Near real time de-noising of satellite-based soil moisture retrievals: An intercomparison among three different techniques

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

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<i>Irrigation scheduling using real-time soil moisture data in corn production</i>

10.13031/aim.201701335 ◽

2017 ◽

Author(s):

Mar�a Zamora Re ◽

Michael D. Dukes

Keyword(s):

Soil Moisture ◽

Real Time ◽

Irrigation Scheduling ◽

Corn Production ◽

Soil Moisture Data

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Next generation system for real-time monitoring of rainfall, soil moisture, and soil temperature

2011 IEEE Sensors Applications Symposium ◽

10.1109/sas.2011.5739796 ◽

2011 ◽

Cited By ~ 5

Author(s):

Hamid Fahim Rezaei ◽

Nicholas Sitter ◽

Anton Kruger

Keyword(s):

Soil Moisture ◽

Soil Temperature ◽

Real Time ◽

Next Generation ◽

Real Time Monitoring ◽

Generation System

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Rancang Bangun Sistem Monitoring Lahan Pertanian Berbasis Mikrokontroler Arduino dan Mobile Web

El Sains : Jurnal Elektro ◽

10.30996/elsains.v2i1.4020 ◽

2020 ◽

Vol 2 (1) ◽

Author(s):

Emilyana Varida ◽

Arif Supriyanto ◽

Wiwik Kusrini ◽

Fathurahmani Fathur

Keyword(s):

Soil Moisture ◽

Real Time ◽

Mobile Web ◽

Moisture Sensor ◽

Soil Moisture Sensor

Saat ini para petani sayuran dalam menanam jenis sayuran masih menggunakan cara tradisional dan belum memperhatikan kondisi tanah yang sesuai untuk jenis sayuran yang akan mereka tanam, hal ini menyebabkan pertumbuhan sayuran menjadi kurang maksimal. Oleh karena itu diperlukan sebuah sistem yang mampu memberikan informasi kadar tanah dan memberikan rekomendasi jenis sayuran yang cocok untuk ditanami. Sistem yang akan dibangun berbasis mikrokontorler arduino, sensor soil moisture, sensor pH, dan modul ESP8266 yang digunakan untuk mengirimkan data hasil dari pembacaan sensor ke perangkat mobile secara real-time untuk diproses dan menghasilkan rekomendasi jenis sayuran yang cocok berdasarkan data hasil dari pembacaan sensor. Berdasarkan hasil pengujian, sistem ini telah mampu menampilkan kondisi kelembaban tanah, kadar pH dengan baik dan mampu merekomendasi jenis sayuran sesuai dengan kondisi hasil pembacaan sensor, sehingga harapannya dapat membantu para petani sayuran dalam menentukan jenis sayuran yang akan mereka tanam dan mampu meminimalisir resiko gagal panen. Kata Kunci: Arduino, soil moisture, pH, mobile web

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A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0285.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 837-843 ◽

Cited By ~ 15

Author(s):

Randal D. Koster ◽

Rolf H. Reichle ◽

Sarith P. P. Mahanama

Keyword(s):

United States ◽

Soil Moisture ◽

Real Time ◽

Surface Soil ◽

Data Driven ◽

Surface Soil Moisture ◽

Near Surface ◽

Time Estimates ◽

Data Driven Approach ◽

Global Surface

Abstract NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

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Application of Sap Flow Measurement in Real Time Soil Moisture Management

Critical Transitions in Water and Environmental Resources Management ◽

10.1061/40737(2004)487 ◽

2004 ◽

Author(s):

S. Takeuchi ◽

T. Yano

Keyword(s):

Soil Moisture ◽

Real Time ◽

Sap Flow ◽

Flow Measurement ◽

Moisture Management

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Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

Remote Sensing ◽

10.3390/rs11030368 ◽

2019 ◽

Vol 11 (3) ◽

pp. 368 ◽

Cited By ~ 4

Author(s):

Zhi Zhang ◽

Dagang Wang ◽

Guiling Wang ◽

Jianxiu Qiu ◽

Weilin Liao

Keyword(s):

Soil Moisture ◽

Real Time ◽

Mainland China ◽

Correction Method ◽

Cumulative Distribution ◽

Area Index ◽

Soil Moisture Data ◽

Precipitation Product ◽

Antecedent Precipitation ◽

Potential Factors

Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time.

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