AMSR2 Soil Moisture Product Validation

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness temperature observations that are used to retrieve soil moisture. Merging AMSR-E and AMSR2 will help build a consistent long-term dataset. Before tackling the integration of AMSR-E and AMSR2 it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites. Three products that rely on different algorithms were evaluated; the JAXA Soil Moisture Algorithm (JAXA), the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). Results indicate that overall the SCA has the best performance based upon the metrics considered

GCOM-W AMSR2 Soil Moisture Product Validation Using Core Validation Sites

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of the Advanced Microwave Scanning RadiometerEarth Observing System (AMSR-E) after almost 10 years of observations. Both missions provide brightness temperature observations that are used to retrieve soil moisture estimates at the near surface. A merged AMSR-E and AMSR2 data product will help build a consistent long-term dataset; however, before this can be done, it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on the validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites around the world. A total of three soil moisture products that rely on different algorithms were evaluated; the Japan Aerospace Exploration Agency (JAXA) soil moisture algorithm, the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). JAXA, SCA and LPRM soil moisture estimates capture the overall climatological features. The spatial features of the three products have similar overall spatial structure. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture with a satisfactory performance matrix when compared to in situ observations (ubRMSE0.059 m3m3, Bias-0.083 m3m3, R0.465). The SCA performs well over low and moderately vegetated areas (ubRMSE0.053 m3m3, Bias-0.039 m3m3, R0.549). The LPRM product has a large dynamic range compared to in situ observations with a wet bias (ubRMSE0.094 m3m3, Bias0.091 m3m3, R0.577). Some of the error is due to the difference in observation depth between the in situ sensors (5 cm) and satellite estimates (1 cm). Results indicate that overall the JAXA and SCA have the best performance based upon the metrics considered

広域観測データを用いた陸面データ同化実験

科学研究費助成事業 研究成果報告書：基盤研究(B)2014-2017課題番号 : 2628915

Information Theoretic Evaluation of Satellite Soil Moisture Retrievals

Microwave radiometry has a long legacy of providing estimates of remotely sensed near surfacesoil moisture measurements over continental and global scales. A consistent assessment of theerrors and uncertainties associated with these retrievals is important for their effective utilization in modeling, data assimilation and end-use application environments. This article presents an evaluationof soil moisture retrieval products from AMSR-E, ASCAT, SMOS, AMSR2 and SMAPinstruments using information theory-based metrics. These metrics rely on time series analysis ofsoil moisture retrievals for estimating the measurement error, level of randomness (entropy) andregularity (complexity) of the data. The results of the study indicate that the measurement errors inthe remote sensing retrievals are significantly larger than that of the ground soil moisture measurements.The SMAP retrievals, on the other hand, were found to have reduced errors (comparable to Preprint submitted to Remote Sensing of Environment October 1, 2017those of in-situ datasets), particularly over areas with moderate vegetation. The SMAP retrievals also demonstrate high information content relative to other retrieval products, with higher levelsof complexity and reduced entropy. Finally, a joint evaluation of the entropy and complexity ofremotely sensed soil moisture products indicates that the information content of the AMSR-E, ASCAT,SMOS and AMSR2 retrievals is low, whereas SMAP retrievals show better performance. The use of information theoretic assessments is effective in quantifying the required levels of improvements needed in the remote sensing soil moisture retrievals to enhance their utility and information content

Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas

© 2019 by the authors. High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as "open-loop" models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byrans Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E(SWI), SMOSSWI, AMSR2(SWI), and ASCAT(SWI), with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50% of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six openloop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by C0 :12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by C0:06, suggesting that data assimilation yields significant benefits at the global scale

Comparison between satellite-based and cosmic ray probe soil moisture estimates : a case study in the Cathedral Peak catchment.

Master of Science in Environmental Hydrology. University of KwaZulu-Natal, Pietermaritzburg 2015.Abstract available in PDF file

Advances in the Ecohydrology of Arid Lands

This is a Special Issue (SI) of Hydrology. The title of the SI is “Advances in the Ecohydrology of Arid Lands”. Ecohydrology is an emerging, cross disciplinary subfield of hydrology devoted to the mutual interactions between water and ecosystems. Today, the important question of what these interactions mean for human society and how human society impacts these interactions is also part of this subject. The specific climatic/geographic focus here is on arid lands broadly defined as water-deficient regions where potential evapotranspiration (PET) exceeds precipitation (P). The intent of the SI is to present scientifically accurate information on the current state of leading ecohydrology oriented research on arid lands, representing the best contemporary thinking in the field. The five research articles presented by no means cover the field but provide an introduction to the variety of current research. The intended audience is not only those involved in this field but also those engaged in the more traditional aspects of hydrology, biology, ecology, geography, engineering, water management, agriculture urban planning, and other relevant fields

HUMAN AND CLIMATE IMPACTS ON FLOODING VIA REMOTE SENSING, BIG DATA ANALYTICS, AND MODELING

Over the last 20 years, the amount of streamflow has greatly increased and spring snowmelt floods have occurred more frequently in the north-central U.S. In the Red River of the North Basin (RRB) overlying portions of North Dakota and Minnesota, six of the 13 major floods over the past 100 years have occurred since the late 1990s. Based on numerous previous studies as well as senior flood forecasters’ experiences, recent hydrological changes related to human modifications [e.g. artificial subsurface drainage (SSD) expansion] and climate change are potential causes of notable forecasting failures over the past decade. My dissertation focuses on the operational and scientific gaps in current forecasting models and observational data and provides insights and value to both the practitioner and the research community. First, the current flood forecasting model needs both the location and installation timing of SSD and SSD physics. SSD maps were developed using satellite “big” data and a machine learning technique. Next, using the maps with a land surface model, the impacts of SSD expansion on regional hydrological changes were quantified. In combination with model physics, the inherent uncertainty in the airborne gamma snow survey observations hinders the accurate flood forecasting model. The operational airborne gamma snow water equivalent (SWE) measurements were improved by updating antecedent surface moisture conditions using satellite observations on soil moisture. From a long-term perspective, flood forecasters and state governments need knowledge of historical changes in snowpack and snowmelt to help flood management and to develop strategies to adapt to climate changes. However, historical snowmelt trends have not been quantified in the north-central U.S. due to the limited historical snow data. To overcome this, the current available historical long-term SWE products were evaluated across diverse regions and conditions. Using the most reliable SWE product, a trend analysis quantified the magnitude of change extreme snowpack and melt events over the past 36 years. Collectively, this body of research demonstrates that human and climate impacts, as well as limited and noisy data, cause uncertainties in flood prediction in the great plains, but integrated approaches using remote sensing, big data analytics, and modeling can quantify the hydrological changes and reduce the uncertainties. This dissertation improves the practice of flood forecasting in Red River of the North Basin and advances research in hydrology and snow science