36 research outputs found
Global downscaling of remotely sensed soil moisture using neural networks
Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e.,
of the order of 1 km) is necessary in order to quantify its role in regional
feedbacks between the land surface and the atmospheric boundary layer.
Moreover, several applications such as agricultural management can benefit
from soil moisture information at fine spatial scales. Soil moisture
estimates from current satellite missions have a reasonably good temporal
revisit over the globe (2–3-day repeat time); however, their finest spatial
resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has
estimated soil moisture at two different spatial scales of 36 and 9 km since
April 2015. In this study, we develop a neural-network-based downscaling
algorithm using SMAP observations and disaggregate soil moisture to 2.25 km
spatial resolution. Our approach uses the mean monthly Normalized Differenced
Vegetation Index (NDVI) as ancillary data to quantify the subpixel
heterogeneity of soil moisture. Evaluation of the downscaled soil moisture
estimates against in situ observations shows that their accuracy is better
than or equal to the SMAP 9 km soil moisture estimates.</p
Data Assimilation to Extract Soil Moisture Information From SMAP Observations
Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach
Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence
A new global estimate of surface turbulent fluxes,
latent heat flux (LE) and sensible heat flux (H), and gross primary
production (GPP) is developed using a machine learning approach informed by
novel remotely sensed solar-induced fluorescence (SIF) and other radiative
and meteorological variables. This is the first study to jointly retrieve LE,
H, and GPP using SIF observations. The approach uses an artificial neural
network (ANN) with a target dataset generated from three independent data
sources, weighted based on a triple collocation (TC) algorithm. The new
retrieval, named Water, Energy, and Carbon with Artificial Neural Networks
(WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at
1°  ×  1° spatial resolution and at monthly time
resolution. The quality of ANN training is assessed using the target data,
and the WECANN retrievals are evaluated using eddy covariance tower estimates
from the FLUXNET network across various climates and conditions. When compared to
eddy covariance estimates, WECANN typically outperforms other products,
particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals
across three extreme drought and heat wave events demonstrates the capability
of the retrievals to capture the extent of these events. Uncertainty
estimates of the retrievals are analyzed and the interannual variability in
average global and regional fluxes shows the impact of distinct climatic
events – such as the 2015 El Niño – on surface turbulent fluxes and
GPP
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
A Global Spatially Contiguous Solar-Induced Fluorescence (CSIF) Dataset Using Neural Networks
Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05 4-day) resolutions during the MODIS era, one for clear-sky conditions (20002017) and the other one in all-sky conditions (20002016). The clear-sky instantaneous CSIF (CSIF(sub clear-inst)) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIF(sub all-daily)) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIF(sub yield), the ratio between OCO-2 SIF and CSIF(sub clear-inst) can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIF(sub all-daily) with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPPSIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPPSIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate
Urine-based enzyme-linked immunosorbent assay for the detection of Helicobacter pylori antibodies in children
ASSESSING URBAN DROUGHTS IN A SMART CITY FRAMEWORK
This study aims to integrate environmental data for drought monitoring to reduce uncertainty in urban drought characterization as part
of the smart city framework. Currently, drought monitoring in urban areas is a challenge. This is due, in part, to a lack of knowledge
on the subject of urban droughts and urban drought vulnerability. A critical part to assessing urban drought and implementing the
necessary policies is determining drought conditions. Often the timing and severity of the drought can leave cities to enforce water
restrictions, so accuracy of this determination has socioeconomic implications. To determine drought conditions, we need to know the
water balance over the urban landscape, of which evapotranspiration (ET) is a key variable. However, ET data and models have high
uncertainty when compared to other hydrological variables (i.e., precipitation). This is largely due to ill-defined empirical models for
characterizing the urban surface resistance parameter (rs) that is used in ET calculations. We propose a method to estimate rs values
using a combination of the Surface Temperature Initiated Closure (STIC) method that calculates regional evapotranspiration data and
an inverted version of the Penman-Monteith equation. We use this approach across the region surrounding Indianapolis, IN (USA)
from 2010-2014. We discuss the potential for this method to be integrated in to smart city framework to improve urban drought
assessment