C. D. Holifield Collins

A derivation of roughness correlation length for parameterizing radar backscatter models

Surface roughness is a key parameter of radar backscatter models designed to retrieve surface soil moisture (θS) information from radar images. This work offers a theory‐based approach for estimating a key roughness parameter, termed the roughness correlation length (L c). The L c is the length in centimetres from a point on the ground to a short distance for which the heights of a rough surface are correlated with each other. The approach is based on the relation between L c and h RMS as theorized by the Integral Equation Model (IEM). The h RMS is another roughness parameter, which is the root mean squared height variation of a rough surface. The relation is calibrated for a given site based on the radar backscatter of the site under dry soil conditions. When this relation is supplemented with the site specific measurements of h RMS, it is possible to produce estimates of L c. The approach was validated with several radar images of the Walnut Gulch Experimental Watershed in southeast Arizona, USA. Results showed that the IEM performed well in reproducing satellite‐based radar backscatter when this new derivation of L c was used as input. This was a substantial improvement over the use of field measurements of L c. This new approach also has advantages over empirical formulations for the estimation of L c because it does not require field measurements of θS for iterative calibration and it accounts for the very complex relation between L c and h RMS found in heterogeneous landscapes. Finally, this new approach opens up the possibility of determining both roughness parameters without ancillary data based on the radar backscatter difference measured for two different incident angles.

Runoff and erosional responses to a drought‐induced shift in a desert grassland community composition

In contrast, measurements on small runoff plots on the hillslopes of the same watershed showed a significant increase in sediment discharge that continued after E. lehmanniana replaced native grasses. Together, these findings suggest alteration in plant community increased sediment yield but that hydrological responses to this event differ at watershed and plot scales, highlighting the geomorphological controls at the watershed scale that determine sediment transport efficiency and storage. Resolving these scalar issues will help identify critical landform features needed to preserve watershed integrity under changing climate conditions.

A remote sensing approach for estimating distributed daily net carbon dioxide flux in semiarid grasslands

[1] Semiarid systems compose a significant portion of the world’s terrestrial area and may play an important role in the global carbon cycle. A model was developed using the relation between surface reflectance and temperature obtained from satellite imagery to determine a Water Deficit Index (WDI) that estimated distributed plant transpiration, and by extension carbon dioxide (CO2) flux, for a point in time. Relationships were developed to scale these instantaneous flux measurements up to daytime estimates, which were then used to obtain measures of nighttime flux. Satellite images were acquired for a 5-year period (1996–2000) during which transpiration and net CO2 flux were measured for a semiarid grassland site in southeastern Arizona. Manual and automatic chamber data were also collected at the same site during the monsoon growing seasons of 2005 and 2006 and used to develop the relationship between and daytime and nighttime CO2 flux. Strong linear relationships were found between WDI-derived instantaneous and daytime net CO2 flux estimates (R 2 = 0.97), and between daytime and nighttime fluxes (R 2 = 0.88). These relations were used to generate maps of distributed total daily net CO2 flux. The error for the model was within the range of error inherent in the data sets used to create it and remained reasonable when used with WDI values less than 0.9. This study demonstrated that remote sensing can offer a physically based means of obtaining daily net CO2 flux in semiarid grasslands.

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 m3m-3, 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m3m-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.

Development and validation of the SMAP enhanced passive soil moisture product

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 m3/m3 at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 m3/m3. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.