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

2017 ◽  
Author(s):  
Mar�a Zamora Re ◽  
Michael D. Dukes
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Irrigation Scheduling ◽  
Corn Production ◽  
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.

scholarly journals Development and integration of soil moisture sensor with drip system for precise irrigation scheduling through mobile phone

2016 ◽  
Vol 8 (4) ◽  
pp. 1959-1965 ◽  
Author(s):  
Jitendra Kumar ◽  
Neelam Patel ◽  
T. B. S. Rajput
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Mobile Phone ◽  
Real Time ◽  
Soil Moisture Content ◽  
Irrigation System ◽  
Irrigation Scheduling ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Level Sensor

Soil moisture sensor is an instrument for quick measurements of soil moisture content in the crop root zone on real time basis. The main objective of this research was development and evaluation of an indigenous sensor for precise irrigation scheduling. The various parts of sensor developed were ceramic cup, acrylic pipe, level sensor, tee, reducer, gland, cork, and end cap. The designed system was successfully tested on okra crop and calibrated with frequency domain reflectometry (FDR) by three methods of irrigation, i.e. check basin, furrow and drip, respectively. The average depth of water depletion in modified tensiometer by these methods was 27 to 35 cm at 50% management allowable depletion (MAD) of field capacity. This depth was useful for the level sensor to be installed inside modified tensiometer for real time irrigation scheduling. The correlation coefficient (R2) between soil moisture content obtained from the developed sensor and FDR was 0.963. Sensor network was integrated with global system for mobile communication (GSM), short message service (SMS) and drip head work to develop an automated irrigation system. This would enable farmers to effectively monitor and control water application in the field by sending command through SMS and receiving pumping status through the mobile phone.

Soil Water Measurement as Basis to Optimize Irrigation Scheduling: A Review

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 553f-554
Author(s):  
A.K. Alva ◽  
A. Fares
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Real Time ◽  
Soil Water Content ◽  
Root Zone ◽  
Soil Water Potential ◽  
Irrigation Scheduling ◽  
Rooting Depth ◽  
The Impact

Supplemental irrigation is often necessary for high economic returns for most cropping conditions even in humid areas. As irrigation costs continue to increase more efforts should be exerted to minimize these costs. Real time estimation and/or measurement of available soil water content in the crop root zone is one of the several methods used to help growers in making the right decision regarding timing and quantity of irrigation. The gravimetric method of soil water content determination is laborious and doesn't suite for frequent sampling from the same location because it requires destructive soil sampling. Tensiometers, which measure soil water potential that can be converted into soil water content using soil moisture release curves, have been used for irrigation scheduling. However, in extreme sandy soils the working interval of tensiometer is reduced, hence it may be difficult to detect small changes in soil moisture content. Capacitance probes which operate on the principle of apparent dielectric constant of the soil-water-air mixture are extremely sensitive to small changes in the soil water content at short time intervals. These probes can be placed at various depths within and below the effective rooting depth for a real time monitoring of the water content. Based on this continuous monitoring of the soil water content, irrigation is scheduled to replenish the water deficit within the rooting depth while leaching below the root zone is minimized. These are important management practices aimed to increase irrigation efficiency, and nutrient uptake efficiency for optimal crop production, while minimizing the impact of agricultural non-point source pollutants on the groundwater quality.

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

&lt;p&gt;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.&amp;#160;&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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 &gt; 3200 registered users strong. Time series with hourly timestamps from 1952 &amp;#8211; 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 (&gt; 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.).&lt;/p&gt;&lt;p&gt;About 10&amp;#8217;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.&lt;/p&gt;&lt;p&gt;In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.&lt;/p&gt;

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.

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 The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

2018 ◽  
Vol 22 (11) ◽  
pp. 5889-5900 ◽  
Author(s):  
Mireia Fontanet ◽  
Daniel Fernàndez-Garcia ◽  
Francesc Ferrer
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Sprinkler Irrigation ◽  
Irrigation Scheduling ◽  
Time Domain Reflectometry ◽  
Rainfall Events ◽  
Soil Moisture Data

Abstract. Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1 km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site.

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
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