Performance Evaluation of UAVSAR and Simulated NISAR Data for Crop/Noncrop Classification Over Stoneville, MS.

Synthetic Aperture Radar (SAR) data are well-suited for change detection over agricultural fields, owing to high spatiotemporal resolution and sensitivity to soil and vegetation. The goal of this work is to evaluate the science algorithm for the NASA ISRO SAR (NISAR) Cropland Area product using data collected by NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) platform and the simulated NISAR data derived from it. This study uses mode 129, which is to be used for global-scale mapping. The mode consists of an upper (129A) and lower band (129B), respectively having bandwidths of 20 and 5 MHz. This work uses 129A data because it has a four times finer range resolution compared to 129B. The NISAR algorithm uses the coefficient of variation (CV) to perform crop/noncrop classification at 100 m. We evaluate classifications using three accuracy metrics (overall accuracy, J-statistic, Cohen's Kappa) and spatial resolutions (10, 30, and 100 m) for crop/noncrop delineating CV thresholds (CVthr) ranging from 0 to 1 in 0.01 increments. All but the 10 m 129A product exceeded NISAR's mission accuracy requirement of 80%. The UAVSAR 10 m data performed best, achieving maximum overall accuracy, J-statistic, and Kappa values of 85%, 0.62, and 0.60. The same metrics for the 129A product respectively are: 77%, 0.40, 0.36 at 10 m; 81%, 0.55, 0.49 at 30 m; 80%, 0.58, 0.50 at 100 m. We found that using a literature recommended CVthr value of 0.5 yielded suboptimal accuracy (65%) at this site and that optimal CVthr values monotonically decreased with decreasing spatial resolution.

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.

Multi-time scale analysis of the spatial representativeness of in situ soil moisture data within satellite footprints.

We conduct a novel comprehensive investigation that seeks to prove the connection between spatial and time scales in surface soil moisture (SM) within the satellite footprint (~50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at time scales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per time-scale basis by comparison to large-spatial scale datasets (the in situ spatial average, SMOS, AMSR2 and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), the percentage of correlated areas (CArea) and a new proposed approach that uses wavelet-based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the time scale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero-crossings and TC is suitable for week and month scales but not for other scales where datasets cross-correlations are found low. In contrast, WCor and CArea give consistent results at all time-scales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (1 station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks.

Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions.

Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp., in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ soil moisture data sets, it has not been feasible to perform a direct analysis of the relationships between soil moisture levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ soil moisture gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower soil moisture in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to soil moisture levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location.

Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates.

SMAP (Soil Moisture Active and Passive) radiometer observations at ~40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9-km SMAP Level-4 Soil Moisture product. This study demonstrates that adding high-resolution radar observations from Sentinel-1 to the SMAP assimilation can increase the spatio-temporal accuracy of soil moisture estimates. Radar observations were assimilated either separately from or simultaneously with radiometer observations. Assimilation impact was assessed by comparing 3-hourly, 9-km surface and root-zone soil moisture simulations with in situ measurements from 9-km SMAP core validation sites and sparse networks, from May 2015 to December 2016. The Sentinel-1 assimilation consistently improved surface soil moisture, whereas root-zone impacts were mostly neutral. Relatively larger improvements were obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performed best, demonstrating the complementary value of radar and radiometer observations.