Evaluation of Satellite and Reanalysis Soil Moisture Products over Southwest China Using Ground-Based Measurements

Peng, Jian; Niesel, Jonathan; Loew, Alexander; Zhang, Shiqiang; Wang, Jie

doi:10.3390/rs71115729

Cited by 99 publications

(85 citation statements)

References 68 publications

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“…They concluded that the CCI SM product correlates well with in situ observations with average R of 0.60. Furthermore, the trend of CCI SM highly agrees with the trends of precipitation and vegetation vigor from various reanalysis products (Albergel et al, 2012;Peng et al, 2015). Therefore, the CCI merged soil moisture is selected in our study to explore the possibility and potential of downscaling to high spatial resolution with optical/thermal infrared data.…”

Section: Esa CCI Soil Moisturementioning

confidence: 88%

Evaluation of soil moisture downscaling using a simple thermal-based proxy – the REMEDHUS network (Spain) example

Peng

Niesel

Loew

2015

Hydrol. Earth Syst. Sci.

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Abstract. Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought prediction. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple vegetation temperature condition index (VTCI) downscaling scheme over a dense soil moisture observational network (REMEDHUS) in Spain. First, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography, and land cover heterogeneity, using data from Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG SEVIRI (METEOSAT Second Generation -Spinning Enhanced Visible and Infrared Imager). Then the downscaling scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintaining the accuracy of CCI soil moisture. The accuracy level is comparable to other downscaling methods that were also validated against the REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying a geostationary satellite for downscaling soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI downscaling method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.

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Section: Esa CCI Soil Moisturementioning

confidence: 88%

Evaluation of soil moisture downscaling using a simple thermal-based proxy – the REMEDHUS network (Spain) example

Peng

Niesel

Loew

2015

Hydrol. Earth Syst. Sci.

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“…In addition, since the performance of satellites was not consistent in this study region, it is, therefore, important to investigate SM data from ascending and descending passes to confirm which pass of a particular satellite has better performance. Other researchers (e.g., [36,[61][62][63]) have pointed out that both day-and night-time retrievals have some advantages and disadvantages, as diurnal variations might have a negative impact on the accuracy during the day and isothermal conditions at night might reduce the errors caused by surface temperature variations.…”

Section: Discussionmentioning

confidence: 99%

“…However, limited studies have been conducted in spatial and temporal heterogeneities in the state of Texas (e.g., [34,35]). Given the region's physical heterogeneity, it is necessary to evaluate satellite soil moisture products before their use in practical applications in Texas [36] or other places with similar heterogeneity. It is not necessarily the case that satellite SM products that have performed well in one region will perform well in other regions [9].…”

Section: Introductionmentioning

confidence: 99%

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

Ray

Fares

et al. 2017

Water

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Abstract:The main goal of this study was to evaluate four major remote sensing soil moisture (SM) products over the state of Texas. These remote sensing products are: (i) the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) (2002( -September 2011; (ii) the Soil Moisture Ocean Salinity system (SMOS, 2010-present); (iii) AMSR2 (2012-present); and (iv) the Soil Moisture Active Passive system (SMAP, 2015-present). The quality of the generated SM data is influenced by the accuracy and precision of the sensors and the retrieval algorithms used in processing raw data. Therefore, it is important to evaluate the quality of these satellite SM products using in situ measurements and/or by inter-comparing their data during overlapping periods. In this study, these two approaches were used where we compared each satellite SM product to in situ soil moisture measurements and we also conducted an inter-comparison of the four satellite SM products at 15 different locations in Texas over six major land cover types (cropland, shrub, grassland, forest, pasture and developed) and eight climate zones along with in situ SM data from 15 Mesonet, USCRN and USDA-NRCS Scan stations. Results show that SM data from SMAP had the best correlation coefficients range from 0.37 to 0.92 with in situ measurements among the four tested satellite surface SM products. On the other hand, SM data from SMOS, AMSR2 and AMSR-E had moderate to low correlation coefficients ranges with in situ data, respectively, from 0.24-0.78, 0.07-0.62 and 0.05-0.52. During the overlapping periods, average root mean square errors (RMSEs) of the correlations between in situ and each satellite data were 0.13 (AMSR-E) and 0.13 (SMOS) cm 3 /cm 3 (2010-2011), 0.16 (AMSR2) and 0.14 (SMOS) cm 3 /cm 3 (2012-2016) and 0.13, 0.16, 0.14 (SMAP, AMSR2, SMOS) cm 3 /cm 3 (2015-2016), respectively. Despite the coarser spatial resolution of all four satellite products (25-36 km), their SM measurements are considered reasonable and can be effectively used for different applications, e.g., flood forecasting, and drought prediction; however, further evaluation of each satellite product is recommended prior to its use in practical applications.

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“…In particular, soil moisture retrieval from microwave observations has been highly advanced and several global soil moisture products have been produced [10,11], such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) [12], the advanced scatterometer (ASCAT) [13], the Soil Moisture and Ocean Salinity (SMOS) [14,15], the Soil Moisture Active Passive (SMAP) [16], and the European Space Agency's Climate Change Initiative (ESA CCI) soil moisture products [17,18]. Extensive validation studies, either through direct comparison against in-situ measurements, or intercomparison among different remotely sensed products, have been conducted to quantify the accuracy of these soil moisture products [19][20][21][22][23]. In addition, the applications of these products have also been widely explored in many fields like agriculture, hydrology, and climate sciences [24][25][26][27].…”

Section: Introductionmentioning

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 of Satellite and Reanalysis Soil Moisture Products over Southwest China Using Ground-Based Measurements

Cited by 99 publications

References 68 publications

Evaluation of soil moisture downscaling using a simple thermal-based proxy – the REMEDHUS network (Spain) example

Evaluation of soil moisture downscaling using a simple thermal-based proxy – the REMEDHUS network (Spain) example

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

Recent Advances in Soil Moisture Estimation from Remote Sensing

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