Validation of SMAP Surface Soil Moisture Using In Situ Measurements in Diverse Agroecosystems Across Texas, US.
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| Title: | Validation of SMAP Surface Soil Moisture Using In Situ Measurements in Diverse Agroecosystems Across Texas, US. |
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| Authors: | Gurau, Sanjita1 (AUTHOR), Tefera, Gebrekidan W.1 (AUTHOR), Ray, Ram L.1 (AUTHOR) raray@pvamu.edu |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 7, p994. 23p. |
| Subjects: | Soil moisture, Cumulative distribution function, Soil moisture measurement, Agricultural ecology |
| Geographic Terms: | Texas |
| Abstract: | Highlights: What are the main findings? SMAP captures seasonal soil-moisture patterns across Texas, with performance varying by site due to rainfall, temperature, and land cover conditions. Quantile Delta Mapping and Empirical Cumulative Density Function bias correction methods substantially reduce RMSE in some stations, demonstrating the benefit of site-specific calibration. What is the implication of the main finding? It validated SMAP daily soil moisture across Texas climate zones and land covers. This is an approach to validate/calibrate satellite soil moisture against in situ measurements. Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP's performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R2), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R2 = 0.57, RMSE = 2.29%), followed by Austin (R2 = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R2 = 0.28, RMSE = 11.28%) and Kingsville (R2 = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? SMAP captures seasonal soil-moisture patterns across Texas, with performance varying by site due to rainfall, temperature, and land cover conditions. Quantile Delta Mapping and Empirical Cumulative Density Function bias correction methods substantially reduce RMSE in some stations, demonstrating the benefit of site-specific calibration. What is the implication of the main finding? It validated SMAP daily soil moisture across Texas climate zones and land covers. This is an approach to validate/calibrate satellite soil moisture against in situ measurements. Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP's performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R2), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R2 = 0.57, RMSE = 2.29%), followed by Austin (R2 = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R2 = 0.28, RMSE = 11.28%) and Kingsville (R2 = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18070994 |
