Atmospheric drying as the main driver of dramatic glacier wastage in the southern Indian Ocean

This study was funded by IPEV-1048 GLACIOCLIM-KESAACO and LEFE-INSU KCRuMBLE programs, and the Agence Nationale de la Recherche through contract ANR-14-CE01-0001-01 (ASUMA).The ongoing retreat of glaciers at southern sub-polar latitudes is particularly rapid and widespread. Akin to northern sub-polar latitudes, this retreat is generally assumed to be linked to warming. However, no long-term and well-constrained glacier modeling has ever been performed to confirm this hypothesis. Here, we model the Cook Ice Cap mass balance on the Kerguelen Islands (Southern Indian Ocean, 49°S) since the 1850s. We show that glacier wastage during the 2000s in the Kerguelen was among the most dramatic on Earth. We attribute 77% of the increasingly negative mass balance since the 1960s to atmospheric drying associated with a poleward shift of the mid-latitude storm track. Because precipitation modeling is very challenging for the current generation of climate models over the study area, models incorrectly simulate the climate drivers behind the recent glacier wastage in the Kerguelen. This suggests that future glacier wastage projections should be considered cautiously where changes in atmospheric circulation are expected.Publisher PDFPeer reviewe

A review of spatial downscaling of satellite remotely sensed soil moisture

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed

Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters

The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 minute temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3 N, 26 W; 28 N, 26 E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05 degree resolution disaggregated SMOS SM product at sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105x105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product was found to be highly sensitive to algorithm input parameters; especially of conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patters of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July-September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa

Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle

Characterizing the land surface temperature (LST) and its diurnal cycle is important in understanding a range of surface properties, including soil moisture status, evaporative response, vegetation stress and ground heat flux. While remote-sensing platforms present a number of options to retrieve this variable, there are inevitable compromises between the resolvable spatial and temporal resolution. For instance, the spatial resolution of geostationary satellites, which can provide sub-hourly LST, is often too coarse (3 km) for many applications. On the other hand, higher-resolution polar orbiting satellites are generally infrequent in time, with return intervals on the order of weeks, limiting their capacity to capture surface dynamics. With recent developments in the application of unmanned aerial vehicles (UAVs), there is now the opportunity to collect LST measurements on demand and at ultra-high spatial resolution. Here, we detail the collection and analysis of a UAV-based LST dataset, with the purpose of examining the diurnal surface temperature response: something that has not been possible from traditional satellite platforms at these scales. Two separate campaigns were conducted over a bare desert surface in combination with either Rhodes grass or a recently harvested maize field. In both cases, thermal imagery was collected between 0800 and 1700 local solar time. The UAV-based diurnal cycle was consistent with ground-based measurements, with a mean correlation coefficient and root mean square error (RMSE) of 0.99 and 0.68 °C, respectively. LST retrieved over the grass surface presented the best results, with an RMSE of 0.45 °C compared to 0.67 °C for the single desert site and 1.28 °C for the recently harvested maize surface. Even considering the orders of magnitude difference in scale, an exploratory analysis comparing retrievals of the UAV-based diurnal cycle with METEOSAT geostationary data yielded pleasing results (R = 0.98; RMSE = 1.23 °C). Overall, our analysis revealed a diurnal range over the desert and maize surfaces of ~20 °C and ~17 °C respectively, while the grass showed a reduced amplitude of ~12 °C. Considerable heterogeneity was observed over the grass surface at the peak of the diurnal cycle, which was likely indicative of the varying crop water status. To our knowledge, this study presents the first spatially varying analysis of the diurnal LST captured at ultra-high resolution, from any remote platform. Our findings highlight the considerable potential to utilize UAV-based retrievals to enhance investigations across multi-disciplinary studies in agriculture, hydrology and land-atmosphere investigations

Suivi des ressources en eau par une approche combinant la télédétection multi-capteur et la modélisation phénoménologique

This thesis aims to improve the spatio-temporal resolution of surface water fluxes at the land surface-atmosphere interface based on appropriate models that rely on readily available multi-sensor remote sensing data. This work has been set up to further develop (disaggregation, assimilation, energy balance modeling) approaches related to soil moisture monitoring in order to optimize water management over semi-arid areas. Currently, the near surface soil moisture data sets available at global scale have a spatial resolution that is too coarse for hydrological applications. Especially, the near surface soil moisture retrieved from passive microwave observations such as AMSR-E (Advanced Microwave Scanning Radiometer-EOS) and SMOS (Soil Moisture and Ocean Salinity) data have a spatial resolution of about 60 km and 40 km, respectively. In this context, the downscaling algorithm "DISaggregation based on Physical And Theoretical scale Change" (or DisPATCh) has been developed. The near surface soil moisture variability is estimate within a low resolution pixel at the targeted 1 km resolution based on an evapotranspiration model using LST (Land surface temperature) and NDVI (vegetation index) derived from MODIS (MODerate resolution Imaging Spectroradiometer) data. Within a first step, DisPATCh is applied to SMOS and AMSR-E soil moisture products over the Murrumbidgee river catchment in Southeastern Australia and is evaluated during a one-year period. It is found that the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. However, the temporal resolution of DisPATCh data is limited by the gaps in MODIS images due to cloud cover, and by the temporal resolution of passive microwave observations (global coverage every 3 days for SMOS). The second step proposes an approach to overcome these limitations by assimilating the 1 km resolution DisPATCh data into a simple dynamic soil model forced by reanalysis meteorological data including precipitation. The original approach combines a variational scheme for root-zone soil moisture analysis and a sequential approach for the update of surface soil moisture. The performance is assessed using ground measurements of soil moisture in the Tensift-Haouz region in Morocco and the Yanco area in Australia during 2014. It is found that the downscaling/assimilation scheme is an efficient approach to estimate the dynamics of the 1 km resolution surface soil moisture at daily time scale, even when coarse scale and inaccurate meteorological data including rainfall are used. The third step presents a physically-based method to correct LST data for topographic effects in order to offer the opportunity for applying DisPATCh over mountainous areas. The approach is tested using ASTER (Advanced Spaceborne Thermal Emission Reflection Radiometer) and Landsat data over a 6 km by 6 km steep-sided area in the Moroccan Atlas. It is found that the strong correlations between LST and illumination over rugged terrain before correction are greatly reduced at ~100 m resolution after the topographic correction. Such a correction method could potentially be used as a proxy of the surface water status over mountainous terrain. This thesis opens the path for developing new remote sensing-based methods in order to retrieve water inputs -including both precipitation and irrigation- at high spatial resolution for water management.Ces travaux ont pour objectif général d'améliorer la représentation spatio-temporelle des processus hydrologiques de surface à partir de modèles dont la complexité est adaptée aux informations disponibles par la télédétection multi-capteur/multi-résolution. Nous avons poursuivi des développements méthodologiques (désagrégation, assimilation, modélisation du bilan d'énergie) autour de l'estimation de l'humidité du sol dans le contexte de la gestion des ressources en eau dans les régions semi-arides. Récemment, des missions spatiales permettent d'observer l'humidité des sols en surface; notamment avec le capteur AMSR-E (Advanced Microwave Scanning Radiometer-EOS) et la mission SMOS (Soil Moisture Ocean Salinity). Toutefois la résolution spatiale de ces capteurs est trop large (> 40 km) pour des applications hydrologiques. Afin de résoudre le problème d'échelle, l'algorithme de désagrégation DisPATCh (Disaggregation based on Physical and Theoretical Scale Change) a été développé en se basant sur un modèle d'évapotranspiration. Dans la première partie de thèse, l'algorithme est appliqué et validé sur le bassin du Murrumbidgee (sud-est de l'Australie) avec une résolution spatiale cible de 1 km à partir des données de LST (Température de surface) et NDVI (indice de végétation) issues de MODIS (MODerate resolution Imaging Spectroradiometer) et de deux produits d'humidité du sol basse résolution : SMOS et AMSR-E. Les résultats montrent que la désagrégation est plus efficace en été, où la performance du modèle d'évapotranspiration est optimale. L'étude précédente a notamment mis en évidence que la résolution temporelle des données DisPATCh est limitée par la couverture nuageuse visible sur les images MODIS et la résolution temporelle des radiomètres micro-ondes (3 jours pour SMOS). Dans la deuxième partie, une nouvelle approche est donc développée pour assurer la continuité temporelle des données d'humidité de surface en assimilant les données DisPATCh dans un modèle dynamique de type force-restore, forcé par des données météorologiques issus de ré-analyses, dont les précipitations. La méthode combine de manière originale un système variationnel (2D-VAR) pour estimer l'humidité du sol en zone racinaire et une approche séquentielle (filtre de Kalman simplifié) pour analyser l'humidité du sol en surface. La performance de l'approche est évaluée sur deux zones: la région Tensift-Haouz au Maroc et la région de Yanco en Australie. Les résultats montrent que le couplage désagrégation/assimilation de l'humidité du sol est un outil performant pour estimer l'humidité en surface à l'échelle journalière, même lorsque les données météorologiques sont incertaines. Dans la troisième partie, une méthode de correction des effets topographiques sur la LST est développée dans le but d'étendre l'applicabilité de DisPATCh aux zones vallonnées ou montagneuses qui jouent souvent le rôle de château d'eau sur les régions semi-arides. Cette approche, basée sur un modèle de bilan d'énergie à base physique, est testée avec les données ASTER (Advanced Spaceborne Thermal Emission Reflection Radiometer) et Landsat sur la vallée d'Imlil dans le Haut Atlas Marocain. Les résultats indiquent que les effets topographiques ont été fortement réduits sur les images de LST à ~100 m de résolution et que la LST corrigée pourrait être utilisée comme une signature de l'état hydrique en montagne. Les perspectives ouvertes par ces travaux concernent la correction/désagrégation des données de précipitations et l'estimation des apports par l'irrigation pour une gestion optimisée de l'eau

Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle

Characterizing the land surface temperature (LST) and its diurnal cycle is important in understanding a range of surface properties, including soil moisture status, evaporative response, vegetation stress and ground heat flux. While remote-sensing platforms present a number of options to retrieve this variable, there are inevitable compromises between the resolvable spatial and temporal resolution. For instance, the spatial resolution of geostationary satellites, which can provide sub-hourly LST, is often too coarse (3 km) for many applications. On the other hand, higher-resolution polar orbiting satellites are generally infrequent in time, with return intervals on the order of weeks, limiting their capacity to capture surface dynamics. With recent developments in the application of unmanned aerial vehicles (UAVs), there is now the opportunity to collect LST measurements on demand and at ultra-high spatial resolution. Here, we detail the collection and analysis of a UAV-based LST dataset, with the purpose of examining the diurnal surface temperature response: something that has not been possible from traditional satellite platforms at these scales. Two separate campaigns were conducted over a bare desert surface in combination with either Rhodes grass or a recently harvested maize field. In both cases, thermal imagery was collected between 0800 and 1700 local solar time. The UAV-based diurnal cycle was consistent with ground-based measurements, with a mean correlation coefficient and root mean square error (RMSE) of 0.99 and 0.68 °C, respectively. LST retrieved over the grass surface presented the best results, with an RMSE of 0.45 °C compared to 0.67 °C for the single desert site and 1.28 °C for the recently harvested maize surface. Even considering the orders of magnitude difference in scale, an exploratory analysis comparing retrievals of the UAV-based diurnal cycle with METEOSAT geostationary data yielded pleasing results (R = 0.98; RMSE = 1.23 °C). Overall, our analysis revealed a diurnal range over the desert and maize surfaces of ~20 °C and ~17 °C respectively, while the grass showed a reduced amplitude of ~12 °C. Considerable heterogeneity was observed over the grass surface at the peak of the diurnal cycle, which was likely indicative of the varying crop water status. To our knowledge, this study presents the first spatially varying analysis of the diurnal LST captured at ultra-high resolution, from any remote platform. Our findings highlight the considerable potential to utilize UAV-based retrievals to enhance investigations across multi-disciplinary studies in agriculture, hydrology and land-atmosphere investigations

Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects

The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics

Data disaggregation and evapotranspiration modeling: a synergism between multi-spectral/multi-resolution remote sensing data

International audienceEvapotranspiration (ET) is a surface process that is particularly well constrained by remote sensing data in the shortwave, thermal infrared and microwave domains. The near-surface soil moisture (NSSM) retrieved over the 0-5 cm layer at L-band is a key parameter of soil evaporation (E). The green vegetation index (GVI) estimated from red and near-infrared reflectances controls the partitioning between E and plant transpiration (T). The surface albedo (SA) derived from shortwave reflectances modulates the available energy and provides information on plants' phenological stage. The land surface temperature (LST) derived from thermal infrared radiances is a signature of the surface thermodynamic equilibrium, which is largely regulated by ET. This talk will present recent developments of three remote sensing methodologies based on the synergy between NSSM, GVI, SA and LST observations, and their physical link with ET process: - DISPATCH is a unique algorithm that combines NSSM, GVI and LST data within an E-based disaggregation scheme of NSSM. It aims at estimating the NSSM variability within a 40 km resolution SMOS (Soil Moisture and Ocean Salinity) pixel at resolutions ranging from 100 m to several km using the shortwave/thermal data collected by Landsat/ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) and MODIS (MODerate resolution Imaging Spectroradiometer), respectively. - SEB-1S is a mono-source surface energy balance model to estimate crop ET using ASTER/Landsat-derived LST, GVI and SA data. It provides a consistent physical interpretation of two commonly used methodologies based on the LST-SA and LST-GVI spaces. - SEB-4S is a four-source version of SEB-1S that provides an estimate of ET and E/T partitioning by separating the surface of agricultural fields in four components: bare soil, unstressed green vegetation, water-stressed green vegetation and senescent vegetation. In each case, a link between ET modeling and NSSM/LST data disaggregation will be highlighted. Finally, the prospect of coupling SEB-4S ET formalism with disaggregation schemes of NSSM and LST data at high-spatial resolution will be presented

Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

International audienceHigh spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m 3 m 23 compared to the open-loop force-restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model

Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil

International audienceRadar data have been used to retrieve and monitor the surface soil moisture (SM) changes in various conditions. However, the calibration of radar models whether empirically or physically-based, is still subject to large uncertainties especially at high-spatial resolution. To help calibrate radar-based retrieval approaches to supervising SM at high resolution, this paper presents an innovative synergistic method combining Sentinel-1 (S1) microwave and Landsat-7/8 (L7/8) thermal data. First, the S1 backscatter coefficient was normalized by its maximum and minimum values obtained during 2015–2016 agriculture season. Second, the normalized S1 backscatter coefficient was calibrated from reference points provided by a thermal-derived SM proxy named soil evaporative efficiency (SEE, defined as the ratio of actual to potential soil evaporation). SEE was estimated as the radiometric soil temperature normalized by its minimum and maximum values reached in a water-saturated and dry soil, respectively. We estimated both soil temperature endmembers by using a soil energy balance model forced by available meteorological forcing. The proposed approach was evaluated against in situ SM measurements collected over three bare soil fields in a semi-arid region in Morocco and we compared it against a classical approach based on radar data only. The two polarizations VV (vertical transmit and receive) and VH (vertical transmit and horizontal receive) of the S1 data available over the area are tested to analyse the sensitivity of radar signal to SM at high incidence angles (39°–43°). We found that the VV polarization was better correlated to SM than the VH polarization with a determination coefficient of 0.47 and 0.28, respectively. By combining S1 (VV) and L7/8 data, we reduced the root mean square difference between satellite and in situ SM to 0.03 m3 m−3, which is far smaller than 0.16 m3 m−3 when using S1 (VV) only