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This study investigates the reconstruction of climatological patterns and vegetation dynamics in the Horn of Africa region using high temporal resolution (i.e. daily) Normalized Difference Vegetation Index (NDVI) datasets. The analysis compares a straight-forward processing approach to derive a daily vegetation index from a geostationary (SEVIRI) satellite with existing NDVI series from geostationary or polar-orbiting (MODIS, MetOp-AVHRR) satellites, highlighting the impact of cloud contamination on data quality in high temporal resolution datasets. Using a smoothing process designed to reconstruct the upper envelope of the vegetation status series, we obtained a daily vegetation dataset that effectively mitigated cloud-induced fluctuations, outperforming polar-orbiting (e.g. MODIS) satellite-derived dataset in capturing regional climatology. We demonstrated this through statistical analysis, including autocorrelation and mean absolute difference between consecutive observations. We showed that cloud contamination significantly affects high temporal resolution NDVI series, particularly in forest areas, which makes it difficult to identify a suitable dataset to validate our approach. Therefore, we mitigated this problem using a Maximum Value Compositing technique, designed to remove cloud-induced biases and further compared our results with another independent vegetation index at coarser temporal resolution derived from AVHRR. We found that our vegetation index closely relates with MODIS 10-day composites after removing cloud-contaminated pixels. Furthermore, the study evaluates the sensitivity of the selected NDVI datasets to drought events, demonstrating the strength of the proposed SEVIRI dataset in capturing the intensity and persistence of vegetation anomalies. In conclusion, the study presents an innovative strategy for deriving daily-resolution NDVI datasets in cloud-prone regions, validating it with independent datasets at different sub-monthly temporal scales.

Land surface temperature (LST) data in the thermal infrared (TIR) band measured by the moderate-resolution imaging spectroradiometer (MODIS) instrument are critical for studying surface urban heat islands (SUHIs); however, these acquired TIR LST data are contaminated by clouds, so it is crucial to develop a method to generate cloud-free LST products. In this article, employing Tianjin as the research area, we combined the weather research and forecasting model with a random forest and a spatial optimization algorithm to propose a cloud-free MODIS-like model (WRFFM). The model can reconstruct cloud-free MODIS-like LSTs and SUHIs are studied. The spatial patterns of the WRFFM LSTs and the MODIS LSTs are consistent; the correlation coefficients in July and December range from 0.8 to 0.91 and 0.8 to 0.93, respectively, and the root mean square errors range from 0.5 to 3.8 K and 0.4 to 1.8 K, respectively, indicating that the modeled results are accurate. We use these WRFFM LSTs to study SUHIs and evaluate the deviations between the MODIS SUHIs and WRFFM SUHIs. When the proportion of clear-sky pixels is below 30%, the deviation is above 3 K, and when the proportion of clear-sky pixels is above 80%, the deviation is below 0.6 K. The results indicate that the developed model can be applied to improve the study of SUHIs and that the number of clear-sky pixels for a city is an important factor that affects the bias relative to the actual SUHI .

Recent advances in satellite-borne optical sensors led to important developments in the monitoring of tropical ecosystems in Asia, which have been strongly affected by recent anthropogenic activities and climate change. Based on our feasibility analyses conducted in Indonesia in Sumatra and Sarawak, Malaysia in Borneo, we discuss the current situation, problems, recent improvements, and future tasks regarding plant phenology observations and land-cover and land-use detection. We found that the Multispectral Instrument (MSI) on board the Sentinel-2A/2B satellites with a 10-m spatial resolution and 5-day observational intervals could be used to monitor phenology among tree species. For the Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary satellite with a 1,000-m spatial resolution and 10-min observational intervals, we found that the time-series in vegetation indices without gaps due to cloud contamination may be used to accurately detect the timing and patterns of phenology among tree species, although the spatial resolution of the sensor requires further improvement. We also found and validated that text and pictures with geolocation information published on the Internet, and historical field notes could be used for ground-truthing land cover and land use in the past and present time. The future development of both high frequency (≤ 10 min) and high spatial resolution (≤ 10 m) optical sensors aboard satellites is expected to dramatically improve our understanding of ecosystems in the tropical Asia.

Spatio-temporal subpixel mapping (STSPM) has shown great potential for monitoring land surfaces, by generating land cover maps with both fine spatial and temporal resolutions. Selecting cloud-free fine spatial resolution images as ancillary data for STSPM can ensure that the temporal dependence term is measured for all subpixels, as in all current STSPM methods. However, such images are generally limited by cloud contamination, thereby resulting in great land cover changes between the available clear image and the desired fine spatial resolution land cover map. This research proposes a cloud-independent STSPM (C-STSPM) method to reconstruct the fine spatial resolution land cover maps by using cloudy images directly, which are assumed to have fewer land cover changes than temporally distant clear images. Cloud-independent spatio-temporal dependence was proposed in the presence of cloudy pixels. Experiments were performed under various cloud conditions involving 21 × 21 pairs of simulated cloudy images. The results demonstrate that by utilizing land cover information of clear pixels in cloudy images, more accurate prediction can be produced by C-STSPM compared to directly discarding those cloudy images, even if the number of cloud pixels increases to 95%. The advantage of C-STSPM is more evident when the clouds are distributed sparsely, which benefits from the increased number of clear pixels at the edge of the cloudy areas. Furthermore, a negative linear correlation was detected between the prediction accuracy and the ratio of overlapping cloudy pixels in the cloudy images. Moreover, the C-STSPM method helps to deal with abrupt changes occurred in the temporally distant cloud-free images by utilizing the temporally adjacent cloudy images with gradual land cover changes. Overall, the C-STSPM method provides a completely new solution to make fuller use of the widely existing cloudy images in multi-scale time-series images.

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Optical remote sensing imagery is commonly used to monitor the spatial and temporal distribution patterns of inland waters. Its usage, however, is limited by cloud contamination, which results in low-quality images or missing values. Selecting cloud-free scenes or combining multi-temporal images to produce a cloud-free composite image can partially overcome this problem at the cost of the monitoring frequency. Predicting the spectral values of cloudy areas based on the spectral characteristics is a possible solution; however, this is not appropriate for water because it changes rapidly. Reconstructing cloud-covered water areas using historical water-distribution data has good performance, but such methods are typically only suitable for lakes and reservoirs, not over vast and complex terrain. This paper proposes a category-based approach to reconstruct the water distribution in cloud-contaminated images using a spatiotemporal dependence model. The proposed method predicts the class label (water or land) of a cloudy pixel based on the neighboring pixel labels and those at the same position in images acquired on other dates according to historical spatiotemporal water-distribution data. The method was evaluated through eight experiments in different study regions using Landsat and Sentinel-2 images. The results demonstrated that the proposed method could yield high-quality cloud-free classification maps and provide good water-extraction accuracy and consistency in most hydrological conditions, with an overall accuracy of up to 98%. The accuracy and practicality of the method render it promising for applications across a wide range of future research and monitoring efforts.

Generating spatiotemporally continuous land surface temperature (LST) data is in great demand for hydrology, meteorology, ecology, environmental studies, etc. However, the thermal infrared (TIR)-based LST measurements are prone to cloud contamination with missing pixels. To repair the missing pixels, a new XGBoost-based linking approach for reconstructing daytime and nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements was introduced. The instantaneous solar radiation and two soil-related predictors from China Data Assimilation System (CLDAS) 0.0625°/1-h data were selected as the linking variables to depict the relationship with instantaneous MODIS LST data. Other land surface properties, including two vegetation indices, the water index, the surface albedo, and topographic parameters, were also used as the predictor variables. The XGBoost method was used to fit an LST linking model by the training datasets from clear-sky pixels and was then applied to the MODIS Aqua-Terra LSTs during summer time (June to August) in 2017 and 2018 across China. The recovered LST data was further rectified with the Savitzky–Golay (SG) filtering method. The results showed the distribution of the reconstructed LSTs present a reasonable pattern for different land-cover types and topography. The evaluation results using in situ longwave radiation measurements showed the RMSE varies from 3.91 K to 5.53 K for the cloud-free pixels and from 4.42 K to 4.97 K for the cloud-covered pixels. In addition, the reconstructed LST products correlated well with CLDAS LST data with similar LST spatial patterns. The variable importance analysis revealed that the two soil-related predictors and the elevation variable are key parameters due to their great contribution to the XGBoost model performance.

The optical remote sensing images not only have to make a fundamental tradeoff between the spatial and spectral resolutions, but also are inevitable to be polluted by the clouds; however, the existing pansharpening methods mainly focus on the resolution enhancement of the optical remote sensing images without cloud contamination. How to fuse the cloud-contaminated images to achieve the joint resolution enhancement and cloud removal is a promising and challenging work. In this paper, a pansharpening method for the challenging cloud-contaminated very high-resolution remote sensing images is proposed. Furthermore, the cloud-contaminated conditions for the practical observations with all the thick clouds, the thin clouds, the haze, and the cloud shadows are comprehensively considered. In the proposed methods, a two-step fusion framework based on multisource and multitemporal observations is presented: 1) the thin clouds, the haze, and the light cloud shadows are proposed to be first jointly removed and 2) a variational-based integrated fusion model is then proposed to achieve the joint resolution enhancement and missing information reconstruction for the thick clouds and dark cloud shadows. Through the proposed fusion method, a promising cloud-free fused image with both high spatial and high spectral resolutions can be obtained. To comprehensively test and verify the proposed method, the experiments were implemented based on both the cloud-free and cloud-contaminated images, and a number of different remote sensing satellites including the IKONOS, the QuickBird, the Jilin (JL)-1, and the Deimos-2 images were utilized. The experimental results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The combination of Landsat-8, Landsat-9, Sentinel-2A and Sentinel-2B data provides a new perspective in remote sensing application for terrestrial monitoring. Jointly, these four sensors together offer global 10–30-m multi-spectral data coverage at a higher temporal revisit frequency. In this study, combinations of four sensors were used to examine the revisit interval by modelled orbit swath information. To investigate different factors that could influence data availability, an analysis was carried out for one year based on daytime surface observations of Landsat-8 and Sentinel-2A -2B. We found that (i) the global median average of revisit intervals for the combination of four sensors was 2.3 days; (ii) the global mean average number of surface observations was 141.4 for the combination of Landsat-8 and Sentinel-2A -2B; (iii) the global mean average cloud-weighted number of observations for the three sensors combined was 81.9. Three different locations were selected to compare with the cloud-weighted number of observations, and the results show an appropriate accuracy. The utility of combining four sensors together and the implication for terrestrial monitoring are discussed.

Land surface temperature (LST) is a key parameter in the physics of land surface processes. Currently, the technique most commonly used to obtain LST is thermal infrared (TIR) remote sensing (RS). However, this technique is affected by clouds and cannot obtain complete spatiotemporal LST images. To solve this problem, RS and weather research and forecasting (WRF) coupled model (RS-WRF coupled model) was developed to produce cloud-free MODIS-like LST data. (i) The WRF model is used to simulate the cloud-free LST with a 1 km resolution. (ii) The optimal machine learning model is utilized to fit the simulated LSTs and produce cloud-free MODIS-like LSTs. (iii) Combined with a median filtering algorithm, the salt and pepper noise in the fitted image is optimized. Taking Beijing as a test site. Under relatively little cloud cover and greater cloud contamination, the root mean square error of the LST constructed by the RS-WRF coupled model is approximately 1.2 and 1.8 K, respectively. The correlation coefficients under both conditions exceed 0.9. Overall, the RS-WRF coupled model can provide cloud-free time series MODIS-like LST images in areas with frequent cloud cover, thereby compensating for the disadvantage that satellite TIR images contaminated by clouds cannot obtain complete LST estimates. • A new method is developed to construct cloud-free MODIS-like LSTs. • The optimal WRF scheme is used to simulate the LST. • The optimal machine learning model and median filter are used to improve the accuracy of LST construction. • The errors in the LSTs constructed under little/considerable cloud contamination are around 1.2 K and 1.8 K, respectively. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a biophysical variable related to atmosphere-biosphere exchange of CO2. One way to obtain LAI value is by the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products. In this paper, we use this product to improve the physiological principles predicting growth model within a Gaussian Bayesian network (GBN) setup. The MODIS time series, however, contains gaps caused by persistent clouds, cloud contamination, and other technique problems. We used the Expectation Maximization (EM) algorithm to estimate these missing values. During a period of 26 successive months, the EM algorithm is applied to four different cases: successively and not successively missing values during two different winter seasons, successively and not successively missing values during one spring season, and not successively missing values during the full study. Results show that the maximum value of the averaged absolute error between the original values and those estimated equals 0.16. This low value indicates that the estimated values well represent the original values. Moreover, the root mean square error of the GBN output reduces from 1.57 to 1.49 when performing the EM algorithm to estimate the not successively missing values. We conclude that the EM algorithm within a GBN can adequately handle missing MODIS LAI values and improves the estimation of the LAI. [ABSTRACT FROM AUTHOR]

Generating spatiotemporally continuous land surface temperature (LST) data is in great demand for hydrology, meteorology, ecology, environmental studies, etc. However, the thermal infrared (TIR)-based LST measurements are prone to cloud contamination with missing pixels. To repair the missing pixels, a new XGBoost-based linking approach for reconstructing daytime and nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements was introduced. The instantaneous solar radiation and two soil-related predictors from China Data Assimilation System (CLDAS) 0.0625°/1-h data were selected as the linking variables to depict the relationship with instantaneous MODIS LST data. Other land surface properties, including two vegetation indices, the water index, the surface albedo, and topographic parameters, were also used as the predictor variables. The XGBoost method was used to fit an LST linking model by the training datasets from clear-sky pixels and was then applied to the MODIS Aqua-Terra LSTs during summer time (June to August) in 2017 and 2018 across China. The recovered LST data was further rectified with the Savitzky–Golay (SG) filtering method. The results showed the distribution of the reconstructed LSTs present a reasonable pattern for different land-cover types and topography. The evaluation results using in situ longwave radiation measurements showed the RMSE varies from 3.91 K to 5.53 K for the cloud-free pixels and from 4.42 K to 4.97 K for the cloud-covered pixels. In addition, the reconstructed LST products correlated well with CLDAS LST data with similar LST spatial patterns. The variable importance analysis revealed that the two soil-related predictors and the elevation variable are key parameters due to their great contribution to the XGBoost model performance. [ABSTRACT FROM AUTHOR]

The combination of Landsat-8, Landsat-9, Sentinel-2A and Sentinel-2B data provides a new perspective in remote sensing application for terrestrial monitoring. Jointly, these four sensors together offer global 10–30-m multi-spectral data coverage at a higher temporal revisit frequency. In this study, combinations of four sensors were used to examine the revisit interval by modelled orbit swath information. To investigate different factors that could influence data availability, an analysis was carried out for one year based on daytime surface observations of Landsat-8 and Sentinel-2A -2B. We found that (i) the global median average of revisit intervals for the combination of four sensors was 2.3 days; (ii) the global mean average number of surface observations was 141.4 for the combination of Landsat-8 and Sentinel-2A -2B; (iii) the global mean average cloud-weighted number of observations for the three sensors combined was 81.9. Three different locations were selected to compare with the cloud-weighted number of observations, and the results show an appropriate accuracy. The utility of combining four sensors together and the implication for terrestrial monitoring are discussed. [ABSTRACT FROM AUTHOR]