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

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

Vegetation/Forest Effects in Microwave Remote Sensing of Soil Moisture

This thesis includes (1) the distorted Born approximation (DBA) and an improved coherent model for vegetation-covered surfaces at L-band for data-cube based soil moisture retrieval; (2) a unified approach for combined active and passive remote sensing of vegetation-covered surfaces with the same input physical parameters; (3) Numerical Maxwell Model in 3D (NMM3D) simulations of a vegetation canopy comprising randomly distributed dielectric cylinders; and (4) a hybrid method based on the generalized T matrix of single objects and Foldy-Lax equations for NMM3D full-wave simulations of the realistic vegetation/forest with vector spherical, spheroidal and cylindrical wave expansoins. The main contributions and novelty of this thesis are NMM3D full-wave simulations of vegetation/forest canopy using the generalized T matrix of the single object and Foldy-Lax equations of multiple scattering among many objects. Before this work, the large-scale full-wave simulations of vegetation/forst such as many tree trunks were deemed very difficult. The NMM3D full-wave simulation results showed that the results of past models significantly overestimate attenuation in a vegetation/forest canopy. The NMM3D full-wave models predict transmissions that are several times greater than that of past models. A much greater microwave transmission means the microwave can better penetrate a vegetation/forest canopy and thus it can be used to retrieve soil moisture. The thesis starts with the DBA to compute the backscattering coefficients for various kinds of vegetation-covered surfaces such as pasture, wheat and canola fields. For the soybean fields, an improved coherent branching model is used. The novel feature of the analytic coherent model consists of conditional probability functions to eliminate the overlapping effects of branches in the former branching models. In order to make use of complex physical models for real time retrieval for satellite missions, the outputs of the physical model are provided as lookup-tables (data-cubes). By inverting the lookup-tables, time-series retrieval of soil moisture is performed. Next, the DBA is extended to calculate the bistatic scattering coefficients. Emissivities are calculated by integrating the bistatic scattering coefficients over the hemispherical solid angle. The backscattering coefficients and emissivities calculated using this approach form a consistent model for combined active and passive microwave remote sensing. In the analytical physical models mentioned above, as well as in another commonly used approach of the radiative transfer equation (RTE), the attenuation of the wave is accounted for by the attenuation rate per unit distance, which originates from the concept of an “effective medium”. Such a model is unsuitable for a vegetation canopy. Because of these issues, NMM3D full-wave simulations of vegetation are pursued. Firstly, the scattering of a vegetation canopy consisting of cylindrical scatterers is calculated. The approach for solving Maxwell’s equations is based on the Foldy-Lax multiple scattering equations (FL) combined with the body of revolution (BOR). For a layer of extended-cylinders distributed in clusters, the NMM3D simulations at C-band show very different results from DBA/RTE. The method FL-BOR is limited for rotationally symmetric objects such as cylinders and circular disks. To perform NMM3D full-wave simulations for realistic vegetation/forests, a hybrid method is used, which is a hybrid of the off-the-shelf techniques and newly developed techniques. The newly developed techniques are the three key steps of the hybrid method: (1) extracting the generalized T matrix of each single object using vector spheroidal/cylindrical waves, (2) vector wave transformations, and (3) solving FL for all the objects.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153347/1/huanght_1.pd