Irrigation Signals Detected From SMAP Soil Moisture Retrievals

Lawston, Patricia M.; Santanello, Joseph A.; Kumar, Sujay V.

doi:10.1002/2017gl075733

Cited by 130 publications

(104 citation statements)

References 26 publications

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“…Similar results have been found recently for specific study sites [65]. These 412 values showed explained variances >70% and RMSE values considerably below (~0.03 m 3 m -3 ) the 413 satellite soil moisture mean of 0.22 m 3 m -3 , which is suitable for many applications [62], such as the 414 detection of irrigation signals [66]. Our results obtained by the cross-validation strategy and ground 415 validation supports the application of a topography-based model to predict satellite soil moisture 416 estimates (Figure 4).…”

supporting

confidence: 80%

Downscaling Satellite Soil Moisture using Geomorphometry and Machine Learning

Guevara

Vargas

2019

Preprint

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13 Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of 14 soils to retain water and for predicting land and atmosphere interactions. The main source of soil 15 moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote 16 sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25-50 km grids); 17 and large areas from regional-to-global scales have spatial information gaps. We provide an alternative 18 approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial 19 resolution across areas where no information is otherwise available. This approach relies on 20 geomorphometry derived terrain parameters and machine learning models to improve the statistical 21 accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information 22 across the conterminous United States on an annual basis . We derived 15 primary and 23 secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm 24 (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and 25 annual satellite soil moisture estimates were used to train the models. The explained variance for all 26 models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the 27 original satellite soil moisture estimates) had higher correlations with field soil moisture observations 28 from the North American Soil Moisture Database (n=668 locations with available data between 1991-29 2013; 0-5cm depth) than the original product. We conclude that the fusion of geomorphometry 30 methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of 31 satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture 32 estimates and regions across the world. 33 3 34

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supporting

confidence: 80%

Downscaling Satellite Soil Moisture using Geomorphometry and Machine Learning

Guevara

Vargas

2019

Preprint

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“…Figure 4a-c shows the difference in topmost 5-cm soil moisture between the irrigated and unirrigated grid cells based on the SMAP observations during summer. For most of the irrigated areas (except a small part of eastern Nebraska in the United States and Bihar in India), there is relatively high soil moisture in the irrigated grids, which agrees with the previous study using the same data but a different detection metric (Lawston et al, 2017), indicating irrigation activities occur during this season and our detecting algorithm is valid.…”

Section: Difference In Soil Moisture and Ndvimentioning

confidence: 99%

Global observed and modelled impacts of irrigation on surface temperature

Chen

Dirmeyer

2019

Intl Journal of Climatology

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Agricultural irrigation has significant potential for altering local climate by reducing soil albedo, increasing evapotranspiration, and enabling greater leaf area. Numerous studies using regional or global climate models have demonstrated the cooling effects of irrigation on mean and extreme temperature, especially over regions where irrigation is extensive. However, these model‐based results have not been well validated due to the limitations of observational data sets. In this study, multiple satellite‐based products, including the Moderate Resolution Imaging Spectroradiometer (MODIS) and Soil Moisture Active Passive (SMAP) data sets, are used to isolate and quantify the local impacts of irrigation on surface climate over irrigated regions, which are derived from the Global Map of Irrigation Areas (GMIA). The relationships among soil moisture, albedo, evapotranspiration, and surface temperature are explored. Strong evaporative cooling by irrigation lowers daytime surface temperature over arid and semi‐arid regions, such as California's Central Valley, the Great Plains, central Asia, and northwestern India. However, the cooling effects are less evident in areas of eastern China and the Lower Mississippi River Basin despite extensive irrigation over these regions. Results are also compared with irrigation experiments using the Community Earth System Model (CESM) to assess the model's ability to represent land–atmosphere interactions in regards to irrigation. CESM greatly underestimates the surface temperature response to irrigation. The comparison between the offline and coupled simulations suggests that the irrigation‐induced cooling can be regulated by the interactions between land surface and atmosphere, and amplified signals are found over the “hot spot” regions. Meanwhile, model resolution can also influence the magnitude of the local cooling by irrigation.

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“…Contrary to the warm season, southern California shows a high a correlation with VIC during the cold season, at around 0.9. We attribute this change from cold season to warm season in southern and southern-central California to the irrigation that SMAP picks up (Lawston et al, 2017) but VIC does not, since the version used here does not have water management effects. A land use and land cover map shows that about one-third of these areas are irrigated vegetation and another third are forests and woodlands (USGS, 2018).…”

Section: Combination Filtersmentioning

confidence: 99%

Developing a drought-monitoring index for the contiguous US using SMAP

Sadri

Wood

Pan

2018

Hydrol. Earth Syst. Sci.

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Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044 • N/S) using an L-band (1.4 GHz) microwave radiometer in 2-3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼ 36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼ 9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May-October) and cold (November-April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov-Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥ 0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters -which also gives the best coverage of the grids throughout CONUS -was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0-D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.

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Irrigation Signals Detected From SMAP Soil Moisture Retrievals

Cited by 130 publications

References 26 publications

Downscaling Satellite Soil Moisture using Geomorphometry and Machine Learning

Downscaling Satellite Soil Moisture using Geomorphometry and Machine Learning

Global observed and modelled impacts of irrigation on surface temperature

Developing a drought-monitoring index for the contiguous US using SMAP

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