Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S.

Ray, Ram L.; Fares, Ali; He, Yiping; Temimi, Marouane

doi:10.3390/w9060372

Cited by 33 publications

(17 citation statements)

References 61 publications

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“…Time series in Figure 3 show that AMSR2 tended to overestimate SSM with a positive bias in all stations. This result is also confirmed by previous studies [57,58]. However, in stations with lower moisture values (Toroq and Khosroshah), AMSR2 provided more precise description of local fluctuations of SM.…”

Section: Evaluation Of Remotely-sensed Datasupporting

confidence: 90%

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

et al. 2019

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Monitoring Surface Soil Moisture (SSM) and Root Zone Soil Moisture (RZSM) dynamics at the regional scale is of fundamental importance to many hydrological and ecological studies. This need becomes even more critical in arid and semi-arid regions, where there are a lack of in situ observations. In this regard, satellite-based Soil Moisture (SM) data is promising due to the temporal resolution of acquisitions and the spatial coverage of observations. Satellite-based SM products are only able to estimate moisture from the soil top layer; however, linking SSM with RZSM would provide valuable information on land surface-atmosphere interactions. In the present study, satellite-based SSM data from Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) are first compared with the few available SM in situ observations, and are then coupled with the Soil Moisture Analytical Relationship (SMAR) model to estimate RZSM in Iran. The comparison between in situ SM observations and satellite data showed that the SMAP satellite products provide more accurate description of SSM with an average correlation coefficient (R) of 0.55, root-mean-square error (RMSE) of 0.078 m 3 m −3 and a Bias of 0.033 m 3 m −3 . Thereafter, the SMAP satellite products were coupled with SMAR model, providing a description of the RZSM with performances that are strongly influenced by the misalignment between point and pixel processes measured in the preliminary comparison of SSM data.Hydrology 2019, 6, 44 2 of 13 emissions are widely investigated in many researches due to their potentials for monitoring SM in all temporal and meteorological conditions and the infiltration ability of microwave emissions in sparse vegetation covers. This method functions based on the high level of difference between the soil and water dielectric constants. Solar illumination and cloud cover do not influence microwave remote sensing technique, and its longer wavelengths are not susceptible to atmospheric scattering. Thus, it was considered as the most effective method in remote sensing of SM [11,13]. In recent years, the surface-reflected Global Navigation Satellite System (GNSS) signals have also been evaluated for SM estimations, which applies a different source of signals from the active/passive microwave sensors to observe the Earth's surface [14]. Moreover, the Advanced Scatterometer (ASCAT), which is an active microwave remote sensing instrument, provides global SM data sets derived from the backscatter measurements [15,16].SM products obtained from active/passive microwave remotely-sensed data have been applied in wide spectra of contexts [17][18][19][20][21][22][23][24][25][26]. However, SM data derived from most of the satellite sources provide the near surface moisture that needs to be converted in Root Zone Soil Moisture (RZSM) estimations [27,28]. Recently, high-resolution observations of RZSM have become available through the NASA Airborne Microwave Observatory of Subcanopy and Subsurfac...

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Section: Evaluation Of Remotely-sensed Datasupporting

confidence: 90%

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

et al. 2019

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“…The state's lowest elevation, 0 m asl, is on the coast, with the highest elevation, 2655 m asl, in the north. The Texas landscape is dominated by sandy loam (24.5%), loam (20.1%), clay (17.4%), and clay loam (16.3%) soil types, whereas 21.7% of the state landscape has sand, loamy sand, silty clay loam, and other soil types (Ray et al, 2017). Texas relies significantly on irrigation for its 2.50 million ha of irrigated lands, 86% of which is irrigated using groundwater (Wagner, 2012).…”

Section: Study Regionmentioning

confidence: 99%

Effects of Drought on Crop Production and Cropping Areas in Texas

Ray

Fares

Risch

2018

Agricultural & Env Letters

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100

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Core Ideas Drought causes significant yield reductions both for rainfed and irrigated crops. Drought can have impact on cropping areas and crop yield. Changing crop types can be used to cope with drought challenges during drought periods. Increased crop yield is required to meet the needs of future population growth, but drought causes significant yield reductions for rainfed and irrigated crops. This study evaluates the impact of drought on crop yield and cropping area over 10 climate zones in Texas from 2008 to 2016. It also depicts the spatiotemporal distribution of crop yield and cropping area changes at each climate zone across the state. We analyzed the impact of drought on crop yields and cropping areas before and after the 2011 severe drought using annual crop yields of four major crops. Results show that drought had a greater impact on winter wheat (Triticum aestivum L.) and corn (Zea mays L.) and lesser impact on cotton (Gossypium spp.) and sorghum [Sorghum bicolor (L.) Moench] production across Texas. Cotton and corn hectarages were reduced during the drought period and increased after that, whereas winter wheat hectarage was reduced in the northern climate zones and increased in the southern climate zones before the drought. Results also indicate that drought impact on crop production may be reduced by replacing water‐demanding crops such as corn with drought‐tolerant crops such as sorghum and expanding irrigation hectarage during drought periods. It may be beneficial for Texas agricultural production to increase the hectarage of sorghum and other grains especially during drought periods. This study provides valuable information that can be used to adopt appropriate measures to cope with future drought challenges in drought‐prone regions.

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“…Ray, et al [34] evaluated four remotely sensed soil moisture products, derived from Advanced Microwave Scanning Radiometer-Earth (AMSR-E), Soil Moisture Ocean Salinity (SMOS), AMSR2 and Soil Moisture Active Passive (SMAP), with in situ measurements over the state of Texas in the United States. The evaluation results highlight the best performance of SMAP product among others.…”

Section: Summary Of the Special Issuementioning

confidence: 99%

Recent Advances in Soil Moisture Estimation from Remote Sensing

Peng

Loew

2017

Water

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Monitoring soil moisture dynamics from local to global scales is essential for a wide range of applications. The field of remote sensing of soil moisture has expanded greatly and the first dedicated soil moisture satellite missions (SMOS, SMAP) were launched, and new missions, such as SENTINEL-1 provide long-term perspectives for land surface monitoring. This special issue aims to summarize the recent advances in soil moisture estimation from remote sensing, including recent advances in retrieval algorithms, validation, and applications of satellite-based soil moisture products. Contributions in this special issue exploit the estimation of soil moisture from both microwave remote sensing data and thermal infrared information. The validation of satellite soil moisture products can be very challenging, due to the different spatial scales of in situ measurements and satellite data. Some papers present validation studies to quantify soil moisture uncertainties. On the other hand, soil moisture downscaling schemes and new methods for soil moisture retrieval from GPS are also addressed by some contributions. Soil moisture data are used in fields like agriculture, hydrology, and climate sciences. Several studies explore the use of soil moisture data for hydrological application such as runoff prediction.

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Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S.

Cited by 33 publications

References 61 publications

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

Effects of Drought on Crop Production and Cropping Areas in Texas

Recent Advances in Soil Moisture Estimation from Remote Sensing

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