Search Results (29)

The fraction of absorbed photosynthetically active radiation (FAPAR) stands as a pivotal parameter within the Earth system, quantifying the energy exchange between vegetation and solar radiation. Accordingly, there is an urgent need for comprehensive validation studies to accurately quantify uncertainties and improve the reliability of FAPAR-based applications. This study validated five global FAPAR products, MOD15A2H, MYD15A2H, VNP15A2H, GEOV2, and GEOV3, over four boreal forest sites in North America. Qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) of each product were analyzed. Time series high-resolution reference FAPAR maps were developed using the Harmonized Landsat and Sentinel-2 dataset. The reference FAPAR maps revealed a strong agreement with the in situ FAPAR from AmeriFlux (correlation coefficient (R) = 0.91; root mean square error (RMSE) = 0.06). The results revealed that global FAPAR products show similar uncertainties (RMSE: 0.16 ± 0.04) and moderate agreement with the reference FAPAR (R = 0.75 ± 0.10). On average, 34.47 ± 6.91% of the FAPAR data met the goal requirements of the Global Climate Observing System (GCOS), while 54.41 ± 6.89% met the threshold requirements of the GCOS. Deciduous forests perform better than evergreen forests, and the products tend to underestimate the reference data, especially for the beginning and end of growing seasons in evergreen forests. There are no obvious quality differences at different QQFs, and the relative QQI can be used to filter high-quality values. To enhance the regional applicability of global FAPAR products, further algorithm improvements and expanded validation efforts are essential. Full article

In protected areas of the Brazilian savannas (Cerrado), Land Surface Phenology (LSP) is influenced by both precipitation and fire, but the nature of these relationships remains unexplored. Here, we assessed the impacts of precipitation and fire on LSP metrics derived from the Normalized Difference Vegetation Index (NDVI) at Emas National Park (ENP). Using TIMESAT, along with the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and 30-m Harmonized Landsat Sentinel (HLS) products, we investigated these effects in both grassland and woodland areas. To evaluate the effects of precipitation, we identified the driest and wettest seasonal cycles between 2002 and 2023 and analyzed the relationships between accumulated rainfall during the rainy season and each of the 13 TIMESAT metrics. To assess the effects of fire, three major events were examined: 1 September 2005 (affecting 45% of the park’s area), 12 August 2010 (90%), and 10 July 2021 (21%). The burned grassland area and the subsequent vegetation recovery following the 2021 event were analyzed in detail using a non-burned control site and LSP metrics extracted from the HLS product, covering both pre- and post-disturbance cycles. The results indicated that the metrics most positively correlated to precipitation were Amplitude (AMP), End of Season (EOS), Large and Small Seasonal Integrals (LSI and SSI), and Rate of Increase at the Beginning of the Season (RIBS). The highest correlation coefficients were found in woodland areas, which were less affected by fire disturbance than grassland areas. Similar trends were observed in the behavior of AMP, EOS, and SSI in response to both precipitation and fire, with fire exerting a stronger influence. By decoupling the fire effects from rainfall influence using the control site, we identified Base Level (BL), SSI, EOS, AMP, and Values at the End and Start of the Season (VES and VSS), as the metrics most sensitive to fire and subsequent vegetation recovery in burned areas. The effects of fire were evident for most metrics, both during the disturbance cycle and in the post-fire cycle. Our study underscores the importance of combining MODIS and HLS time series to understand vegetation phenology in the Cerrado. Full article

This study investigates the spatial–temporal dynamics of vegetation indices in olive orchards across two traditionally rainfed regions of the Iberian Peninsula, namely the “Trás-os-Montes” (TM) agrarian region in Portugal and the Badajoz (BA) province in Spain, in response to drought conditions. Using satellite-derived vegetation indices, derived from the Harmonized Landsat Sentinel-2 project (HLSL30), such as the Normalized Difference Moisture Index (NDMI) and Soil-Adjusted Vegetation Index (SAVI), this study evaluates the impact of drought periods on olive tree growing conditions. The Mediterranean Palmer Drought Severity Index (MedPDSI), specifically developed for olive trees, was selected to quantify drought severity, and impacts on vegetation dynamics were assessed throughout the study period (2015–2023). The analysis reveals significant differences between the regions, with BA experiencing more intense drought conditions, particularly during the warm season, compared to TM. Seasonal variability in vegetation dynamics is clearly linked to MedPDSI, with lagged responses stronger in the previous two-months. Both the SAVI and the NDMI show vegetation vigour declines during dry seasons, particularly in the years of 2017 and 2022. The findings reported in this study highlight the vulnerability of rainfed olive orchards in BA to long-term drought-induced stress, while TM appears to have slightly higher resilience. The study underscores the value of combining satellite-derived vegetation indices with drought indicators for the effective monitoring of olive groves and to improve water use management practices in response to climate change. These insights are crucial for developing adaptation measures that ensure the sustainability, resiliency, and productivity of rainfed olive orchards in the Iberian Peninsula, particularly under climate change scenarios. Full article

Southern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such as NASA’s GEDI spaceborne LiDAR, enable more precise mapping of canopy cover. Although GEDI provides accurate data, its limited spatial coverage restricts large-scale assessments. To address this, we combined GEDI with Synthetic Aperture Radar (SAR), and optical imagery (Sentinel-1 GRD and Landsat–Sentinel Harmonized (HLS)) data to create a comprehensive canopy cover map for Florida. Using a random forest algorithm, our model achieved an R² of 0.69, RMSD of 0.17, and MD of 0.001, based on out-of-bag samples for internal validation. Geographic coordinates and the red spectral channel emerged as the most influential predictors. External validation with airborne laser scanning (ALS) data across three sites yielded an R² of 0.70, RMSD of 0.29, and MD of −0.22, confirming the model’s accuracy and robustness in unseen areas. Statewide analysis showed lower canopy cover in southern versus northern Florida, with wetland forests exhibiting higher cover than upland sites. This study demonstrates the potential of integrating multiple remote sensing datasets to produce accurate vegetation maps, supporting forest management and sustainability efforts in Florida. Full article

The integration of the Crop Growth Model (CGM), Radiative Transfer Model (RTM), and Machine Learning Algorithm (MLA) for estimating crop traits represents a cutting-edge area of research. This integration requires in-depth study to address RTM limitations, particularly of similar spectral responses from multiple input combinations. This study proposes the integration of CGM and RTM for crop trait retrieval and evaluates the performance of CGM output-based RTM spectra generation for multiple crop traits estimation without biased sampling using machine learning models. Moreover, PROSAIL spectra as training against Harmonized Landsat Sentinel-2 (HLS) as testing was also compared with HLS data only as an alternative. It was found that satellite data (HLS, 80:20) not only consistently performed better, but PROSAIL (train) and HLS (test) also had satisfactory results for multiple crop traits from uniform training samples in spite of differences in simulated and real data. PROSAIL-HLS has an RMSE of 0.67 for leaf area index (LAI), 5.66 µg/cm² for chlorophyll ab (Cab), 0.0003 g/cm² for dry matter content (Cm), and 0.002 g/cm² for leaf water content (Cw) against the HLS only, with an RMSE of 0.40 for LAI, 3.28 µg/cm² for Cab, 0.0002 g/cm² for Cm, and 0.001 g/cm² for Cw. Optimized machine learning models, namely Extreme Gradient Boost (XGBoost) for LAI, Support Vector Machine (SVM) for Cab, and Random Forest (RF) for Cm and Cw, were deployed for temporal mapping of traits to be used for wheat productivity enhancement. Full article

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPP_HLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPP_MODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPP_EC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide. Full article

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R² = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data. Full article

In the Mediterranean area, vegetation dynamics and phenology analysed over a long time can have an important role in highlighting changes in land use and cover as well as the effect of climate change. Over the last 30 years, remote sensing has played an essential role in bringing about these changes thanks to many types of observations and techniques. Satellite images are to be considered an important tool to grasp these dynamics and evaluate them in an inexpensive and multidisciplinary way thanks to Landsat and Sentinel satellite constellations. The integration of these tools holds a dual potential: on the one hand, allowing us to obtain a longer historical series of reflectance data, while on the other hand making data available with a higher frequency even within a specific timeframe. The study aims to conduct a comprehensive cross-comparison analysis of long-time-series pixel values in the Mediterranean regions. For this scope comparisons between Landsat-7 (ETM+), Landsat-8 (OLI), and Sentinel-2 (MSI) satellite sensors were conducted based on surface reflectance products. We evaluated these differences using Ordinary Least Squares (OLS) and Major Axis linear regression (RMA) analysis on points extracted from over 15,000 images across the Mediterranean basin area from 2017 to 2020. Minor but consistent differences were noted, necessitating the formulation of suitable adjustment equations to better align Sentinel-2 reflectance values with those of Landsat-7 or Landsat-8. The results of the analysis are compared with the most-used harmonization coefficients proposed in the literature, revealing significant differences. The root-mean-square deviation, the mean difference and the orthogonal distance regression (ODR) slope show an improvement of the parameters for both models used (OLS and RMA) in this study. The discrepancies in reflectance values leads to corresponding variations in the estimation of biophysical parameters, such as NDVI, showing an increase in the ODR slope of 0.3. Despite differences in spatial, spectral, and temporal characteristics, we demonstrate that integration of these datasets is feasible through the application of band-wise regression corrections for a sensitive and heterogeneous area like those of the Mediterranean basin area. Full article

Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021–2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture. Full article

Paddy rice cropping patterns (PRCPs) play important roles in both agroecosystem modeling and food security. Although paddy rice maps have been generated over several regions using satellite observations, few studies have focused on mapping diverse smallholder PRCPs, which include crop rotation and are dominant cropping structures in South China. Here, an approach called the feature selection and hierarchical classification (FSHC) method was proposed to effectively identify paddy rice and its rotation types. Considering the cloudy and rainy weather in South China, a harmonized Landsat and Sentinel-2 (HLS) surface reflectance product was employed to increase high-quality observations. The FSHC method consists of three processes: cropping intensity mapping, feature selection, and decision tree (DT) model development. The FSHC performance was carefully evaluated using crop field samples obtained in 2018 and 2019. Results suggested that the derived cropping intensity map based on the Savitzky–Golay (S-G) filtered normalized difference vegetation index (NDVI) time series was reliable, with an overall accuracy greater than 93%. Additionally, the optimal spectral (i.e., normalized difference water index (NDWI) and land surface water index (LSWI)) and temporal (start-of-season (SOS) date) features for distinguishing different PRCPs were successfully identified, and these features are highly related to the critical growth stage of paddy rice. The developed DT model with three hierarchical levels based on optimal features performed satisfactorily, and the identification accuracy of each PRCP can be achieved approximately 85%. Furthermore, the FSHC method exhibited similar performances when mapping PRCPs in adjacent years. These results demonstrate that the proposed FSHC approach with HLS data can accurately extract diverse PRCPs over fragmented croplands; thus, this approach represents a promising opportunity for generating refined crop type maps. Full article

In this study, an approach for the harmonized calculation of the Leaf Area Indices (LAIs) for agronomic crops from Sentinel-2 MSI and Landsat OLI multispectral satellite data is proposed in order to obtain a dense seasonal trajectory. It was developed and tested on dominant crops grown in the Czech Republic, including winter wheat, spring barley, winter rapeseed, alfalfa, sugar beet, and corn. The two-step procedure harmonizing Sentinel-2 MSI and Landsat OLI spectral data began with deriving NDVI, MSAVI, and NDWI_1610 vegetation indices (VIs) as proxy indicators of green biomass and foliage water content, the parameters contributing most to a stand’s spectral response. Second, a simple linear transformation was applied to the resulting VI values. The regression model itself was built on an artificial neural network, then trained on PROSAIL simulations data. The LAI estimates were validated using an extensive dataset of in situ measurements collected during 2017 and 2018 in the lowlands of the Central Bohemia Region. Very strong agreement was observed between LAI estimates from both Sentinel-2 MSI and Landsat OLI data and independent ground-based measurements (r between 0.7 and 0.98). Very good results were also achieved in the mutual comparison of Sentinel-2 and Landsat-based LAI datasets (rRMSE < 20%, r between 0.75 and 0.99). Using data from all currently available Sentinel-2 (A/B) and Landsat (8/9) satellites, a dense harmonized LAI time series can be created with high potential for use in precision agriculture. Full article

Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The establishment of a VC out of individual missions offers new possibilities for many application domains, in particular in the fields of land surface monitoring and change detection. In this context, this paper describes the Copernicus Sen2Like algorithms and software, a solution for harmonizing and fusing Landsat 8/Landsat 9 data with Sentinel-2 data. Developed under the European Union Copernicus Program, the Sen2Like software processes a large collection of Level 1/Level 2A products and generates high quality Level 2 Analysis Ready Data (ARD) as part of harmonized (Level 2H) and/or fused (Level 2F) products providing high temporal resolutions. For this purpose, we have re-used and developed a broad spectrum of data processing and analysis methodologies, including geometric and spectral co-registration, atmospheric and Bi-Directional Reflectance Distribution Function (BRDF) corrections and upscaling to 10 m for relevant Landsat bands. The Sen2Like software and the algorithms have been developed within a VC establishment framework, and the tool can conveniently be used to compare processing algorithms in combinations. It also has the potential to integrate new missions from spaceborne and airborne platforms including unmanned aerial vehicles. The validation activities show that the proposed approach improves the temporal consistency of the multi temporal data stack, and output products are interoperable with the subsequent thematic analysis processes. Full article

The Brazilian Savanna presents a complex agricultural dynamic and cloud cover issues; therefore, there is a need for new strategies for more detailed agricultural monitoring. Using a hierarchical classification system, we explored the Harmonized Landsat Sentinel-2 (HLS) dataset to detect soybean in western Bahia, Brazil. Multispectral bands (MS) and vegetation indices (VIs) from October 2021 to March 2022 were used as variables to feed Random Forest models, and the performances of the complete HLS time-series, HLSS30 (harmonized Sentinel), HLSL30 (harmonized Landsat), and Landsat 8 OLI (L8) were compared. At Level 1 (agricultural areas × native vegetation), HLS, HLSS30, and L8 produced identical models using MS + VIs, with 0.959 overall accuracies (OA) and Kappa of 0.917. At Level 2 (annual crops × perennial crops × pasturelands), HLS and L8 achieved an OA of 0.935 and Kappa > 0.89 using only VIs. At Level 3 (soybean × other annual crops), the HLS MS + VIs model achieved the best performance, with OA of 0.913 and Kappa of 0.808. Our results demonstrated the potential of the new HLS dataset for medium-resolution mapping initiatives at the crop level, which can impact decision-making processes involving large-scale soybean production and agricultural sustainability. Full article

Dense time series of remote sensing images with high spatio-temporal resolution are critical for monitoring land surface dynamics in heterogeneous landscapes. Spatio-temporal fusion is an effective solution to obtaining such time series images. Many spatio-temporal fusion methods have been developed for producing high spatial resolution images at frequent intervals by blending fine spatial images and coarse spatial resolution images. Previous studies have revealed that the accuracy of fused images depends not only on the fusion algorithm, but also on the input image pairs being used. However, the impact of input images dates on the fusion accuracy for time series with different temporal variation patterns remains unknown. In this paper, the impact of input image pairs on the fusion accuracy for monotonic linear change (MLC), monotonic non-linear change (MNLC), and non-monotonic change (NMC) time periods were evaluated, respectively, and the optimal selection strategies of input image dates for different situations were proposed. The 16-day composited NDVI time series (i.e., Collection 6 MODIS NDVI product) were used to present the temporal variation patterns of land surfaces in the study areas. To obtain sufficient observation dates to evaluate the impact of input image pairs on the spatio-temporal fusion accuracy, we utilized the Harmonized Landsat-8 Sentinel-2 (HLS) data. The ESTARFM was selected as the spatio-temporal fusion method for this study. The results show that the impact of input image date on the accuracy of spatio-temporal fusion varies with the temporal variation patterns of the time periods being fused. For the MLC period, the fusion accuracy at the prediction date (PD) is linearly correlated to the time interval between the change date (CD) of the input image and the PD, but the impact of the input image date on the fusion accuracy at the PD is not very significant. For the MNLC period, the fusion accuracy at the PD is non-linearly correlated to the time interval between the CD and the PD, the impact of the time interval between the CD and the PD on the fusion accuracy is more significant for the MNLC than for the MLC periods. Given the similar change of time intervals between the CD and the PD, the increments of R² of fusion result for the MNLC is over ten times larger than those for the MLC. For the NMC period, a shorter time interval between the CD and the PD does not lead to higher fusion accuracies. On the contrary, it may lower the fusion accuracy. This study suggests that temporal variation patterns of the data must be taken into account when selecting optimal dates of input images in the fusion model. Full article

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta. Full article