Soil Moisture Active Passive (SMAP) Calibration and Validation Plan and Current Activities

The primary objective of the SMAP calibration and validation (Cal/Val) program is demonstrating that the science requirements (product accuracy and bias) have been met over the mission life. This begins during pre-launch with activities that contribute to high quality products and establishing post-launch validation infrastructure and continues through the mission life. However, the major focus is on a relatively short Cal/Val period following launch. The general approach and elements of the SMAP Cal/Val plan will be described and along with details on several ongoing or recent field experiments designed to address both near- and long-term Cal/Val

A panel analysis of UK industrial company failure

We examine the failure determinants for large quoted UK industrials using a panel data set comprising 539 firms observed over the period 1988-93. The empirical design employs data from company accounts and is based on Chamberlain’s conditional binomial logit model, which allows for unobservable, firm-specific, time-invariant factors associated with failure risk. We find a noticeable degree of heterogeneity across the sample companies. Our panel results show that, after controlling for unobservables, lower liquidity measured by the quick assets ratio, slower turnover proxied by the ratio of debtors turnover, and profitability were linked to the higher risk of insolvency in the analysis period. The findings appear to support the proposition that the current cash-flow considerations, rather than the future prospects of the firm, determined company failures over the 1990s recession

Seasonal Dependence of SMAP Radiometer-Based Soil Moisture Performance as Observed over Core Validation Sites

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. This study compares the SMAP and core validation site (CVS) soil moisture values over a period of three years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product

Heads I win, tails you lose? A career analysis of executive pay and corporate performance

The paper adopts a novel career perspective to examine theories of corporate control in the context of executive pay. Detailed career histories of boardroom executives in all FTSE 350 companies between 1996 and 2008 are utilised. The paper highlights the failure of existing arrangements to adjust pay outcomes where career performance is poor. The leading theoretical reasons for this disconnect, namely managerial power and neoinstitutionalism, are not consistent with the data. The paper identifies a settling-up process at work, whereby pay is adjusted in the light of both past pay and past performance. From a policy perspective, a case is made for adopting a cumulative or career-oriented approach to rewarding executive performance through the use of truly long-term incentives in the form of 'career shares'

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

Governance, regulation and financial market instability: the implications for policy

Just as the 1929 Stock Market Crash discredited Classical economic theory and policy and opened the way for Keynesianism, a consequence of the collapse of confidence in financial markets and the banking system—and the effect that this has had on the global macro economy—is currently discrediting the ‘conventional wisdom’ of neo-liberalism. This paper argues that at the heart of the crisis is a breakdown in governance that has its roots in the co-evolution of political and economic developments and of economic theory and policy since the 1929 Stock Market Crash and the Great Depression that followed. However, while many are looking back to the Great Depression and to the theories and policies that seemed to contribute to recovery during the first part of the twentieth century, we argue that the current context is different from the earlier one; and there are more recent events that may provide better insight into the causes and contributing factors giving rise to the present crisis and to the implications for theory and policy that follow

The International Soil Moisture Network:Serving Earth system science for over a decade

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository

Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones