Downscaling SMAP Soil Moisture Data Using MODIS Data

Soil moisture level is an important index in studying environmental changes. High resolution soil moisture data is in high demand for agricultural and weather forecasting purpose. Current daily large-scale soil moisture projects fail to provide sufficient resolution for medium or small region research. To acquire high-resolution soil moisture data, different kinds of methods are put into practice, including multivariate statistical regression, weight aggregation and so on. In this research, SMAP (Soil Moisture Active Passive) level 3 data with 36-km resolution are successfully downscaled by MODIS (Moderate Resolution Imaging Spectroradiometer) 1-km LST (Land Surface Temperature) product, NDVI (Difference Vegetation Index) product, SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), and TWI (Topographic Wetness Index). Three regression models are built based on these supplemental indexes correlated with the SMAP retrieval. All downscaled results are validated with SMAPVEX15 field data. The research aims to establish and validate the multivariate regression method for downscaling low-resolution remote sensing image (such as SMAP) with local field observations. Based on the validation results, the research suggests the regression models have a decent fit. The downscaled soil moisture data indicating the method is applicable to small region research

Surface Soil Moisture Retrievals from Remote Sensing:Current Status, Products & Future Trends

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space

Assessment of Drought in Grasslands: Spatio – Temporal Analyses of Soil Moisture and Extreme Climate Effects in Southwestern Mongolia

Soil moisture plays an essential key role in the assessment of hydrological and meteorological droughts that may affect a wide area of the natural grassland and the groundwater resource. The surface soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological, and agricultural applications, especially in water-limited or drought-prone regions. However, gauging soil moisture is challenging because of its high variability. While point-scale in-situ measurements are scarce, the remote sensing tools remain the only practical means to obtain regional and global-scale soil moisture estimates. A Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to gauge the Earth’s surface soil moisture (SM) at the near-daily time scales. This work aims to evaluate the spatial and temporal patterns of SMOS soil moisture, determine the effect of the climate extremes on the vegetation growth cycle, and demonstrate the feasibility of using our drought model (GDI) the Gobi Drought Index. The GDI is based on the combination of SMOS soil moisture and several products from the MODIS satellite. We used this index for hydro-meteorological drought monitoring in Southwestern Mongolia. Firstly, we validated bias-corrected SMOS soil moisture for Mongolia by the in-situ soil moisture observations 2000 to 2015. Validation shows satisfactory results for assessing drought and water-stress conditions in the grasslands of Mongolia. The correlation analysis between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems shows a high correlation between the bias-corrected, monthly-averaged SMOS and NDVI data (R2 > 0.81). Further analysis of the SMOS and in situ SM data revealed a good match between spatial SM distribution and the rainfall events over Southwestern Mongolia. For example, during dry 2015, SM was decreased by approximately 30% across the forest-steppe and steppe areas. We also notice that both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia. The second part of this research, analyzed several dzud (specific type of climate winter disaster) events (2000, 2001, 2002, and 2010) related to drought, to comprehend the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. We determined how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), NDVI, and livestock mortality data. The NDVI is used as an indicator of vegetation activity and growth. Its spatial and temporal pattern is expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in arid areas. We found that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The snowy winters can cause a 10 to 20-day early peak in NDVI and the following increase in vegetation growth. However, during a year with dry winter conditions, the vegetation growth phase begins later due to water deficiency and the entire year has a weaker vegetation growth. Generally, livestock loss and the reduction of grazing pressure was played a crucial role in vegetation recovery after extreme climatic events in Mongolia. At the last stage of our study, we develop an integrated Gobi drought index (GDI), derived from SMOS and LST, PET, and NDVI MODIS products. GDI can incorporate both, the meteorological and soil moisture drought patterns and sufficiently well represent overall drought conditions in the arid lands. Specifically, the monthly GDI and 1-month standardized precipitation index SPI showed significant correlations. Both of them are useful for drought monitoring in semi-arid lands. But, the SPI requires in situ data that are sparse, while the GDI is free from the meteorological network restriction. Consequently, we compared the GDI with other drought indices (VSWI, NDDI, NDWI, and in-situ SM). Comparison of these drought indices with the GDI allowed assessing the droughts’ behavior from different angles and quantified better their intensity. The GDI maps at fine-scale (< 1km) permit extending the applicability of our drought model to regional and local studies. These maps were generated from 2000 to 2018 across Southwestern Mongolia. Fine-scale GDI drought maps are currently limited to the whole territory for Mongolia but the algorithm is dynamic and can be transported to any region. The GDI drought index can be served as a useful tool for prevention services to detect extremely dry soil and vegetation conditions posing a risk of drought and groundwater resource depletion. It was able to detect the drought events that were underestimated by the National Drought Watch System in Mongolia. In summary, with the help of satellite, climatological, and geophysical data, the integrated GDI can be beneficial for vegetation drought stress characterization and can be a useful tool to monitor the effectiveness of pasture land restoration management practices for Mongolian livelihoods. The future application of the GDI can be extended to monitor potential impacts on water resources and agriculture in Mongolia, which have been impacted by long periods of drought

Improvements and Applications of Satellite-Derived Soil Moisture Data for Flood Forecasting

Accurate knowledge of the spatiotemporal behavior of soil moisture can greatly improve hydrological forecasting capability. While ground-based soil moisture measurements are ideal, they tend to be sparse in space and only available for limited periods. To overcome this, a viable alternative is space-borne microwave remote sensing because of the observational capability it offers for retrieving soil moisture in near real-time at the global scale. However, its direct applications have been limited due to the uncertainty associated, and the coarse spatial resolution these are available at. Therefore, this thesis aims to use satellite soil moisture products for assessing flood risk by redressing their drawbacks in terms of accuracy and spatial resolution. The research consists of three inter-dependent focal areas; evaluation, improvement and application of the soil moisture products. For the first objective, this thesis compared two alternate soil moisture products using spatiotemporally identical passive microwave observations but different retrieval algorithms. Complementarity in the performance of the products was identified and accordingly provided the basis for the improvement in soil moisture. For the second objective, based on the identified complementarity, different formulations of weighted linear combination were proposed as a means of reducing the structural uncertainty associated with each retrieval algorithm. To address the limitation of resulting retrievals existing over coarse grid resolutions, an approach was presented to spatially disaggregate coarse soil moisture by only using a remotely sensed vegetation index product. The method provides a continuous timeseries of disaggregated soil moisture with a persistence structure closer to what is observed. Lastly, for the third objective, a fully remote sensing based flood warning method using readily available soil moisture and rainfall data, open-access topographic and soil data, was developed. This method was applied over a number of anthropogenically unaffected river basins and was shown to have promise for flood warning in ungauged watersheds. Ongoing and future research will form an integrated pathway for producing an improved soil moisture product available at finer spatial resolution, which can be used for various regional applications, along with using this to provide real-time flood warnings using freely available information to rural and remote communities worldwide

Estudio de la humedad superficial del suelo en la cuenca del Duero mediante la integración multiescala de técnicas basadas en teledetección, modelización y observaciones in situ

[ES]El conocimiento de la humedad del suelo con suficiente detalle espacial y temporal a escala regional es de suma importancia para la gestión eficaz de los recursos hídricos en regiones semiáridas con una fuerte implantación de la actividad agrícola de secano y regadío, como es el caso de la cuenca del Duero. La investigación realizada va dirigida a contribuir al conocimiento de las variaciones espaciales y temporales de la humedad del suelo en zonas de suelos agrícolas del sector central de la cuenca del Duero. En la presente tesis se aborda el estudio de la humedad superficial mediante una aproximación multiescala basada en técnicas de teledetección, modelización distribuida y mediciones in situ de estaciones experimentales distribuidas en la cuenca de Duero. Entre las novedades que aporta esta investigación destacan la utilización de datos de alta resolución de la versión SMOS L4 “all weather” v.3 a resolución espacial de un kilómetro y del modelo SWBM-GA aplicado de forma espacialmente distribuida (SWBMd) a la misma resolución. El objetivo es la validación temporal y espacial del producto SMOS L4 v.3 con datos de humedad de SWBMd como alternativa eficiente a las estimaciones in situ, con objeto de reducir los efectos de las incertidumbres derivadas de la diferencia de resolución espacial entre las mediciones in situ y los datos del satélite. El estudio de variabilidad de la humedad del suelo se realiza con una resolución espacial de un kilómetro y una resolución temporal diaria a lo largo de un periodo superior a dos años, y se desarrolla en zonas agrícolas del sector central de la cuenca del Duero; tanto de forma puntual en estaciones de las redes de medición de humedad como de forma distribuida en dos subzonas. Las subzonas, con una extensión superior a 1000 km2 cada una, se localizan en áreas agrícolas representativas de características edáficas y climáticas contrastadas. Con este fin, en la metodología se aplica el modelo SWBM-GA de forma distribuida y se compara con los datos obtenidos por el satélite SMOS en su versión L4 y con mediciones in situ de estaciones experimentales distribuidas en la cuenca de Duero. Posteriormente, los datos de humedad SMOS L4 se validan mediante estrategias espaciales y temporales en las subzonas de estudio. Entre las conclusiones obtenidas en la investigación destacan la elevada capacidad del modelo distribuido SWBMd para simular con precisión la humedad superficial a la resolución espacial requerida, con una resolución temporal horaria y diaria, en suelos de uso agrícola con una gama amplia de texturas en la zona de estudio. El modelo distribuido permite la validación de SMOS L4 de forma fiable, al reducir los problemas de escala derivados de las diferencias entre la resolución espacial de SMOS L4 y las mediciones in situ. Las series de humedad de SMOS L4 representan satisfactoriamente, de forma estable y consistente, la variabilidad temporal de la humedad SWBMd en las estaciones representativas de la cuenca del Duero y en las dos subzonas de estudio. La elevada fiabilidad de SMOS L4 para estimar la variabilidad temporal de la humedad contrasta con su menor capacidad para detectar la variabilidad espacial de la humedad en las subzonas de estudio. La variabilidad espacial de la humedad de SMOS L4 es más homogénea y se distribuye en función de una zonificación relacionada fundamentalmente con las variables climáticas temperatura y precipitación, las cuales ejercen una influencia sobre la variación de la humedad del suelo en mayores extensiones. En contraste, el modelo distribuido presenta una alta capacidad para discriminar la variabilidad espacial de la humedad del suelo debido al papel que juegan a escala de detalle las características edáficas. El estudio integrado de los datos de humedad del suelo obtenidos con las técnicas de teledetección, modelización distribuida y medidas in situ permite superar las limitaciones individuales de cada una de las técnicas, enriqueciendo y proponiendo un sistema más eficaz de monitorización de la humedad del suelo a diferentes escalas