Chandra D. Holifield Collins

Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

AbstractThe Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requiremen...

Development of an integrated multiplatform approach for assessing brush management conservation efforts in semiarid rangelands

Millions of dollars have been spent on brush management, or removal of unwanted woody vegetation, as a conservation practice to control the presence of woody species. Land managers need an inexpensive means of monitoring the effects of brush management conser- vation methods for decreasing degradation in rangeland systems. In this study, free, publically available, high-resolution (1 m) imagery from the National Agricultural Imagery Program (NAIP) and moderate-resolution (30 m) Landsat-5 Thematic Mapper (TM) imagery were com- bined to produce a large-scale technique for mapping woody cover. High-resolution imagery- based estimates of woody cover were found to be reasonable (RMSE ¼ 3.8%, MAE ¼ 2.9%) surrogates for ground-based woody cover. An equation for TM-derived woody cover was developed. TM scenes of woody cover (TMWC) were produced and validated using NAIP and ground-based data. Results showed that the developed relation produced viable (RMSE ¼ 8.5%, MAE ¼ 6.4%) maps of woody cover that could be used to successfully track the occurrence of brush removal, as well as monitor the presence or lack of subsequent reemergence. This work provides land managers with an operational means of determining where to allocate resources to implement brush management, as well as a cost-effective method of monitoring the effects of their efforts.

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m3 m−3 and 0.001 m3 m−3, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m3 m−3, but increased the root zone bias by 0.014 m3 m−3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to a skill degradation in other land surface variables.

Strategies for validating satellite soil moisture products using in situ networks: Lessons from the USDA-ARS watersheds

There are a variety of soil moisture station designs and networks deployed throughout the world, each with varying applications and uses. For the purpose of satellite validation of soil moisture products, a dense network of soil moisture networks are required with soil moisture sensors at the near surface (∼5 cm or less) to correspond to the satellite footprints and signals. The USDA-Agricultural Research Service operates a collection of soil moisture networks as a part of the Long Term Agro-ecosystem Research (LTAR) network to this end. These networks have been used to validate products from AMSR-E, SMOS, Aquarius, and SMAP. A review of these results and a synopsis of successful scaling strategies are discussed.