Normalized Difference Vegetation Index Images Research Articles

Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation

Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

UPM Satellite Ground Station Data for Borneo NDVI Image Analysis

Small satellites have been developed and used for earth observations as the remote sensing data can be used to perform analysis such as weather forecasts, vegetation monitorings, climate changes, and disaster monitorings. The Flying Laptop satellite (Flying Laptop) is the first small satellite developed by the University of Stuttgart (Germany), which is mainly used for research and development projects. The raw data obtained from Flying Laptop is used in the data analysis and data processing, which involve the ground tracks and images analysis. The filtered images are used to formulate Normalized Difference Vegetation Index (NDVI) plots, which are broadly used for the vegetation monitor in recent years. Due to the weather influences and the available filter in the Flying Laptop imaging payload, the generated NDVI plot needs to be improved in terms of its contrast and the difference in an identification of landcover type for better monitoring purposes. The ground track analysis and image comparison with respect to the fixed ground were used to identify the location of the image being captured and filtered out for unusable images. The formulation of NDVI is repeated with cloud maskings using Normalized Difference Water Index (NDWI) as references and accordingly normalized the plot in a logarithmic scale. The proposed method of using the cloud masking with references to NDWI improves the contrast of the overall NDVI plots. The usage of automation also helps to improve the efficiency in generating NDVI plots.

Comparison of Mangrove Index (MI) and Normalized Difference Vegetation Index (NDVI) for the detection of degraded mangroves in Alas Purwo Banyuwangi and Segara Anakan Cilacap, Indonesia

Deterioration of the environmental quality of mangrove ecosystem must be identified as early as possible for management and threat mitigation. Degraded mangrove areas have to be distinguished from healthy ones. Low value of Mangrove Index (MI) corresponded to the degraded mangrove area where dominated by two species (Derris trifoliata and Acanthus ilicifolius), which are indicators of mangrove degradation at Segara Anakan mangrove area. The Normalized Difference Vegetation Index (NDVI) failed to detect the degaradation due to existing of these species by detecting as high index value. The study was designed to (1) test the performance of MI at Alas Purwo mangrove area which categorized as a non-degraded mangrove forest, (2) compare the spatial pattern of MI images with that of NDVI images, and (3) further investigate the ability of MI to detect areas of degraded mangrove when it occurs in only small areas. We used Landsat-8 OLI imagery because it includes the short-wave infrared (SWIR) bands required to generate MI. Both direct visual comparison and correlation analysis of the resulting maps were conducted to evaluate the ability of these indices to detect degraded and non-degraded areas of mangroves. In general, both indices detected a similar pattern, which is contrary to the results from Segara Anakan images. However, a difference value between MI and NDVI of >0.2 indicated the presence of small areas of vegetation with a spectral reflectance similar to that of species indicating degradation at Segara Anakan. This finding was confirmed by field observation of mangrove species. A difference of >0.2 between MI and NDVI indicates mangrove degradation for small areas. This study provides a new tool to determine the condition of mangrove forest remotely, allowing for rapid and targeted management action to tackle degradation.

Design of a Multilayer Perceptron Network Based on the Normalized Histogram to Develop Yields Predictive Model

This article presents a multilayer perceptron (MLP) for patio-temporal remote sensing analysis of satellite image time series for a yield prediction model. This paper illustrates the model for crop yield prediction from the spatial cumulation of normalized difference vegetation index (NDVI) image time series at the province level. The methodological framework comprises the transformation of each NDVI image into a histogram to ensure no loss of information in the mapping of high- dimensional/unstructured NDVI images into pixels consideration, to be then used in training the MLP. The research work also includes an analysis of several activation functions for the hidden layer and testing their consequences on accuracy, including Radial-Basis (RadBas), Logarithmic-Sigmoid (LogSig), Hyperbolic-Tangent Sigmoid (TanSig), which depend mainly on exponential functions and limit the amplitude of the output. The proposed approach was utilized to predict the winter crop yield in Diwaniyah-Iraq province, one of the main agriculture regions, whose economy considerably depends on crop production. It can also be extended to other crops and other regions in Iraq. The proposed methodology showed the ability to predict the crop yield around seven-nine weeks before harvest. It also, outperformed the performance of traditional approaches by transforming the input into a more convenient form that reflects more useful information. The results show that the proposed model provides efficient accuracy (determination coefficient R2 > 0.85 and error level <0.24).

Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018–2022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018–2022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 °C in the areas that were changed from gardens to buildings.

SAR2NDVI: a pix2pix generative adversarial network for reconstructing field-level normalized difference vegetation index time series using Sentinel-1 synthetic aperture radar data

The normalized difference vegetation index (NDVI) is essential for monitoring urban green space, forest cover, and crop growth from sowing to harvesting. High-resolution images from Sentinel-2 and Landsat-8 satellites are available nowadays to get field-level phenological information at short intervals. However, cloud cover is one of the biggest hindrances in vegetation monitoring using NDVI, resulting in data gaps. To address this issue, we propose an NDVI gap-filling methodology to generate cloud-free NDVI time series from Sentinel-1 SAR data using the pix2pix generative adversarial network (GAN) model. Pix2pix GANs with generators based on U-Net and ResNet were designed, and the performance of the models was compared for both the VV and VH polarizations. The generalization capability of the models was studied using synthetic aperture radar (SAR) and NDVI image pairs with different vegetation, field sizes, and shapes. Experimental results show that the generated synthetic NDVI images can effectively substitute the cloudy images for gap-filling. Compared to ResNet, the U-Net generator has given the best results with PSNR = 29.447 dB, RMSE = 0.055, and Pearson correlation coefficient ρ = + 0.909 for VH polarization.

Automatic Detection of Floating Macroalgae via Adaptive Thresholding Using Sentinel-2 Satellite Data with 10 m Spatial Resolution

Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu’s, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined.

Generative adversarial approach to urban areas NDVI estimation: A case study of Łódź, Poland

Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. In- painting, dataset augmentation using artificial samples, or increasing spatial resolution of aerial imagery are only a few notable examples of utilising GANs in remote sensing (RS). The normalised difference vegetation index (NDVI) ground-truth labels were prepared by combining RGB and NIR orthophotos. The dataset was then utilised as input for a conditional generative adversarial network (cGAN) to perform an image-to-image translation. The main goal of the neural network was to generate an artificial NDVI image for each processed 256 px × 256 px patch using only in- formation available in the panchromatic input. The network achieved a structural similarity index measure (SSIM) of 0.7569 ± 0.1083, a peak signal-to-noise ratio (PSNR) of 26.6459 ± 3.6577 and a root-mean-square error (RSME) of 0.0504 ± 0.0193 on the test set, which should be considered high. The perceptual evaluation was performed to verify the meth- od’s usability when working with a real-life scenario. The research confirms that the structure and texture of the pan- chromatic aerial RS image contain sufficient information for NDVI estimation for various objects of urban space. Even though these results can highlight areas rich in vegetation and distinguish them from the urban background, there is still room for improvement regarding the accuracy of the estimated values. The research aims to explore the possibility of utilising GAN to enhance panchromatic images (PAN) with information related to vegetation. This opens exciting opportunities for historical RS imagery processing and analysis.

Deep Feature and Domain Knowledge Fusion Network for Mapping Surface Water Bodies by Fusing Google Earth RGB and Sentinel-2 Images

Mapping surface water bodies from fine spatial resolution optical remote sensing imagery is essential for the understanding of the global hydrologic cycle. Although satellite data are useful for mapping, the limited spectral information captured by some satellite systems can be suboptimal for the task. For example, the very high-resolution images of Google Earth (GE) only contain RGB bands, which often means many water bodies and land objects are confused. Sentinel-2 (S2) imagery has a spectral resolution more suitable for mapping water bodies, but its medium spatial resolution limits the ability for detailed mapping of water-land boundaries. This letter proposes a deep feature and domain knowledge fusion network (DFDKFNet) for mapping surface water bodies by fusing GE and S2 images while incorporating domain knowledge. DFDKFNet uses the remote sensing indices of normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) derived from the S2 image as the representative domain knowledge to better extract water bodies from terrestrial features. A similar pixel-based approach is used to downscale the NDWI and NDVI maps to match the spatial resolution between the GE and S2 images. The DFDKFNet uses the GE and downscaled NDWI and NDVI images to extract the deep semantic features of water bodies, which are fused with the domain knowledge extracted from the NDWI and NDVI images. DFDKFNet was compared with several state-of-the-art algorithms, and the results show that DFDKFNet can enhance water body mapping accuracy.

The Impact of the Type and Spatial Resolution of a Source Image on the Effectiveness of Texture Analysis

This paper presents a study on the effectiveness of texture analysis of remote sensing imagery depending on the type and spatial resolution of the source image. The study used the following image types: near-infrared band, red band, first principal component, second principal component and normalized difference vegetation index images of pixel size from 2 m to 30 m, generated from a multispectral WorldView-2 image. The study evaluated the separability of the selected pairs of the following land cover classes: bare soil, low vegetation, coniferous forest, deciduous forest, water reservoirs, built-up areas. The tool used for texture analysis was granulometric analysis based on morphological operations—one of less popular methods which, however, as demonstrated by previous studies, shows high effectiveness in separating classes of different texture. The conducted study enabled researchers to evaluate the significance of image type and resolution for visibility of texture in the image and the possibility of using texture to differentiate between classes. The obtained results showed that there is no single, universal combination of conditions of texture analysis, which would be the best from the point of view of all classes. For most of the analyzed pairs of classes, the best results were obtained for the highest spatial resolution of the image (2–3 m), but the class of built-up areas stands out in this comparison—the best distinction was obtained with the average spatial resolution (10–15 m). Research has also shown that there is no single type of image that is universally the best basis for texture analysis. While for the majority of classes the image of the first principal component was the best, for the class of built-up areas it was the image of the red channel.

Classification of Tree Composition in the Forest Using Images from SENTINEL-2: A Case Study of Geomunoreum Forests Using NDVI Images

Climate change may alter tree species’ distribution, which could impact on forest biodiversity. However, frequent and continuous surveys of forests need intense labor and are time-consuming. The current study utilized SENTINEL-2 images of Geomunoreum to solve this problem as a case study. Acquired images were converted into various indices, such as the normalized difference vegetation index (NDVI), which could be an efficient method to examine the diversity in forests over time. In the current study, the images were obtained in March and April from 2017 to 2021. As a result of analysis using NDVI images of the study area taken from the satellite, vegetation groups were classified into evergreen trees and deciduous trees. This implies that NDVI using extracted data from SENTINEL-2 images could be used for surveying large-scale examinations for tree classification in order to observe variations caused by climate change in an efficient and cost-effective manner.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Extraction and spatiotemporal changes of open-pit mines during 1985-2020 using Google Earth Engine: A case study of Qingzhou City, Shandong Province, China.

The global use of mineral resources has increased exponentially for decades and will continue to grow for the foreseeable future, resulting in increasingly negative impacts on the surrounding environment. However, to date, there are a lack of historical and current spatial extent datasets with high accuracy for mining areas in many parts of the world, which has hindered a more comprehensive understanding of the environmental impacts of mining. Using the Google Earth Engine cloud platform and the Landsat normalized difference vegetation index (NDVI) datasets, the spatial extent data of open-pit mining areas for eight years (1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020) was extracted by the Otsu algorithm. The limestone mining areas in Qingzhou, Shandong Province, China, was selected as a case study. The annual maximum NDVI was first derived from the Landsat NDVI datasets, and then the Otsu algorithm was used to segment the annual maximum NDVI images to obtain the extent of the mining areas. Finally, the spatiotemporal characteristics of the mining areas in the study region were analyzed in reference to previous survey data. The results showed that the mining areas were primarily located in Shaozhuang Town, Wangfu Street and the northern part of Miaozi Town, and the proportion of mining areas within these three administrative areas has increased annually from 88% in 1985 to more than 98% in 2010. Moreover, the open-pit mining areas in ​​Qingzhou gradually expanded from a scattered, point-like distribution to a large, contiguous distribution. From 1985 to 2020, the open-pit mining area expanded to more than 10 times its original size at a rate of 0.5 km2/year. In 2015, this area reached its maximum size of 19.7 km2 and slightly decreased in 2020. Furthermore, the expansion of the mining areas in Qingzhou went through three stages: a slow growth period before 1995, a rapid expansion period from 1995 to 2005, and a shutdown and remediation period after 2005. A quantitative accuracy assessment was performed by calculating the Intersection over Union (IoU) of the extraction results and the visual interpretation results from Gaofen-2 images with 1-m spatial resolution. The IoU reached 72%. The results showed that it was feasible to threshold the Landsat annual maximum NDVI data by the Otsu algorithm to extract the annual spatial extent of the open-pit mining areas. Our method will be easily transferable to other regions worldwide, enabling the monitoring of mine environments.

Land Used Mapping using Unmanned Aerial Vehicle (UAV) along Parit Rasipan Drainage System

Excess nutrients accelerate the growth of plants and algae in water sources, leading to environmental issues like flow retardation (flood), a decrease in the amount of oxygen in the water, and a decline in the quality of the water. Therefore, the goal of this study is to identify the qualityParit Rasipan drainage system's eutrophic level in terms of temperature, dissolved oxygen, pH, and turbidity; and to analyse an NDVI (Normalized Difference Vegetation Index) image taken by unmanned aerial vehicle (UAV) and using Agisoft photoscan software. As a result, four sampling locations—residential (S1), industrial (S2), agricultural (S3), and farming (S4)—have been chosen for water quality sampling and analysis along the Parit Rasipan drainage system. The HACH method wasused to analyse water samples that have been obtained according to the Standard Methods for Water and Wastewater Examinations. It was found that agricultural zone waspoor water quality compared residential, industrial and farmingzoneswithmeasurements of 3.82 mg/L, 3.09, 25.57 °C, and 14.6 NTU, respectively, the agricultural zone hasthe highest values for thefour metrics of dissolved oxygen, pH, temperature, and turbidity. This scenario could be due to the usage of the fertilizerto cultivate the oil palmcontributed toParit rasipan drainage system (S3)which resulted in asubstantial amount of eutrophication. Hence, the use of UAVs and the agisoft photoscan programme has considerably improved the mapping of the water quality metrics and eutrophic level.

An MLC and U-Net Integrated Method for Land Use/Land Cover Change Detection Based on Time Series NDVI-Composed Image from PlanetScope Satellite

Land use/land cover change (LUCC) detection based on optical remote-sensing images is an important research direction in the field of remote sensing. The key to it is to select an appropriate data source and detection method. In recent years, the continuous expansion of construction land in urban areas has become the main reason for the increase in LUCC demand. However, due to the complexity and diversity of land-cover types, it is difficult to obtain high-precision classification results. In this article, a 12-month time series NDVI (Normalized Difference Vegetation Index) image of the study area was generated based on the high spatial and temporal resolution PlanetScope satellite images. According to the time series NDVI image, representative land-cover samples were selected, and the changed land samples were selected at the same time. This method could directly obtain the LUCC detection results of the study area through land-cover classification. First, Maximum Likelihood Classification (MLC), a classical machine-learning method, was used for supervised classification, and the samples needed for deep learning were selected according to the classification results. Then, the U-Net model, which can fully identify and explore the deep semantic information of the time series NDVI image, was used for land classification. Finally, this article made a comparative analysis of the two classification results. The results demonstrate that the overall classification accuracy based on time series NDVI is significantly higher than that of single-scene NDVI and mean NDVI. The LUCC detection method proposed in this article can effectively extract changed areas. The overall accuracy of the MLC and U-Net model is 79.38% and 85.26%, respectively. Therefore, the deep-learning method can effectively improve the accuracy of land-cover classification and change detection.

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands.

Urban land use land cover mapping in tropical savannah using Landsat-8 derived normalized difference vegetation index (NDVI) threshold

Generation of land use/land cover map at different spatial scales using satellite remote sensing data has been in practice as far back as early 1970s. Since then, research focus has been on the development of classification steps and improving the quality of the resulting maps. In recent times, the demand for detailed high accuracy land-use and land-cover (LULC) data has been on the increase due to the growing complexity of earth processes, while, at the same time, processing step is becoming more complex. This paper explores Landsat 8 derived normalized difference vegetation index (NDVI) threshold for the purpose of simplifying land cover classification process. NDVI images of January, May and December, 2018, representing dry, wet and harmattan seasons were generated. Thereafter, NDVI values corresponding to the location of a set of training data representing the target urban land covers (water, built-up area, soil, grassland and shrub) were extracted. Using the statistics of the extracted values, NDVI threshold for the respective land cover type were determined for the classification process. Finally, the classification accuracy was evaluated using the unbiased matrix coefficient technique which produced overall accuracy of 71.3%, 46.4% and 75.6% at 95% confidence limit for the months of January, May and December of the year review respectively. The result has shown that NDVI threshold is a simple and practical alternative to obtain LULC map at a reasonable time with a few data.

Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai–Tibetan Plateau from 2000–2020

As the largest and highest alpine ecoregion in the world, the Qinghai–Tibetan Plateau (QTP) is extremely sensitive to climate change and has experienced extraordinary warming during the past several decades; this has greatly affected various ecosystem processes in this region such as vegetation production and phenological change. Therefore, numerous studies have investigated changes in vegetation dynamics on the QTP using the satellite-derived normalized-difference vegetation index (NDVI) time-series data provided by the Moderate-Resolution Imaging Spectroradiometer (MODIS). However, the highest spatial resolution of only 250 m for the MODIS NDVI product cannot meet the requirement of vegetation monitoring in heterogeneous topographic areas. In this study, therefore, we generated an 8-day and 30 m resolution NDVI dataset from 2000 to 2020 for the QTP through the fusion of 30 m Landsat and 250 m MODIS NDVI time-series data. This dataset, referred to as QTP-NDVI30, was reconstructed by employing all available Landsat 5/7/8 images (>100,000 scenes) and using our recently developed gap-filling and Savitzky–Golay filtering (GF-SG) method. We improved the original GF-SG approach by incorporating a module to process snow contamination when applied to the QTP. QTP-NDVI30 was carefully evaluated in both quantitative assessments and visual inspections. Compared with reference Landsat images during the growing season in 100 randomly selected subregions across the QTP, the reconstructed 30 m NDVI images have an average mean absolute error (MAE) of 0.022 and a spatial structure similarity (SSIM) above 0.094. We compared QTP-NDVI30 with upscaled cloud-free PlanetScope images in some topographic areas and observed consistent spatial variations in NDVI between them (averaged SSIM = 0.874). We further examined an application of QTP-NDVI30 to detect vegetation green-up dates (GUDs) and found that QTP-NDVI30-derived GUD data show general agreement in spatial patterns with the 250 m MODIS GUD data, but provide richer spatial details (e.g., GUD variations at the subpixel scale). QTP-NDVI30 provides an opportunity to monitor vegetation and investigate land-surface processes in the QTP region at fine spatiotemporal scales.

A Sensor Bias Correction Method for Reducing the Uncertainty in the Spatiotemporal Fusion of Remote Sensing Images

With the development of multisource satellite platforms and the deepening of remote sensing applications, the growing demand for high-spatial resolution and high-temporal resolution remote sensing images has aroused extensive interest in spatiotemporal fusion research. However, reducing the uncertainty of fusion results caused by sensor inconsistencies and input data preprocessing is one of the challenges in spatiotemporal fusion algorithms. Here, we propose a novel sensor bias correction method to correct the input data of the spatiotemporal fusion model through a machine learning technique learning the bias between different sensors. Taking the normalized difference vegetation index (NDVI) images with low-spatial resolution (MODIS) and high-spatial resolution (Landsat) as the basic data, we generated the neighborhood gray matrices from the MODIS image and established the image bias pairs of MODIS and Landsat. The light gradient boosting machine (LGBM) regression model was used for the nonlinear fitting of the bias pairs to correct MODIS NDVI images. For three different landscape areas with various spatial heterogeneities, the fusion of the bias-corrected MODIS NDVI and Landsat NDVI was conducted by using the spatiotemporal adaptive reflection fusion model (STARFM) and the flexible spatiotemporal data fusion method (FSDAF), respectively. The results show that the sensor bias correction method can enhance the spatially detailed information in the input data, significantly improve the accuracy and robustness of the spatiotemporal fusion technology, and extend the applicability of the spatiotemporal fusion models.