstructed 30 m FVC. It was concluded that the proposed method was applicable to reconstruct the time-series FVC over a large spatial scale, and the produced fine-resolution dataset can support the data needed by many Earth system science studies. Full article

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R² = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R² = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R² = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future. Full article

The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the flexible spatiotemporal data fusion (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR. Full article

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km²) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R² = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R² = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R² = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R² = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. Full article

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km²), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R² of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R² of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R² of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R² = 0.88) and OSR (R² = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R² of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively. Full article

The unmixing-based spatiotemporal fusion model is one of the effective ways to solve limitations in temporal and spatial resolution tradeoffs in a single satellite sensor. By using fusion data from different satellite platforms, high resolution in both temporal and spatial domains can be produced. However, due to the ill-posed characteristic of the unmixing function, the model performance may vary due to the different model setups. The key factors affecting the model stability most and how to set up the unmixing strategy for data downscaling remain unknown. In this study, we use the multisource land surface temperature as the case and focus on the three major factors to analyze the stability of the unmixing-based fusion model: (1) the definition of the homogeneous change regions (HCRs), (2) the unmixing levels, and (3) the number of HCRs. The spatiotemporal data fusion model U-STFM was used as the baseline model. The results show: (1) The clustering-based algorithm is more suitable for detecting HCRs for unmixing. Compared with the multi-resolution segmentation algorithm and k-means algorithm, the ISODATA clustering algorithm can more accurately describe LST’s temporal and spatial changes on HCRs. (2) For the U-STFM model, applying the unmixing processing at the change ratio level can significantly reduce the additive and multiplicative noise of the prediction. (3) There is a tradeoff effect between the number of HCRs and the solvability of the linear unmixing function. The larger the number of HCRs (less than the available MODIS pixels), the more stable the model is. (4) For the fusion of the daily 30 m scale LST product, compared with STARFM and ESTARFM, the modified U-STFM (iso_USTFM) achieved higher prediction accuracy and a lower error ($R{ }^2$: 0.87 and RMSE:1.09 k). With the findings of this study, daily fine-scale LST products can be predicted based on the unmixing-based spatial–temporal model with lower uncertainty and stable prediction. Full article

Accurate mapping of tree species is critical for the sustainable development of the forestry industry. However, the lack of cloud-free optical images makes it challenging to map tree species accurately in cloudy mountainous regions. In order to improve tree species identification in this context, a classification method using spatiotemporal fusion and ensemble classifier is proposed. The applicability of three spatiotemporal fusion methods, i.e., the spatial and temporal adaptive reflectance fusion model (STARFM), the flexible spatiotemporal data fusion (FSDAF), and the spatial and temporal nonlocal filter-based fusion model (STNLFFM), in fusing MODIS and Landsat 8 images was investigated. The fusion results in Helong City show that the STNLFFM algorithm generated the best fused images. The correlation coefficients between the fusion images and actual Landsat images on May 28 and October 19 were 0.9746 and 0.9226, respectively, with an average of 0.9486. Dense Landsat-like time series at 8-day time intervals were generated using this method. This time series imagery and topography-derived features were used as predictor variables. Four machine learning methods, i.e., K-nearest neighbors (KNN), random forest (RF), artificial neural networks (ANNs), and light gradient boosting machine (LightGBM), were selected for tree species classification in Helong City, Jilin Province. An ensemble classifier combining these classifiers was constructed to further improve the accuracy. The ensemble classifier consistently achieved the highest accuracy in almost all classification scenarios, with a maximum overall accuracy improvement of approximately 3.4% compared to the best base classifier. Compared to only using a single temporal image, utilizing dense time series and the ensemble classifier can improve the classification accuracy by about 20%, and the overall accuracy reaches 84.32%. In conclusion, using spatiotemporal fusion and the ensemble classifier can significantly enhance tree species identification in cloudy mountainous areas with poor data availability. Full article