Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC

Ebrahimi-Khusfi, Mohsen; Alavipanah, Seyed Kazem; Hamzeh, Saeid; Amiraslani, Farshad; Samany‬, Najmeh Neysani; Wigneron, Jean‐Pierre

doi:10.1016/j.jag.2017.12.005

Cited by 19 publications

(6 citation statements)

References 56 publications

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“…To date, numerous researchers have studied the accuracy of SMAP SM products across a wide range of geographic regions and climatic and environmental conditions around the world. Several studies compared the potential of SMAP in determining SM values with the performance of earlier passive microwave satellites such as SMOS, AMSR-E, and ASCAT [21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products

et al. 2022

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SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.

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Section: Introductionmentioning

confidence: 99%

Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products

et al. 2022

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“…As for indirect approaches, SM estimation from visible and infrared data is based on land surface reflectance at much higher spatial resolutions [6]. To evaluate SM estimation, there are many studies on the comparison of SM products and modeled SM on different scales [7][8][9][10][11][12]. In order to explore the potential of optical and thermal remote sensing imagery for SM estimation, different indices (e.g., VCI-vegetation condition index, TVDI-temperature vegetation dryness index, TCI-temperature condition index, ATIapparent thermal inertia) [13][14][15][16][17] and models (SVAT-soil vegetation atmosphere transfer, EF-evaporative fraction model) [18,19] have been applied to different climate conditions.…”

Section: Introductionmentioning

confidence: 99%

Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

Yuan

Zhang

et al. 2021

Remote Sensing

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Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results (R¯) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum R¯ of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 104 km2 and 667.44 × 104 km2 with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.

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“…SMAP mission provides a set of soil moisture products at different spatial scales through inversion of physical radiative transfer models [25]. The zeroth-order solution of the radiative transfer equation, known as τ-ω model was used to account for the vegetation effect on the brightness temperature [26,27]. In addition to brightness temperature, a number of ancillary data concerning the vegetation and soil characteristics such as effective soil temperature and vegetation water content were also required as inputs to generate the soil moisture products [28].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

et al. 2020

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This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R2OLS = 0.981, R2SVM = 0.943, R2RF = 0.983) and low RMSE (RMSEOLS = 0.016 cm3/cm3, RMSESVM = 0.047 cm3/cm3, RMSERF = 0.016 cm3/cm3). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R2OLS = 0.376~0.85, R2SVM = 0.376~0.814, R2RF = 0.39~0.854; RMSEOLS = 0.049~0.105 cm3/cm3, RMSESVM = 0.073~0.1 cm3/cm3, RMSERF = 0.047~0.102 cm3/cm3), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.

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Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC

Cited by 19 publications

References 56 publications

Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products

Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products

Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

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