Advances in soil moisture retrieval from remote sensing

Ning, 潘宁 Pan; Wang, Shuai; Yanxu, 刘焱序 Liu; Zhao, Wenwu; Bojie, 傅伯杰 Fu

doi:10.5846/stxb201804260949

Cited by 2 publications

(1 citation statement)

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“…Soil moisture is a relative variable in a certain region, which is expressed as the percentage of soil moisture content per unit volume to the net weight of the soil sample [1]. As an important element in the terrestrial surface system [2], soil moisture affects the ecological environment, water resource utilization, agricultural irrigation, and other processes, reflecting the interaction among the pedosphere, hydrosphere, atmosphere, and biosphere [3][4][5]. Soil moisture is closely related to soil conditions, water resource distribution, survival, and behavior of organisms.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Feng

Huang

Zhao³

et al. 2022

Sensors

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Satellite-based soil moisture products are suitable for large-scale regional monitoring due to the accessibility. Five soil moisture products including SMAP, ESA CCI, and AMSR2 (ascending, descending, and average) were selected in the continental United States (US) from 2016 to 2021. To evaluate the performance of the products and assess their applicability, ISMN (International Soil Moisture Network) data were used as the in situ measurement. PBIAS (Percentage of BIAS), R (Pearson correlation coefficient), RMSE (Root Mean Square Error), ubRMSE (unbiased RMSE), MAE (Mean Absolute Error), and MBE (Mean Bias Error) were selected for evaluation. The performance of five products over six observation networks and various land cover types was compared, and the differences were analyzed at monthly, seasonal, and annual scales. The results show that SMAP had the smallest deviation with the ISMN data because PBIAS was around −0.13, and MBE was around −0.02 m3/m3. ESA CCI performed the best in almost all aspects; its R reached around 0.7, and RMSE was only around 0.07 m3/m3 at the three time scales. The performance of the AMSR2 products varied greatly across the time scales, and increasing errors and deviations showed from 2016 to 2020. The PBO_H2O and USCRN networks could reflect soil moisture characteristics in the continental US, while iRON performed poorly. The evaluation of the networks was closely related to spatial distributions. All products performed better over grasslands and shrublands with R, which was greater than 0.52, and ubRMSE was around 0.1 m3/m3, while products performed worse over forests, where PBIAS was less than −0.62, and RMSE was greater than 0.2 m3/m3, except for ESA CCI. From the boxplot, SMAP was close to the ISMN data with differences less than 0.004 m3/m3 between the median and lower quartiles.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Feng

Huang

Zhao³

et al. 2022

Sensors

View full text Add to dashboard Cite

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Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index

Lu¹,

Zhao

Huang

et al. 2022

Sensors

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Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., >3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm3/cm3 versus 0.027 to 0.032 cm3/cm3 for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications.

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Advances in soil moisture retrieval from remote sensing

Cited by 2 publications

References 0 publications

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index

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