Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics

Zhang, Peng; Yu, Hongbo; Gao, Yibo; Zhang, Qiaofeng

doi:10.3390/su15119112

Cited by 3 publications

(1 citation statement)

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“…Through a comparison of in situ data and satellite data using multiple indicators, they found that the data's ubRMSE closely approached the product's mission requirement of 0.04 (cm 3 /cm 3 ). Moreover, Zhang et al [25] evaluated the global spatiotemporal accuracy characteristics of SMAP satellite soil moisture based on international ground-based soil moisture observations. The results indicated a high level of accuracy for the SMAP satellite soil moisture product.…”

Section: Introductionmentioning

confidence: 99%

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Zhu,

Tian,

et al. 2024

Remote Sensing

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Soil moisture (SM) is a crucial environmental variable, and it plays an important role in energy and water cycles. SM data retrieval based on microwave satellite remote sensing has garnered significant attention due to its spatial continuity, wide observational coverage, and relatively low cost. Validating the accuracy of satellite remote sensing SM products is a critical step in enhancing data credibility, which plays a vital role in ensuring the effective application of satellite remote sensing data across various fields. Firstly, this study focused on Henan Province and evaluated the accuracy of the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture (SPL3SMP_E) product along with its application in agriculture. The evaluation was based on in situ SM data from 55 stations in Henan Province. The assessment metrics used in this study include mean difference (MD), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the Pearson correlation coefficient (R). The time span of this study is from 2017 to 2020. The evaluation results indicated that the SPL3SMP_E soil moisture product performs well, as reflected by an ubRMSE value of 0.045 (m3/m3), which was relatively close to the product’s design accuracy of 0.04 (m3/m3). Moreover, the accuracy of the product was unaffected by temporal factors, but the product exhibited strong spatial aggregation, which was closely related to land use types. Then, this study explored the response of the SPL3SMP_E product to irrigation signals. The precipitation and irrigation data from Henan Province were employed to investigate the response of the SPL3SMP_E soil moisture product to irrigation. Our findings revealed that the SPL3SMP_E soil moisture product was capable of capturing over 70% of irrigation events in the study area, indicating its high sensitivity to irrigation signals in this region. In this study, the SPL3SMP_E product was also employed for monitoring agricultural drought in Henan Province. The findings revealed that the collaborative use of the SPL3SMP_E soil moisture product and machine learning algorithms proves highly effective in monitoring significant drought events. Furthermore, the integration of multiple indices demonstrated a notable enhancement in the accuracy of drought monitoring. Such an evaluation holds significant implications for the effective application of satellite remote sensing SM data in agriculture and other domains.

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Section: Introductionmentioning

confidence: 99%