Revisit Frequency Research Articles

Impacts of Satellite Revisit Frequency on Spring Phenology Monitoring of Deciduous Broad-Leaved Forests Based on Vegetation Index Time Series

Satellites have different revisit frequencies (i.e., temporal resolutions), ranging from daily to monthly. The satellite revisit frequencies suitable for accurately monitoring the phenology of deciduous broad-leaved forests (DBF) are not well-known. To fill this knowledge gap, this study used MODIS Daily Nadir BRDF-Adjusted images to simulate EVI time series with a wide range of temporal resolutions from daily to 52 days, to investigate the impacts of satellite revisit frequency on monitoring spatial and temporal patterns of spring phenology, i.e., the start of season (SOS), of DBF in North America. Then, these EVI time series were used to extract SOS by two common phenology extraction methods (i.e., relative threshold and curvature methods). Our results reveal that 1 ) low temporal resolutions cannot accurately reconstruct real vegetation growth profile, which generally causes a false early SOS detection, 2 ) the impact of temporal resolutions is nonlinear. The accuracy of SOS detection from data with relatively high frequencies (e.g., 7 days) is only slightly lower than that from daily time series but the accuracy decreases largely with low frequencies, and 3 ) validation with ground observations from PhenoCam Network stations and an experiment using three real satellite datasets (i.e., MODIS, Landsat 8, and Sentinel-2) confirm the findings from our simulation study. This study suggests that satellites with medium temporal resolutions, such as Sentinel-2 and Landsat 8, could extract reliable phenology metrics in non-cloudy regions.

Quality analysis of water level series obtained by altimetric radar satellite along the São Francisco River

ABSTRACT Radar altimeters are instruments carried on space missions and allow for determination of heights, particularly in oceans and ice sheets. The use of altimetry data on continental waters involves several challenges, such as the revisit frequency (typically 27 to 35 days), an accuracy of decimeters, data handling and processing, particularly for narrow rivers such as the São Francisco River (width<1km). Radar satellite altimetry has advantages over the conventional in situ monitoring network, including in terms of spatial coverage and global altimetric reference of data. Thus, altimetry data should be used as a complementary and/or alternative source to in situ data. In this context, this study consolidates and evaluates the altimetric series of five different altimetry missions: Envisat in two orbits, Saral, Sentinel 3-A, and Sentinel 3-B. The altimetry water level time series of 17 Virtual stations were compared with leveled gauging stations series to calculate absolute and relative errors. Ultimately, the errors varied from 0.13 m to 0.36 m in the best cases (41%), in line with recent literature. Sentinel-3 satellites showed the best RMSE absolute/relative results: 0.95/0.49 m (S-3A) and 0.96/0.52 m (S-3B). The second best RMSEs was Envisat-X (1.39/0.50 m), then Envisat (1.87/0.56 m) and Saral (1.74/0.60 m).

Very High Spatial Resolution Downscaled SMAP Radiometer Soil Moisture in the CONUS Using VIIRS/MODIS Data

Satellite remote sensing has been providing passive microwave soil moisture (SM) retrievals of a global spatial coverage and a high revisit frequency for research and applications in earth and environmental sciences, specifically after the soil moisture active and passive (SMAP) was launched in 2015. But, the spatial resolution of SM data is restricted to tens of kilometers, which is insufficient for regional or watershed scale studies. In this article, an SM downscaling algorithm was developed based on the vegetation modulated apparent thermal inertia relationship between SM and changes in land surface temperature (LST). The algorithm used data sets from the North America Land Data Assimilation System Noah model outputs and the advanced very high resolution radiometer data of the long term data record from 1981 to 2018. Here, the downscaling model was applied to visible/infred LST data from the visible infrared imaging radiometer suite at 400-m and the moderate resolution imaging spectroradiometer at 1 km to downscale the L2 radiometer half-orbit 9 km SMAP SM from 2018 to 2019 for the contiguous United States. The 400-m/1-km downscaled SM products were validated using 125 in situ SM ground measurements acquired from the International Soil Moisture Network. The validation results summarized by SM network show that the overall unbiased RMSE for 400-m of the improved/original downscaling algorithms and 1-km SM outperform 9-km SM by 0.01, 0.007, and 0.012 m3/m3 volumetric soil moisture, respectively, which indicates a fairly good performance of the downscaling algorithm. It is also found that precipitation has an impact on the 9-km SMAP SM.

One‐dimensional maximum‐likelihood estimation for spaceborne precipitation radar data assimilation

AbstractSpaceborne precipitation radar such as Global Precipitation Measurement (GPM)/dual‐frequency precipitation radar (DPR) provides valuable observations of precipitation systems in three dimensions. Assimilation of GPM/DPR data is becoming an important technique for improving the accuracy of forecasting to complement scarce ground‐based observations. This study presents a new, one‐dimensional maximum‐likelihood estimation (1D‐MLE) method developed by the authors that enables the estimation of relative humidity profiles according to a non‐Gaussian probability density function. By assimilating the estimated relative humidity profiles using a four‐dimensional variational (4D‐Var) method, mesoscale precipitation forecasts by the Japan Meteorological Agency (JMA) have been considerably improved. The displacement of forecast precipitation during a severe weather event, in particular, is improved significantly. It was found that forecasting accuracy was maintained for a narrow GPM/DPR swath and low revisit frequency by repeating the assimilation–forecast cycle. Since the effectiveness was confirmed, the JMA began assimilating GPM/DPR data using the 1D‐MLE approach from March 2016.

Global Revisit Interval Analysis of Landsat-8 -9 and Sentinel-2A -2B Data for Terrestrial Monitoring.

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.

Impact of the number of dates and their sampling on a NDVI time series reconstruction methodology to monitor urban trees with Ven[formula omitted]s satellite

This article studies the influence of the number of satellite remote sensing acquisition dates and their sampling on the performance of a time series reconstruction method developed in Granero-Belinchon et al. 2020. This method initially aimed at monitoring urban London plane (Platanus x acerifolia) trees, and was tested with Sentinel-2 imagery at spatial resolutions of 10 and 20 m and a temporal revisit of 5 days. Due to its higher revisit frequency of 2 days while having a similar spatial resolution of 10 m, Venμs imagery was consequently used in the present article to fulfill with the purpose of this study. The strategy relies on the building of different acquisition date configurations based on the Venμs time series by considering uniform and non-uniform samplings and with a total number of acquisitions ranging from 45 to 14. Thus, the aim of the article is to examine the number of annual acquisitions needed to describe properly a vegetation phenological cycle and the impact of the annual sampling of these acquisitions on the final reconstructed time series. To this end, this study was carried out by using the widely used Normalized Difference Vegetation Index (NDVI). Results showed that on one hand, applied on an acquisition configuration composed of at least 18 uniformly sampled dates throughout the year, this reconstruction methodology is able to describe correctly the annual NDVI dynamics but leads to inaccuracies in the description of intra-annual ones. Nevertheless, these intra-annual descriptions are improved with the increase of the number of acquisitions. On the other hand, strongly non-uniform acquisition date samplings lead to inaccurate descriptions of the undersampled time periods but correct descriptions of the rest of the time series curve. The study case is London planes located in Toulouse (France) with 45 cloud-free Venμs images during the year 2019. Finally, this work emphasizes the main limitations of the studied reconstruction methodology when few acquisitions or very non-uniform acquisition date samplings are available and thus the identification of borderline cases in future applications and other study cases.

Sentinel-2 remote sensing of Zostera noltei-dominated intertidal seagrass meadows

Accurate habitat mapping methods are urgently required for the monitoring, conservation, and management of blue carbon ecosystems and their associated services. This study focuses on exposed intertidal seagrass meadows, which play a major role in the functioning of nearshore ecosystems. Using Sentinel-2 (S2) data, we demonstrate that satellite remote sensing can be used to map seagrass percent cover (SPC) and leaf biomass (SB), and to characterize its seasonal dynamics. In situ radiometric and biological data were acquired from three intertidal meadows of Zostera noltei along the European Atlantic coast in the summers of 2018 and 2019. This information allowed algorithms to estimate SPC and SB from a vegetation index to be developed and assessed. Importantly, a single SPC algorithm could consistently be used to study Z. noltei-dominated meadows at several sites along the European Atlantic coast. To analyze the seagrass seasonal cycle and to select images corresponding to its maximal development, a two-year S2 dataset was acquired for a French study site in Bourgneuf Bay. The potential of S2 to characterize the Z. noltei seasonal cycle was demonstrated for exposed intertidal meadows. The SPC map that best represented seagrass growth annual maximum was validated using in situ measurements, resulting in a root mean square difference of 14%. The SPC and SB maps displayed a patchy distribution, influenced by emersion time, mudflat topology, and seagrass growth pattern. The ability of S2 to measure the surface area of different classes of seagrass cover was investigated, and surface metrics based on seagrass areas with SPC ≥ 50% and SPC ≥ 80% were computed to estimate the interannual variation in the areal extent of the meadow. Due to the high spatial resolution (pixel size of 10 m), frequent revisit time (≤ 5 days), and long-term objective of the S2 mission, S2-derived seagrass time-series are expected to contribute to current coastal ecosystem management, such as the European Water Framework Directive, but to also guide future adaptation plans to face global change in coastal areas. Finally, recommendations for future intertidal seagrass studies are proposed.

Sentinel-1 TOPS co-registration over low-coherence areas and its application to velocity estimation using the all pairs shortest path algorithm

The C-band Sentinel-1 A/B satellites in TOPS mode provide unprecedented opportunities for continuous radar mapping of the earth with enhanced revisit frequency. The reliability for routine operational services relies on a very stringent azimuth co-registration accuracy. However, while the enhanced spectral diversity (ESD) technique achieves a co-registration accuracy of better than 0.001 pixels, the accuracy might still be degraded over low-coherence areas due to fast decorrelation. When TOPS time series data are available, the abrupt loss of coherence further affects the estimation accuracy of the network ESD. I integrate a double sample over the burst overlap region with an ESD estimator to reduce the ESD phase error and simultaneously mitigate the abrupt loss of coherence in the time series using the all pairs shortest path algorithm. I further use this algorithm to create a spatio-temporal network of co-registered Sentinel-1 SAR stacks for line-of-sight (LOS) velocity estimation over low-coherence areas. I perform InSAR time series analysis and evaluate the effectiveness of the proposed method using data acquired from descending tracks over representative low-coherence regions in south-western China. For those images suffering from significant low coherence, this method can achieve a better co-registration accuracy than those obtained from the previous network ESD method. By cross-validating descending COSMO-SkyMed data, a 39% uncertainty reduction in the estimated LOS velocity can be achieved when applying a co-registered Sentinel-1 stack with high accuracy and spatio-temporal network evolution.

Focusing Spotlight-Mode Bistatic GEO SAR With a Stationary Receiver Using Time-Doppler Resampling

Bistatic geosynchronous synthetic aperture radar (GEO SAR) system, with a geosynchronous illuminator and a stationary ground receiver, provides capability and flexibility of continuous observation and frequent revisit over certain area, which is of great significance for applications such as disaster monitoring. The Doppler frequency modulation (FM) rate in this configuration, however, varies along the azimuth direction, which increases difficulty in imaging. To equalize the Doppler FM rate, a new imaging method is proposed in this paper. By conducting time-Doppler resampling, the proposed method can remove the quadratic variance of Doppler FM rate at orbital positions where the Doppler FM rate is around zero. Validation results show that the proposed algorithm provides a better focusing quality compared with existing methods.

Displacements Monitoring over Czechia by IT4S1 System for Automatised Interferometric Measurements Using Sentinel-1 Data

The Sentinel-1 satellite system continuously observes European countries at a relatively high revisit frequency of six days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for interferometric (InSAR) processing is an original data provided in a Single Look Complex (SLC) level. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nationwide Sentinel-1 data stored in burst units that have been pre-processed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g., Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nationwide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provides a signal for early warning against a potential dangerous displacement, e.g., a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nationwide PS processing using data over Czechia was performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.

STOCKPILE MONITORING USING LINEAR SHAPE-FROM-SHADING ON PLANETSCOPE IMAGERY

Abstract. The storage and management of stockpiles of materials is a fundamental process in large scale activities such as mining, civil engineering, and in the management of waste landfill sites. Following the evolution of stocks has always been important, and advancements in remote sensing technology are not only facilitating this, but also making it possible in near real-time. Nowadays, this monitoring appears to be performed almost exclusively using UAV based techniques. This paper proposes to apply a simple Shape-from-Shading method on low resolution satellite images provided by PlanetScope to monitor the evolution of the volume of stockpiles. The proposed Shape-from-Shading formulation makes it possible to handle occluding objects in the scene. The loss in accuracy due to the low resolution of PlanetScope images is well compensated by the daily revisit frequency and by the fact that spaceborne acquisitions require no human supervision. With satellites it is also easy to follow simultaneously several stocking sites all over the world. We test our method on two coal storage sites and demonstrate that the stockpiles are well detected.

Snow Cover Estimation Underneath the Clouds Based on Multitemporal Correlation Analysis in Historical Time-Series Imagery

The estimation of a snow-covered area (SCA) is often achieved by classification of imagery acquired by passive optical sensors aboard satellite platforms with high revisit frequencies [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)] required by various applications. The extraction of the SCA from optical imagery is inevitably hindered by the presence of clouds, where the surface labels (i.e., snow and no-snow) remain unknown. In the bulk of existing research, cloud pixels are either masked out without any further processing or assigned to snow/no-snow classes by performing spatial or temporal filters. The current approaches to deal with the cloud-obscuration problem are subjected to sizable uncertainties. They mostly neglect or only partially account for the temporal correlation, which undermines the full potential of long time series. We propose a novel method for estimating snow/no-snow labels beneath the clouds that leverages the multitemporal correlation between the presence/absence of snow and environmental factors including the topographical elevation, the date of acquisition (and thus the season), and the cloud-obscuration duration. The proposed method is built upon analyzing the long time series of maps derived from the single date classification of images in order to estimate the conditional probabilities of transition between the snow and no-snow classes. The probabilities are estimated as a function of the aforementioned environmental and multitemporal factors, which allow for the prediction of labels beneath the clouds in either archive or new acquisitions. Validation results on a four-year time series of daily MODIS images acquired over the Euregio region in Italian and Austrian Alps prove the effectiveness and robustness of the proposed method in assigning labels beneath the clouds.

An analysis of first-time and repeat-visitor destination images through the prism of the three-factor theory of consumer satisfaction

The study examines first time and repeat visitor primary destination images through the prism of the factor theory of consumer satisfaction to expose the positive-negative asymmetry of their relationship with overall visitor satisfaction and word-of-mouth recommendation, and the differential impact of factor type on first time visitors and repeaters. It also subdivides repeat visitors on the basis of revisit frequency to reveal significant differences between repeater subgroups and between first time visitors and high frequency repeaters, which have been overlooked in previous research. Combining this theoretical approach with the examination of repeater heterogeneity has revealed the criticality of the destination's basic and performance factors; the former are potentially critical constraints on re-visitation for the large majority of visitors, while many of the latter have a positive impact on satisfaction and visitor recommendation but also have a disproportionately greater negative impact when their performance is poor. Recommendations are made for the management and marketing of the destination's image attributes in line with first time visitor and repeater requirements.

Unplanned return to operation room (OR) following growing spinal constructs (GSCs) in early onset scoliosis (EOS)-a multi-centric study

To evaluate the incidence and risk factors associated with the unplanned return to OR in EOS. Medical records of 51 patients of EOS operated at three different centres using various types of GSCs were evaluated for complications requiring unplanned surgeries. Data were analysed to find out rate of unplanned surgeries in relation to the aetiology, age and Cobb angle at index surgery, type of implant, cause of unplanned surgery, and management required. Out of 51 patients, three did not meet inclusion criteria. Forty-eight patients of EOS operated by GSCs with a mean age of 6.7years (range 2-12years) with an average follow-up of 67.3months were studied. There were 30 congenital, 10 idiopathic, 4 syndromic, and 4 neuromuscular cases. Thirty-nine out of 48 patients had one or more unplanned surgeries on follow-up (81.25%). Out of total 248 surgeries following index procedure, 82 were unplanned surgeries (33.06%), including 53 implant revisions, 12 implant-removal, 14 debridement, and 2 flaps. The common complications were 24.14% rod/screw breakage, 42.53% anchor pull-out, 16.09% infections, 6.90% wound dehiscence, and 4.6% neuro deficits. Unplanned surgeries were significantly higher in syndromic (58.8%) and neuromuscular (52.9%) than congenital (27.2%) and idiopathic (37.8%) cases (p < 0.05). Age at index procedure < 5years had higher unplanned surgeries than age > 5years (2.5 and 1.23 per patient, respectively, p< 0.05). Type of implant and initial Cobb angle did not significantly affect the rate of unplanned surgeries (p > 0.05) CONCLUSION: GSCs in EOS require a frequent revisit to operation room which should be well understood by the surgeon and parents.

Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data

Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new vegetation index from dual-pol (DpRVI) SAR data. We assess the utility of this index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.

Snow Moving to Higher Elevations: Analyzing Three Decades of Snowline Dynamics in the Alps

AbstractIn the Alps, snow cover dynamics can be monitored using Earth observation (EO). However, low revisit frequency and cloud cover pose a challenge to long‐term time series analysis using high spatial resolution EO images. In this study, we applied the random forest regression to model regional snowline elevations (RSEs). In this manner, daily snowline dynamics and their long‐term trends can be derived, despite the aforementioned challenges. Of the six investigated Alpine catchments between 1984 and 2018, a significant increasing trend of RSEs is shown in four catchments in the early ablation seasons (between 5.38 ± 2.64 and 11.29 ± 4.79 m·a−1) and five catchments in the middle ablation seasons (between 4.17 ± 2.62 and 8.76 ± 4.42 m·a−1). On average, the random forest regression models can explain 75% of the RSE variations. Furthermore, air temperature was found influential in snow persistence especially during middle and late ablation seasons.

Impact of migration from an illicit drug scene on hospital outcomes among people who use illicit drugs in Vancouver, Canada.

People who use illicit drugs (PWUD) are vulnerable to an array of negative health outcomes, and increased hospital services utilisation. PWUD are also a transient population which poses challenges to the provision of optimal health care. The objective of this study was to identify out-migration patterns from Vancouver's Downtown Eastside (DTES), a neighbourhood where services for PWUD are concentrated, and to estimate the impact of these patterns on hospitalisation events among PWUD. Data were collected through three prospective cohorts of PWUD in Vancouver, which were linked with health administrative data. Latent class growth analysis was used to define migration trajectory groups. Poisson regression was used to estimate the effect of migration patterns on hospitalisation events. A total of 1180 participants were included in the study. Four latent classes were identified: early migration out (243, 20.6%); frequent revisit (112, 9.5%); late migration out (219, 18.6%); and consistently living in the DTES (606, 51.4%). Compared with those who consistently lived in the DTES, participants in the early migration out group had lower hospitalisation events (adjusted rate ratio = 0.65; 95% confidence interval: 0.48-0.90). We found that PWUD who migrated out of the DTES early had lower hospitalisation events compared to those who consistently lived in the DTES, which may be a function of lesser addiction severity among this trajectory group. These findings underscore a need to provide transitional health and social service supports for other trajectory groups in an effort to minimise hospitalisation for preventable causes.

A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global SM index monitor based on integrated SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) remote sensing data. We make use of the short period (2015–2018) of SMAP datasets in combination with two approaches—Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process—and integrate them with SMOS data in a way that SMOS data is consistent with SMAP. The integrated SMOS and SMAP (SMOS/SMAP) has an increased global revisit frequency and a period of record from 2010 to the present. A four-parameter Beta distribution was fitted to the SMOS/SMAP dataset for each calendar month of each grid cell at ~36 km resolution for the period from 2010 to 2018. We used an asymptotic method that guarantees the values of the bounding parameters of the Beta distribution will envelop both the smallest and largest observed values. The Kolmogorov-Smirnov (KS) test showed that more grids globally will pass if the integrated dataset is from the Bayesian conditional approach. A daily global SM index map is generated and posted online based on translating each grid's integrated SM value for that day to a corresponding probability percentile relevant to the particular calendar month from 2010 to 2018. For validation, we use the Canadian Prairies Ecozone (CPE). We compare the integrated SM with the SMAP core validation and RISMA sites from ISMN, compare our indices with other models (VIC, ESA's CCI SM v04.4 integrated satellite data, and SPI-1), and make a two-by-two comparison of candidate indices using heat maps and summary CDF statistics. Furthermore, we visually compare our global SM-based index maps with those produced by other organizations. Our Global SM Index Monitor (GSMIM) performed, in many tests, similarly to the CCI's product SM index but with the advantage of being a near-real-time tool, which has applications for identifying evolving drought for food security conditions, insurance, policymaking, and crop planning especially for the remote parts of the globe.

Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data.

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.

CorPhU: an algorithm based on phase closure for the correction of unwrapping errors in SAR interferometry

SUMMARY Interferometric Synthetic Aperture Radar (InSAR) is commonly used in Earth Sciences to study surface displacements or construct high resolution topographic maps. Recent satellites such as those of the Sentinel-1 constellation allow to derive dense deformation maps with millimetric precision with high revisit frequency. However, InSAR is still limited by interferometric coherence. Interferometric phase noise resulting from a loss of coherence, due to changes in scattering properties between repeated SAR acquisitions, may lead to unwrapping errors, which then in turn lead to centimetric errors in time-series reconstruction. We present an algorithm based on interferometric phase closure to automatically correct unwrapping errors. We describe the algorithm and highlight its performances with two case studies, in Lebanon with Envisat satellite data and in Central Turkey with Sentinel-1 data. The first data set is particularly affected by unwrapping errors because of long spatial (500 m) and temporal baseline interferograms (6 yr) and decorrelation due, in particular, to vegetation. The second data set contains unwrapping errors because of temporal changes in the scattering properties of the ground. For these two examples, the algorithm allows the correction of almost all detectable unwrapping errors, without requiring visual inspection or manual deletions. Our algorithm is efficient especially on large data sets, such as with Sentinel-1 constellation, where interferometric phase is redundant and improves eventually the reconstruction of time-series.