Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept

2021 ◽  
Vol 245 ◽  
pp. 106632
Author(s):  
Renkuan Liao ◽  
Shirui Zhang ◽  
Xin Zhang ◽  
Mingfei Wang ◽  
Huarui Wu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Proof Of Concept ◽  
Irrigation Systems ◽  
Soil Moisture Data

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

2019 ◽  
Vol 11 (3) ◽  
pp. 368 ◽  
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.

The International Soil Moisture Network in assistance of EO soil moisture validation products, services and models

2020 ◽  
Author(s):  
Daniel Aberer ◽  
Irene Himmelbauer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Wouter Dorigo ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Measurement Techniques ◽  
European Space Agency ◽  
Web Portal ◽  
Soil Moisture Data ◽  
Spatial Coverage

<p>The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models. </p><p>In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.</p><p>The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.</p><p>Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community > 3200 registered users strong. Time series with hourly timestamps from 1952 – up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (> 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).</p><p>About 10’000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.</p><p>In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.</p>

scholarly journals Hydrological real-time modelling in the Zambezi river basin using satellite-based soil moisture and rainfall data

2011 ◽  
Vol 15 (3) ◽  
pp. 999-1008 ◽  
Author(s):  
P. Meier ◽  
A. Frömelt ◽  
W. Kinzelbach
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
River Basin ◽  
Lead Time ◽  
Time Lag ◽  
Soil Storage ◽  
Soil Moisture Data ◽  
Modelling Framework ◽  
Zambezi River Basin ◽  
Zambezi River

Abstract. Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.

Near‐real‐time one‐kilometre Soil Moisture Active Passive soil moisture data product

2020 ◽  
Vol 34 (21) ◽  
pp. 4083-4096
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu ◽  
Hamid Moradkhani ◽  
Li Fang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Soil Moisture Data ◽  
Data Product

scholarly journals Design of a Real Time Feedback Automation Irrigation System

2019 ◽  
Vol 4 (12) ◽  
pp. 117-120
Author(s):  
O'tega Ejofodomi ◽  
Godswill Ofualagba
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Real Time ◽  
Network Optimization ◽  
Soil Moisture Content ◽  
Irrigation System ◽  
Water Source ◽  
Irrigation Systems ◽  
Optimization Software ◽  
Control Units

This paper presents the design of a real time feedback control automated irrigation systems that consists of monitoring units, control units, irrigation pipeline valves, and a network of irrigation pipeline. The monitoring units continuously measure the soil moisture content in the irrigation blocks, and if the moisture content drops below a predetermined threshold for the particular crop under production, it sends a wireless message to the control units controlling the pipeline valves along the water flow channel, causing the control units to open the valves leading to the water source and commencing automatic irrigation. When the moisture content rises above a predetermined threshold for the crop, the monitoring units sends a wireless message to the control units, causing them to close the pipeline valves and cease automatic irrigation. An automatic irrigation system pipeline network optimization software has also been designed to plan, cost, and design the automatic irrigation system for a piece of land prior to installation.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Real-time SARS-CoV-2 diagnostic and variants tracking over multiple candidates using nanopore DNA sequencing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
François Stüder ◽  
Jean-Louis Petit ◽  
Stefan Engelen ◽  
Marco Antonio Mendoza-Parra
Keyword(s):  
Real Time ◽  
Optimal Solution ◽  
Proof Of Concept ◽  
Gene Encoding ◽  
Spike Gene ◽  
Long Period ◽  
The World ◽  
Receptor Recognition ◽  
Nanopore Dna Sequencing ◽  
Novel Coronavirus

AbstractSince December 2019, a novel coronavirus responsible for a severe acute respiratory syndrome (SARS-CoV-2) is accountable for a major pandemic situation. The emergence of the B.1.1.7 strain, as a highly transmissible variant has accelerated the world-wide interest in tracking SARS-CoV-2 variants’ occurrence. Similarly, other extremely infectious variants, were described and further others are expected to be discovered due to the long period of time on which the pandemic situation is lasting. All described SARS-CoV-2 variants present several mutations within the gene encoding the Spike protein, involved in host receptor recognition and entry into the cell. Hence, instead of sequencing the whole viral genome for variants’ tracking, herein we propose to focus on the SPIKE region to increase the number of candidate samples to screen at once; an essential aspect to accelerate diagnostics, but also variants’ emergence/progression surveillance. This proof of concept study accomplishes both at once, population-scale diagnostics and variants' tracking. This strategy relies on (1) the use of the portable MinION DNA sequencer; (2) a DNA barcoding and a SPIKE gene-centered variant’s tracking, increasing the number of candidates per assay; and (3) a real-time diagnostics and variant’s tracking monitoring thanks to our software RETIVAD. This strategy represents an optimal solution for addressing the current needs on SARS-CoV-2 progression surveillance, notably due to its affordable implementation, allowing its implantation even in remote places over the world.

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