Cloud detection and analysis of MODIS image

Clouds play a major role in controlling Earth's climate, and cloud detection is a crucial step in the numerical weather prediction, and global climate models. Multi-angle imaging spectroradiometer (MLSR) and moderate resolution imaging spectroradiometer (MODIS) were launched in 1999 by clouds. However, cloud detection algorithms using either MISR or MODIS data separately do not take full advantage of the data collected by both sensors. In this paper, we propose and test two schemes to combine MISR and MODIS data for cloud detection in polar regions, Both schemes are follow-ups of a two-step polar cloud detection algorithm using MISR data: enhanced linear correlation matching classification followed by quadratic discriminate analysis (ELMS-QDA) (T, Shi et al.). The first scheme is mapping the MODIS cloud detection results to the MISR grid based on a nearest neighbor method, then only reporting the agreed pixels of the ELCMC-QDA results (from MISR) and MODIS operational results. This scheme improves the classification accuracy, but reduces the coverage of the results. Instead of combining the MISR and MODIS results directly, the second scheme uses the agreed pixels of ELCMC results and MODIS operational results as the training data for the QDA on MISR features, and output the results from the QDA. Both schemes are tested over a region where expert labels show that both MISR and MODIS operational algorithms do not work well (according to expert labels, 53% and 12.72% misclassification rates for MISR and MODIS operational algorithms respectively). The first scheme only makes 0.72% of errors, but leaves 68.72% of pixels unclassified. The second scheme reaches a 2.93% of misclassification rate, which is smaller than a 4.09% rate from ELCMC-QDA, and it provides a full coverage. Hence we propose using QDA on ELCMC and MODIS agreed pixels as an algorithm to fuse the MISR and MODIS information for the polar cloud detection.

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.

Knowledge of the variation and regulation of the diffuse attenuation coefficient is important for management of lake water quality and ecological environments. Data were collected seven times (378 samples) from 2006 to 2011 in Lake Taihu to improve the existing semianalytical model of diffuse attenuation coefficient at 490 nm (Kd(490)) by measuring in situ Kd(490) and remote sensing reflectance (Rrs). This semianalytical model was applied to determine the long‐term variation of Kd(490) and its driving factors from Moderate Resolution Imaging Spectroradiometer (MODIS) images. Our semianalytical model of Kd(490) improves the retrieval accuracy of Kd(490) from 29.60% to 22.58% (root‐mean‐square percentage error) and 24.28 ± 18.54% (mean value ± standard deviation) to 19.29 ± 14.34% (mean absolute relative error). The Kd(490) from maps produced from MODIS images clearly present the temporal and spatial variations of Kd(490). This indicates the suitability of using the improved semianalytical model and MODIS to observe Kd(490) in turbid shallow inland waters. Long‐term MODIS observation of Kd(490) shows distinct seasonal and spatial variations. Temporally, the highest and the lowest Kd(490) were found in winter and in summer, respectively. Spatially, high Kd(490) values were primarily distributed in the southwestern and northwestern Lake Taihu, and relatively low Kd(490) values were distributed in Meiliang Bay and Gonghu Bay (the eastern part of the lake and East Bay were excluded in this study due to invalid retrieval results of Kd(490)). Many covarying points were found between Kd(490) and maximum wind speed from long‐term observations, suggesting that wind‐induced sediment resuspension is the principal driver of high Kd(490). Correlation was also observed between Kd(490) and heavy rainfall events, suggesting the regulating effect of heavy rainfall.

This paper presents a method of validating the MODIS (Moderate-Resolution Imaging Spectroradiometer) surface reflectance (MOD09) based on ground spectral measurements and high-resolution remote sensing images. Given that the spatial resolution of the MODIS image is too low to directly compare ground measurements with the MODIS pixels, registration is first made between MODIS and a high-resolution image. Afterwards, the same sizable sample area to one MODIS pixel is chosen in a high resolution image (a pixel in MODIS corresponds to many pixels in a high-resolution image), after which image classification is conducted in each sample area. Finally, reflectance is measured for each class in each area. Taking the ratio of every class area to each sample area as the weight, the weighted mean reflectance is then calculated for each sample area (a pixel for MODIS), which is treated as the measured reflectance of the corresponding pixel in the MODIS image. A contradistinctive analysis is carried out between measured reflectance and MODIS retrieval reflectance from the space in order to determine the accuracy of the MODIS land surface reflectance product and correct its error. The experiment indicates that a linear positive correlativity between the MODIS pixel retrieval reflectance from space and the measured (calculated) surface reflectance exists. From this, we can build an error correction model of the MODIS surface reflectance product by linear regression. The accuracy of the MODIS surface reflectance products after error correction is remarkably improved. Likewise, the relative error can be controlled by 10%, thereby satisfying the requirements of various remote sensing applications.

The 36 channel Moderate Resolution Imaging Spectroradiometer (MODIS) offers the opportunity for multispectral approaches to cloud detection. The MODIS cloud mask developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) uses several cloud detection tests to indicate a level of confidence that the MODIS is observing clear skies. The MODIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. The updated cloud mask has many improvements, such as improved cloud/surface discrimination over desert regions, sun glint processing and thin cirrus detection. For non-snow-covered land areas, a clear sky confidence of 0.96 (probably clear) will be assigned if thresholds are met for three tests: 3.9-11 μm and 3.75-3.9 μm brightness temperature differences and a 1.24/0.55 μm reflectance ratio test. Values of these must be 2.0, respectively. A change has been made to the NIR (band 2) reflectance test for sun glint processing. The updated method is to calculate a cloud threshold as a linear function of sun-glint angle in three separate ranges. A new clear-sky restoral test was added where the ratio of band 17/18 reflectance is utilized to discriminate between low clouds and water surfaces. The thin cirrus thresholds using corrected band 26 (1.38 μm) reflectances were also modified.

We used publicly available digital spatial datasets to study the area extents and their horizontal variations of two water bodies within the Danjiangkou Reservoir, China. Between 2003 and 2005, the water levels varied from 140 to 149 m above mean sea level as measured by the Geoscience Laser Altimeter System (GLAS). Detailed procedures to derive the horizontal extents and variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) coupled with GLAS data and to verify the extents and variations spatially were provided. For the water bodies on the north and west, the surface water extents derived from four MODIS images varied between 174 and 218 km2 and from 96 to 135 km2, respectively. The extents by inundating the DEM using the GLAS data were 178–212 km2 for the water body on the north and 104–118 km2 for the water body on the west. The spatial verifications of surface water extents derived from the MODIS images versus DEM coupled with GLAS data agreed 83–93%. Within the ring areas between water/land boundaries at elevations of 140 and 147 m, and 140 and 149 m, the spatial agreement was 52–75%.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is an important data set in global burned-area mapping. The MODIS global burned-area product has a coarse spatial resolution at approximately 500 m, which often introduces errors to the mapped burned areas. In this letter, a novel subpixel mapping (SPM) approach was proposed to produce burned-area maps at the fine spatial resolution similar to Landsat imagery, by exploring the spectral and spatial information provided by the second and fifth bands of MODIS. The proposed SPM approach aims to refine the estimate of burned areas, which have been detected by the MODIS global burned-area product. The performance of the proposed SPM approach was assessed with an experiment area containing six burned areas, by comparing with the MODIS burned-area product MCD45. The result shows that the average omission error decreased from 52.26% for MCD45 to 16.74% for SPM, and the average commission error decreased from 21.76% for MCD45 to 12.54% for SPM. The kappa value increased from 0.5583 for MCD45 to 0.8756 for SPM, indicating that the proposed SPM approach is effective in reducing the influence of the coarse spatial resolution of MODIS imagery in mapping a burned area and refining existing global burned-area products.

Vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI) are widely used for assessing vegetation cover and condition. One of the NDVI's significant disadvantages is its sensitivity to aerosols in the atmosphere, hence several atmospherically resistant VIs were formulated using the difference in the radiance between the blue and the red spectral bands. The state‐of‐the‐art atmospherically resistant VI, which is a standard Moderate Resolution Imaging Spectroradiometer (MODIS) product, together with the NDVI, is the Enhanced Vegetation Index (EVI). A different approach introduced the Aerosol‐free Vegetation Index (AFRI) that is based on the correlation between the shortwave infrared (SWIR) and the visible red bands. The AFRI main advantage is in penetrating an opaque atmosphere influenced by biomass burning smoke, without the need for explicit correction for the aerosol effect. The objective of this research was to compare the performance of these three VIs under smoke conditions. The AFRI was applied to the 2.1 µm SWIR channel of the MODIS sensor onboard the Earth Observing System (EOS) Terra and Aqua satellites in order to assess its functionality on these imaging platforms. The AFRI performance was compared with those of NDVI and EVI. All VIs were calculated on images with and without present smoke, using the surface‐reflectance MODIS product, for three case studies of fires in Arizona, California, and Zambia. The MODIS Fire Product was embedded on the images in order to identify the exact location of the active fires. Although good correlations were observed between all VIs in the absence of smoke (in the Arizona case R 2 = 0.86, 0.77, 0.88 for the NDVI–EVI, AFRI–EVI, and AFRI–NDVI, respectively) under smoke conditions a high correlation was maintained between the NDVI and the EVI, while low correlations were found for the AFRI–EVI and AFRI–NDVI (0.21 and 0.16, for the Arizona case, respectively). A time series of MODIS images recorded over Zambia during the summer of 2000 was tested and showed high NDVI fluctuations during the study period due to oscillations in aerosol optical thickness values despite application of aerosol corrections on the images. In contrast, the AFRI showed smoother variations and managed to better assess the vegetation condition. It is concluded that, beneath the biomass burning smoke, the AFRI is more effective than the EVI in observing the vegetation conditions.

This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS). A multi-index threshold endmember extraction method was applied to select the endmembers from MODIS images. To incorporate endmember variability, an endmember selection strategy, called the combined use of typical and neighboring endmembers, was adopted in multiple endmember spectral mixture analysis (MESMA), which can assure a robust subpixel water fractions estimation. The accuracy of the algorithm was assessed at both the local scale and regional scale. At the local scale, a comparison using the eight pairs of MODIS/Landsat 8 Operational Land Imager (OLI) water maps demonstrated that subpixels water fractions were well retrieved with a root mean square error (RMSE) of 7.86% and determination coefficient (R2) of 0.98. At the regional scale, the MODIS water fraction map in October 2014 matches well with the TP lake data set and the Global Lake and Wetland Database (GLWD) in both latitudinal and longitudinal distribution. The lake area estimation is more consistent with the reference TP lake data set (difference of −3.15%) than the MODIS Land Water Mask (MOD44W) (difference of −6.39%).

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.

Severe cloud contamination of MODIS Land Surface Temperatures over an Arctic ice cap, Svalbard

Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation

Techniques are presented for generating thermal infrared and visible composite images from cloud-free portions of MODIS (MODerate resolution Imaging Spectroradiometer) images closely spaced in time, with a focus on studies of landfast sea ice along the East Antarctic coast. Composite image inclusion criteria are based on modified MODIS EOS cloud mask product results. The compositing process places emphasis on retaining maximum spatial resolution while minimizing computing storage space requirements. Composite images can be produced either as a regular product (e.g., on a 10-day grid), or dynamically (whenever enough information is acquired to produce a new output image). The techniques presented are applicable at any latitude, are available for all MODIS channels at their native resolution, can combine Aqua and Terra images, and can produce maps in any output projection. However, due to the polar orbit of NASA's Terra and Aqua satellites which host the MODIS instrument, more frequent coverage is produced at higher latitudes. Thus, the techniques presented are particularly applicable to polar research. Examples of summertime (visible) and wintertime (IR) composite image generation of the landfast sea ice around the Mertz Glacier region, East Antarctica, are included.

Spatiotemporal data fusion is a key technique for generating unified time-series images from various satellite platforms to support the mapping and monitoring of vegetation. However, the high similarity in the reflectance spectrum of different vegetation types brings an enormous challenge in the similar pixel selection procedure of spatiotemporal data fusion, which may lead to considerable uncertainties in the fusion. Here, we propose an object-based spatiotemporal data-fusion framework to replace the original similar pixel selection procedure with an object-restricted method to address this issue. The proposed framework can be applied to any spatiotemporal data-fusion algorithm based on similar pixels. In this study, we modified 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 model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. The results show that the proposed object-based framework can improve all three data-fusion algorithms significantly by delineating vegetation boundaries more clearly, and the improvements on FSDAF is the greatest among all three algorithms, which has an average decrease of 2.8% in relative root-mean-square error (rRMSE) in all sensor combinations. Moreover, the improvement on fusing Sentinel 2 and Landsat 8 images is more significant (an average decrease of 2.5% in rRMSE). By using the fused images generated from the proposed object-based framework, we can improve the vegetation mapping result by significantly reducing the “pepper-salt” effect. We believe that the proposed object-based framework has great potential to be used in generating time-series high-resolution remote-sensing data for vegetation mapping applications.

An enhanced dust index (EDI) for Moderate Resolution Imaging Spectroradiometer (MODIS) solar reflectance bands is proposed that provides a means to detect the dust status of the atmosphere. The EDI utilizes only solar reflectance channels and may therefore be applied consistently to the entire MODIS time series records (1999 to present) for daytime dust observation, producing a higher spatial resolution (500 m) dust result than that from thermal-infrared records (1000 m), which were developed previously and are currently being used. The index introduces dust optical density (α), which can be simply estimated by spectral unmixing, into the normalized difference between reflectance at near-infrared (2.13 μm) and blue (0.469 μm). Dust severity can thus be rated from weak to severe within a standard range of –1 to 1. The index was applied to 11 typical dust events during 2000–2010 in East Asia, where it showed good coherence with meteorological station-observed visibility (R 2 = 0.7909, p < 0.05) and standardized visibility (R 2 = 0.7128, p < 0.05). Further comparison with the commonly used normalized difference dust index (NDDI) and brightness temperature difference (BTD) between MODIS bands 31 and 32 also indicated a better performance of the EDI in identifying the spatial and density distributions of dust. Previously applied satellite-based dust indices, particularly for the visible and near-infrared, can therefore be improved for a better quantification of dust aerosols.