Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data

This study describes a semi-empirical model developed to estimate volumetric soil moisture ( v ϑ) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient (σ° dB) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients 0 VV σ and 0 VH σ were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0–10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March–May) of 2017 and 2018. A linear equation was proposed by combining 0 VV σ and 0 VH σ to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization(VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of 0 VV σ and 0 VH σ . The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of 0 VV σ + 0 VH σ showed a significantly higher R2 than the individual polarimetric channels

Monitoring crops water needs at high spatio-temporal resolution by synergy of optical/thermal and radar observations

L'optimisation de la gestion de l'eau en agriculture est essentielle dans les zones semi-arides afin de préserver les ressources en eau qui sont déjà faibles et erratiques dues à des actions humaines et au changement climatique. Cette thèse vise à utiliser la synergie des observations de télédétection multispectrales (données radar, optiques et thermiques) pour un suivi à haute résolution spatio-temporelle des besoins en eau des cultures. Dans ce contexte, différentes approches utilisant divers capteurs (Landsat-7/8, Sentinel-1 et MODIS) ont été developpées pour apporter une information sur l'humidité du sol (SM) et le stress hydrique des cultures à une échelle spatio-temporelle pertinente pour la gestion de l'irrigation. Ce travail va parfaitement dans le sens des objectifs du projet REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) qui visent à estimer l'humidité du sol dans la zone racinaire (RZSM) afin d'optimiser la gestion de l'eau d'irrigation. Des approches innovantes et prometteuses sont mises en place pour estimer l'évapotranspiration (ET), RZSM, la température de surface du sol (LST) et le stress hydrique de la végétation à travers des indices de SM dérivés des observations multispectrales à haute résolution spatio-temporelle. Les méthodologies proposées reposent sur des méthodes basées sur l'imagerie, la modélisation du transfert radiatif et la modélisation du bilan hydrique et d'énergie et sont appliquées dans une région à climat semi-aride (centre du Maroc). Dans le cadre de ma thèse, trois axes ont été explorés. Dans le premier axe, un indice de RZSM dérivé de LST-Landsat est utilisé pour estimer l'ET sur des parcelles de blé et des sols nus. L'estimation par modélisation de ET a été explorée en utilisant l'équation de Penman-monteith modifiée obtenue en introduisant une relation empirique simple entre la résistance de surface (rc) et l'indice de RZSM. Ce dernier est estimé à partir de la température de surface (LST) dérivée de Landsat, combinée avec les températures extrêmes (en conditions humides et sèches) simulée par un modèle de bilan d'énergie de surface piloté par le forçage météorologique et la fraction de couverture végétale dérivée de Landsat. La méthode utilisée est calibrée et validée sur deux parcelles de blé situées dans la même zone près de Marrakech au Maroc. Dans l'axe suivant, une méthode permettant de récupérer la SM de la surface (0-5 cm) à une résolution spatiale et temporelle élevée est développée à partir d'une synergie entre données radar (Sentinel-1) et thermique (Landsat) et en utilisant un modèle de bilan d'énergie du sol. L'approche développée a été validée sur des parcelles agricoles en sol nu et elle donne une estimation précise de la SM avec une différence quadratique moyenne en comparant à la SM in situ, égale à 0,03 m3 m-3. Dans le dernier axe, une nouvelle méthode est développée pour désagréger la MODIS LST de 1 km à 100 m de résolution en intégrant le SM proche de la surface dérivée des données radar Sentinel-1 et l'indice de végétation optique dérivé des observations Landsat. Le nouvel algorithme, qui inclut la rétrodiffusion S-1 en tant qu'entrée dans la désagrégation, produit des résultats plus stables et robustes au cours de l'année sélectionnée. Dont, 3,35 °C était le RMSE le plus bas et 0,75 le coefficient de corrélation le plus élevé évalués en utilisant le nouvel algorithme.Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

A multitemporal algorithm, originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated to retrieve soil moisture from L-band radar data, such as those provided by the National Aeronautics and Space Administration Soil Moisture Active/Passive (SMAP) mission. This type of algorithm may deliver more accurate soil moisture maps that mitigate the effect of roughness and vegetation changes. Within the multitemporal inversion scheme based on the Bayesian maximum a posteriori probability (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model. The model calibration and validation tasks have been accomplished using the data collected during the SMAP validation experiment 12 spanning several soil conditions (pasture, wheat, corn, and soybean). The data have been used to update the forward model for bare soil scattering at L-band and to tune a simple vegetation scattering model considering two different classes of vegetation: those producing mainly single scattering effects and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction. The algorithm retrievals showed a root mean square difference (RMSD) around 5% over bare soil, soybean, and cornfields. As for wheat, a bias was observed; when removed, the RMSD went down from 7.7% to 5%

Remote sensing based assessment of land cover and soil moisture in the Kilombero floodplain in Tanzania

Wetlands provide important ecological, biological, and social-economic services that are critical for human existence. The increasing demand for food, arable land shortage and changing climate conditions in East Africa have created a paradigm shift from upland cultivation to wetland use due to their year-round soil water availability. However, there is need to control and manage the activities within the wetlands to ensure sustainable use while negating any negative effects caused by these activities. This is implemented through the decisions made by the land managers within the wetlands. Providing the users of the wetlands with scientific knowledge acts as a support tool for policy-making geared towards the sustainable use of the wetlands. The overall research contains two main components: First, the need for timely land cover maps at a reasonable scale, and secondly, the assessment of soil moisture as a major contributor to agricultural production. The objectives of the study were to generate land cover maps from multi-sensor optical datasets and to assess the performance of single-polarized Sentinel-1 Gray Level Co-occurrence Matrix (GLCM) texture and Principal Component Analysis (PCA) features by applying multiple classification algorithms in a floodplain in the Kilombero catchment. Furthermore, soil moisture spatial-temporal patterns over three hydrological zones was assessed, estimation of soil moisture from radar data and generation of soil moisture products from global products was investigated. The correlation of the merged products to Normalized Difference Vegetation Index (NDVI) measures was also investigated. RapidEye, Sentinel-2 and Landsat images were used in determining the areal extents of four major land cover classes namely vegetated, bare, water and built up. The acquisition period of the images ranges from August 2013 to June 2015 for the RapidEye images, December 2015 to August 2016 for the Sentinel-2 images and 2013 to 2016 Landsat-8 images were included in the land cover time series dynamic study. However, the major challenge arising was cloud coverage and hence Sentinel-1 images were tested in the application of Synthetic Aperture Radar (SAR) in wetland mapping. Variograms were used in spatial-temporal assessment of soil moisture data collected from three hydrological zones, riparian, middle and fringe. A roughness parameter was derived from a semi-empirical model. Soil moisture was retrieved from TerraSAR-X and RadarSAT-2 with the retrieved roughness parameter as an input in a linear regression equation. Triple collocation was applied in error assessment of the global soil moisture products prior to development of a merged product. Cross-correlation was applied in relating NDVI to soil moisture. Optical data (RapidEye, Landsat-8, and Sentinel-2) generated land cover maps used in assessing the land cover dynamics over time. The land cover ratios were related to depth to groundwater. As the depth to groundwater reduced in June the bare land coverage was 45-57% while that of vegetation was 34-47%. In December when the depth to groundwater was highest, bare land coverage was 62-69% while that of the vegetated area was 27-25%. This indicates that depth of groundwater and vegetation coverage responds to seasonality. During the dry season, 68-81% of the total vegetation class is within the riparian zone. In the classification of the SAR images, the overall accuracies for the single polarized VV images ranged from 54-76%, 60-81% and 61-80% for Random Forest (RF), Neural Network (NN) and Support Vector Machine (SVM) respectively. GLCM features had overall accuracies of 64-86%, 65-88% and 65-86% for RF, NN, and SVM respectively. PCA derived images had similar overall accuracies of 68-92% for NN, RF, and SVM respectively. The PCA images had the highest overall accuracy for the entire time series indicating that reduction in the number of texture features to layers containing the maximum variance improves the accuracy. The standard deviation of soil moisture was noted to increase with increasing soil moisture. Soil texture plays a key role in soil moisture retention. The riparian fields had a high water content explained by the high clay and organic matter content. A roughness parameter was derived and utilized in the retrieval of soil moisture from SAR resulting to R2 of 0.88- 0.92 between observed and simulated soil moisture values from co-polarized RadarSAT-2 HH and TerraSAR-X HH and VV. Merged soil moisture product from FEWSNET Land Data Assimilation System_NOAH (FLDAS_NOAH), ECMWF Re-Analysis Interim (ERA-Interim) and Soil Moisture and Ocean Salinity (SMOS) and FLDAS_Variable Infiltration Capacity (VIC), ERA-Interim and SMOS had similar patterns attributed to FLDAS_NOAH and FLDAS_VIC forced by the same precipitation product (RFE). Cross-correlation of Moderate-resolution Imaging Spectrometer (MODIS) NDVI and the merged soil moisture products revealed a 2-month lag of NDVI. Hence, the relationship is useful in determining the Start of Season from soil moisture products. In conclusion, the successful land cover mapping of the study area demonstrated the use of satellite imagery for wetland characterization. The vast coverage and frequent acquisitions of optical and microwave remotely sensed data additionally make the approaches transferable to other locations and allow for mapping at larger scales. Soil moisture assessment from point data revealed varied soil moisture patterns whereas global remotely sensed and modeled products rather provide complementary information about growing conditions, and hence a situational assessment tool of potential of physical availability dimension of food security. This study forms a baseline upon which additional monitoring and assessment of the Kilombero wetland ecosystem can be performed with the current results marked as a reference. Moreover, the study serves as a demonstration case of remote sensing based approaches for land cover and soil moisture mapping, whose results are useful to stakeholders to aid in the implementation of adapted production techniques for yield optimization while minimizing the unsustainable use of the natural resources.Feuchtgebiete erbringen wichtige ökologische, biologische und sozial-ökonomische Dienstleistungen, welche entscheidend für das menschliche Dasein sind. Der steigende Bedarf an Nahrung, der Mangel an landwirtschaftlichen Nutzflächen und die Veränderung der klimatischen Bedingungen in Ostafrika haben zu einem Paradigmenwechsel vom Anbau im Hochland hin zur Nutzung von Feuchtgebieten geführt. Allerdings sind Kontrolle und Management der Aktivitäten in Feuchtgebieten notwendig, um die nachhaltige Nutzung zu sichern und negative Effekte dieser Aktivitäten zu vermeiden. Die Implementierung erfolgt durch die Landverwalter in den Feuchtgebieten. Den Nutzern von Feuchtgebieten wissenschaftliche Erkenntnisse bereitzustellen dient als Hilfsmittel zur politischen Entscheidungsfindung für die nachhaltige Feuchtgebietsnutzung. Die Forschung im Rahmen der Dissertation beinhaltet zwei Hauptkomponenten: erstens den Bedarf an aktuellen Landbedeckungskarten auf einer angemessenen Skalenebene und zweitens die Erfassung der Bodenfeuchte als wichtiger Einflussfaktor auf die landwirtschaftliche Produktion. Das Ziel der Untersuchung war, Landbedeckungskarten auf Grundlage von multisensorischen optischen Daten zu erstellen und die Eignung der Textur der einfach polarisierten Sentinel-1 Grauwertmatrix (GLCM) sowie der einer Hauptkomponentenanalyse (PCA) bei Anwendung unterschiedlicher Klassifikationsalgorithmen zu beurteilen. Des Weiteren wurden raum-zeitliche Bodenfeuchtemuster über drei hydrologische Zonen hinweg modelliert, die Bodenfeuchte aus Radardaten abgeleitet sowie die Erstellung von Bodenfeuchteprodukten auf Basis von globalen Produkten untersucht. Die Korrelation der Bodenfeuchteprodukte mit dem Normalisierten Differenzierten Vegetationsindex (NDVI) wurde ebenfalls analysiert. RapidEye, Sentinel-2 und Landsat Bilder wurden genutzt um die räumliche Ausdehnung der vier Hauptklassen (Vegetation, freiliegender Boden, Wasser und Bebauung) der Landbedeckung zu ermitteln. Für die Zeitreihenanalyse der der Landbedeckungsdynamik wurden RapidEye-Daten von August 2013 bis Juni 2015, Sentinel-2-Bilder von Dezember 2015 bis August 2016 und Landsat-8-Bilder von 2013 bis 2016 verwendet. Die größte Herausforderung war jedoch die Wolkenbedeckung, weshalb die Anwendung von Synthetic Aperture Radar (SAR) für die Feuchtgebietskartierung getestet wurde. Die gemessene Bodenfeuchte wurde mittels Variogrammen für die drei hydrologischen Zonen (Uferzone, Mitte und Randgebiete) raum-zeitlich interpoliert. Ein Rauhigkeitsparameter wurde aus einem semi-empirischen Modell hergeleitet. Die Bodenfeuchte wurde aus TerraSAR-X und RadarSAT-2- Bildern unter Verwendung des Rauhigkeitsparameters als Eingangsgröße in einer linearen Regression abgeleitet. Vor der Zusammenführung der Produkte wurde das globale Bodenfeuchteprodukt mithilfe von dreifacher Kollokation auf Fehler überprüft. Die Kreuzkorrelation zwischen NDVI und Bodenfeuchte wurde berechnet. Optische Daten (RapidEye, Landsat-8 und Sentinel-2) wurden genutzt, um die zeitliche Dynamik der Landbedeckung zu bestimmen. Die Landbedeckungsverhältnisse wurde mit der Höhe des Grundwasserspiegels korreliert. Ein hoher Grundwasserstand im Juni resultierte in 45-57% unbedecktem Boden, während der Anteil der Vegetation 34-47% betrug. Im Dezember, als der Grundwasserspiegel seinen Tiefststand hatte, erhöhte sich der Anteil des freiliegenden Bodens auf 62-69% und der Anteil der Vegetation verringerte sich auf 27-25%. Das zeigt, dass Grundwasserspiegel und Vegetation saisonalen Schwankungen unterworfen sind. Während der Trockenzeit liegen 68-81% der gesamten als Vegetation klassifizierten Fläche innerhalb der Uferzone. In der Klassifikation der SAR-Bilder liegt die Gesamtgenauigkeit der einfach polarisierten VV-Bilder im Rahmen von 54-76%, 60-81% und 61-80%, entsprechend für Random Forest (RF), Neuronale Netze (NN) und Support Vector Machine (SVM). Die GLCM ergab eine Gesamtgenauigkeit von 64-86%, 65-88% und 65-86% für RF, NN und SVM. Die über eine PCA abgeleiteten Bilder erreichten eine ähnliche Genauigkeit von 68-92% für NN, RF und SVM. Die PCA-Bilder weisen die höchste Gesamtgenauigkeit der gesamten Zeitreihe auf, was darauf hinweist, dass eine Reduktion von Textureigenschaften auf Layer der maximalen Varianz enthalten, die Genauigkeit erhöht. Die Standardabweichung der Bodenfeuchte stieg mit zunehmender Bodenfeuchte. Die Bodentextur spielt dabei eine Schlüsselrolle für das Wasserhaltevermögen des Bodens. Die Uferzone wies einen hohen Wassergehalt auf, was durch den hohen Anteil von Ton und Humus zu erklären ist. Die beobachteten und simulierten Bodenfeuchtewerte von co-polarisierten RadarSAT-2 HH, TerraSAR-X HH und VV Daten korrelieren mit einem R2 von 0.88 - 0.92. Die zusammengesetzten globalen Bodenfeuchteprodukte von FLDAS_NOAH, ERA-Interim sowie SMOS und FLDAS_VIC, ERA-Interim und SMOS zeigen ähnliche Muster wie FLDAS_NOAH und FLDAS_VIC, was über die Verwendung desselben Niederschlagsproduktes (RFE) zu erklären ist. Die Kreuzkorrelation von MODIS NDVI und den zusammengeführten Bodenfeuchteprodukten ergab eine zeitliche Verzögerung des NDVI von zwei Monaten. Dieser Zusammenhang kann daher bei der Bestimmung des Saisonbeginns aus Bodenfeuchtigkeitsprodukten nützlich sein. Zusammengefasst hat die Studie gezeigt, wie Satellitenbilder zur Charakterisierung von Wetlands genutzt werden können. Die große Abdeckung und häufige Aufnahme der optischen und Mikrowellen-Fernerkundungsdaten ermöglichen darüber hinaus die Übertragung der Ansätze auf weitere Gebiete und Kartierung auf größeren Skalen. Die Punktmessungen zeigen kleinräumige Muster der Bodenfeuchte, während globale Fernerkundungsprodukte und Modelle Informationen über die Wachstumsbedingungen liefern und somit ein Bewertungsinstrument der Ernährungssicherheit darstellen können. Weiterhin bildet die Studie eine Basis, auf der ein weitergehendes Monitoring und eine Bewertung des Feuchtgebietsökosystems durchgeführt werden kann. Sie ist ein Beispiel für fernerkundungsbasierte Ansätze zur Landbedeckungs- und Bodenfeuchtekartierung; ihre Ergebnisse sind nützlich, um Akteuren bei der Implementierung von Produktionstechniken zu unterstützen, welche die Erträge maximieren und gleichzeitig die nicht nachhaltige Nutzung der natürlichen Ressourcen minimieren

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Disaggregation of SMOS soil moisture to 100m resolution using MODIS optical/thermal and sentinel-1 radar data: evaluation over a bare soil site in morocco

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ°). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ° and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ° ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ° where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD = 0.032 m3 m−3).This work is a contribution to the REC project funded by the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) action under grant agreement no: 645642. In addition, this work has been partially funded by a public grant of Ministerio de Economía y Competitividad (DI-14-06587) and AGAUR-Generalitat de Catalunya (DI-2015-058)