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

2018 ◽  
Author(s):  
Mireia Fontanet ◽  
Daniel Fernández-Garcia ◽  
Francesc Ferrer
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Spatial Scales ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Rainfall Events ◽  
Soil Moisture Sensors ◽  
Gravimetric Measurements ◽  
Agricultural Applications ◽  
Remote Sensing Techniques

Abstract. Soil moisture measurements are needed in a large number of applications such as climate change, watershed water balance and irrigation management. One of the main characteristics of this property is that soil moisture is highly variable with both space and time, hindering the estimation of a representative value. Deciding how to measure soil moisture before undertaking any type of study is therefore an important issue that needs to be addressed correctly. Nowadays, different kinds of methodologies exist for measuring soil moisture; Remote Sensing, soil moisture sensors or gravimetric measurements. This work is focused on how to measure soil moisture for irrigation scheduling, where soil moisture sensors are the main methodology for monitoring soil moisture. One of its disadvantages, however, is that soil moisture sensors measure a small volume of soil, and do not take into account the existing variability in the field. In contrast, Remote Sensing techniques are able to estimate soil moisture with a low spatial resolution, and thus it is not possible to apply these estimations to agricultural applications. In order to solve this problem, different kinds of algorithms have been developed for downscaling these estimations from low to high resolution. The DISPATCH algorithm downscales soil moisture estimations from 40 km to 1 km resolution using SMOS satellite soil moisture, NDVI and LST from MODIS sensor estimations. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in two different hydrologic scenarios; (1) when wet conditions are maintained around the field for rainfall events, and (2) when it is local irrigation that maintains wet conditions. Results show that the DISPATCH algorithm is sensitive when soil moisture is homogenized during general rainfall events, but not when local irrigation generates occasional heterogeneity. In order to explain these different behaviours, we have examined the spatial variability scales of NDVI and LST data, which are the variables involved in the downscaling process provided by the MODIS sensor. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average water content at the site, and this could be a reason for why the DISPATCH algorithm is unable to detect soil moisture increments caused by local irrigation.

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.

scholarly journals Remote Sensing Applications for Planning Irrigation Management. The Use of SEBAL Methodology for Estimating Crop Evapotranspiration in Cyprus

2012 ◽  
Vol 9 (1) ◽  
pp. 17-21
Author(s):  
George Papadavid ◽  
Skevi Perdikou ◽  
Michalakis Hadjimitsis ◽  
Diofantos Hadjimitsis
Keyword(s):  
Remote Sensing ◽  
Water Allocation ◽  
Surface Energy Balance ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Severe Drought ◽  
Remotely Sensed Data ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Remote Sensing Techniques

Abstract Water allocation to crops has always been of great importance in the agricultural process. In this context, and under the current conditions, where Cyprus is facing a severe drought the last five years, the purpose of this study is basically to estimate the needed crop water requirements for supporting irrigation management and monitoring irrigation on a systematic basis for Cyprus using remote sensing techniques. The use of satellite images supported by ground measurements has provided quite accurate results. Intended purpose of this paper is to estimate the Evapotranspiration (ET) of specific crops which is the basis for irrigation scheduling and establish a procedure for monitoring and managing irrigation water over Cyprus, using remotely sensed data from Landsat TM/ ETM+ and a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL).

scholarly journals Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3786 ◽  
Author(s):  
Sumon Datta ◽  
Saleh Taghvaeian ◽  
Tyson Ochsner ◽  
Daniel Moriasi ◽  
Prasanna Gowda ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cropping Systems ◽  
Irrigation Management ◽  
Clay Content ◽  
Field Capacity ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Undisturbed Soil ◽  
Soil Moisture Sensors ◽  
Rosetta Model

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

scholarly journals Designing Low-Cost Capacitive-Based Soil Moisture Sensor and Smart Monitoring Unit Operated by Solar Cells for Greenhouse Irrigation Management

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5387
Author(s):  
Abdelaziz M. Okasha ◽  
Hasnaa G. Ibrahim ◽  
Adel H. Elmetwalli ◽  
Khaled Mohamed Khedher ◽  
Zaher Mundher Yaseen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Root Zone ◽  
Low Cost ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Soil Moisture Sensors

Precise and quick estimates of soil moisture content for the purpose of irrigation scheduling are fundamentally important. They can be accomplished through the continuous monitoring of moisture content in the root zone area, which can be accomplished through automatic soil moisture sensors. Commercial soil moisture sensors are still expensive to be used by famers, particularly in developing countries, such as Egypt. This research aimed to design and calibrate a locally manufactured low-cost soil moisture sensor attached to a smart monitoring unit operated by Solar Photo Voltaic Cells (SPVC). The designed sensor was evaluated on clay textured soils in both lab and controlled greenhouse environments. The calibration results demonstrated a strong correlation between sensor readings and soil volumetric water content (θV). Higher soil moisture content was associated with decreased sensor output voltage with an average determination coefficient (R2) of 0.967 and a root-mean-square error (RMSE) of 0.014. A sensor-to-sensor variability test was performed yielding a 0.045 coefficient of variation. The results obtained from the real conditions demonstrated that the monitoring system for real-time sensing of soil moisture and environmental conditions inside the greenhouse could be a robust, accurate, and cost-effective tool for irrigation management.

Use of geographic information systems and remote sensing as automated tool for variable rate irrigation prescriptions

2019 ◽  
Author(s):  
◽  
Anh Thi Tuan Nguyen
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Regression Models ◽  
Field Data ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Variable Rate ◽  
Water Application ◽  
Center Pivot

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Economic as well as water shortage pressure on agricultural use of water has placed added emphasis on efficient irrigation management. Center pivot technology has made great improvement with variable rate irrigation (VRI) technology to vary water application spatially and temporally to maximize the economic and environmental return. Proper management of VRI systems depends on correctly matching the pivot application to specific field temporal and areal conditions. There is need for a tool to accurately and inexpensively define dynamic management zones, to sense within-field variability in real time, and control variable rate water application so that producers are more willing to adopt and utilize the advantages of VRI systems. This study included tests of the center pivot system uniformity performance in 2014 at Delta Research Center in Portageville, MO. The goal of this research was to develop MOPivot software with an algorithm to determine unique management areas under center pivot systems based on system design and limitations. The MOPivot tool automates prescriptions for VRI center pivot based on non-uniform water needs while avoiding potential runoff and deep percolation. The software was validated for use in real-time irrigation management in 2018 for VRI control system of a Valley 8000 center pivot planted to corn. The water balance model was used to manage irrigation scheduling. Field data, together with soil moisture sensor measurement of soil water content, were used to develop the regression model of remote sensing-based crop coefficient (Kc). Remote sensing vegetation index in conjunction with GDD and crop growth stages in regression models showed high correlation with Kc. Validation of those regression models was done using Centralia, MO, field data in 2016. The MOPivot successfully created prescriptions to match system capacity of the management zone based on system limitations for center pivot management. Along with GIS data sources, MOPivot effectively provides readily available graphical prescription maps, which can be edited and directly uploaded to a center pivot control panel. The modeled Kc compared well with FAO Kc. By combining GDD and crop growth in the models, these models would account for local weather conditions and stage of crop during growing season as time index in estimating Kc. These models with Fraction of growth (FrG) and cumulative growing degree days (cGDD) had a higher coefficient of efficiency, higher Nash-Sutcliffe coefficient of efficiency and higher Willmott index of agreement. Future work should include improvement in the MOPivot software with different crops and aerial remote sensing imagery to generate dynamic prescriptions during the season to support irrigation scheduling for real-time monitoring of field conditions.

scholarly journals SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP

Irriga ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 585-598
Author(s):  
Pedro Henrique Jandreice Magnoni ◽  
Cesar De Oliveira Ferreira Silva ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Remote Sensing ◽  
Water Budget ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
In Situ Data ◽  
Joint Application ◽  
E Mail ◽  
Sentinel 2

SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP*     PEDRO HENRIQUE JANDREICE MAGNONI1; CÉSAR DE OLIVEIRA FERREIRA SILVA1 E RODRIGO LILLA MANZIONE2   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista", Avenida Universitária, n° 3780, Altos do Paraíso, 18610-034, Botucatu, São Paulo, Brasil,  [email protected]; [email protected]. 2 Departamento de Engenharia de Biossistemas, Faculdade de Ciências e Engenharia, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Domingos da Costa Lopes, 780, CEP 17602496, Tupã – SP, Brasil. E-mail: [email protected]. *Este artigo é proveniente das dissertações de mestrado dos dois primeiros autores.     1 RESUMO   Ferramentas baseadas em sensoriamento remoto possibilitam o monitoramento do balanço hídrico da água em diferentes resoluções espaciais e temporais. Ainda assim, modelos que exigem dados in-situ impossibilitam sua aplicação em áreas com escassez de dados. No sentido de lidar com esse desafio, o presente trabalho apresenta uma abordagem de escolha do momento de irrigar, pelo balanço hídrico da água no solo, baseada em estimativa da evapotranspiração real (ETA) obtida com o uso conjunto de imagens multiespectrais do sensor MSI/SENTINEL-2 e dados de uma estação meteorológica pública. A área de estudo foi um pivô central localizado no munícipio de Itatinga-SP. Para a tomada de decisão do momento de irrigar, com base em um manejo por lâmina de irrigação fixa, foi feita a interpolação da fração evapotranspirativa entre os dias com imagens disponíveis para obter a ETA nos dias sem imagens por meio do seu produto com a evapotranspiração de referência. Essa abordagem captou variações climáticas essenciais para a estimativa do balanço hídrico em dias sem imagem. Destaca-se nessa aplicação conjunta sua capacidade de ser realizada sem necessitar de parâmetros específicos da cultura, do microclima ou do relevo, tornando-se interessante para regiões com escassez de dados.   Palavras-chave:  evapotranspiração, momento de irrigar, agriwater.     MAGNONI, P. H. J.; SILVA, C. O. F.; MANZIONE, R. L. REMOTE SENSING APPLIED TO IRRIGATION MANAGEMENT IN AREAS WITH LACK OF DATA: A CASE STUDY IN A CENTRAL PIVOT IN ITATINGA-SP     2 ABSTRACT   Remote sensing-based tools allow the monitoring of water budgets over different spatial and temporal resolutions. Nevertheless, some models require in situ data, preventing their application in areas with a lack of data. To address this challenge, this work presents an approach for irrigation scheduling, based on soil water budget estimation using actual evapotranspiration (ETA) obtained using MSI/SENTINEL-2 multispectral images and data from a public meteorological station. The study area consisted of a central pivot located in the municipality of Itatinga-SP, Brazil. For decision-making of irrigation scheduling, considering a fixed irrigation rate, the evapotranspiration fraction was interpolated between the days with available images to obtain the ETA on the days without images using its product with the reference evapotranspiration. This approach captured essential climate variations for estimating the water budget on non-image days. Noteworthy in this joint application is its suitability to be performed not requiring crop-, microclimate- or relief-specific parameters, making it useful for regions with a lack of data.   Keywords: evapotranspiration, irrigation scheduling, agriwater.

scholarly journals Linking Foliar Traits to Belowground Processes

2020 ◽  
pp. 173-197
Author(s):  
Michael Madritch ◽  
Jeannine Cavender-Bares ◽  
Sarah E. Hobbie ◽  
Philip A. Townsend
Keyword(s):  
Remote Sensing ◽  
Plant Traits ◽  
Spatial Scales ◽  
Root Turnover ◽  
Litter Chemistry ◽  
Plant Chemistry ◽  
Fine Root Turnover ◽  
Belowground Processes ◽  
Remote Sensing Techniques ◽  
Foliar Traits

AbstractAbove- and belowground systems are linked via plant chemistry. In forested systems, leaf litter chemistry and quality mirror that of green foliage and have important afterlife effects. In systems where belowground inputs dominate, such as grasslands, or in ecosystems where aboveground biomass is frequently removed by burning or harvesting, foliar traits may provide important information regarding belowground inputs via exudates and fine-root turnover. Many, if not most, of the plant traits that drive variation in belowground processes are also measurable via remote sensing technologies. The ability of remote sensing techniques to measure fine-scale biodiversity and plant chemistry over large spatial scales can help researchers address ecological questions that were previously prohibitively expensive to address. Key to these potential advances is the idea that remotely sensed vegetation spectra and plant chemistry can provide detailed information about the function of belowground processes beyond what traditional field sampling can provide.

scholarly journals Water stress coefficient determined by orbital remote sensing techniques

2020 ◽  
Vol 24 (12) ◽  
pp. 847-853
Author(s):  
Élvis da S. Alves ◽  
Roberto Filgueiras ◽  
Lineu N. Rodrigues ◽  
Fernando F. da Cunha ◽  
Catariny C. Aleman
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Irrigation Management ◽  
Simple Algorithm ◽  
Landsat 8 ◽  
Stress Coefficient ◽  
Use Efficiency ◽  
Remote Sensing Techniques ◽  
Water Stress Coefficient ◽  
The Relationship

ABSTRACT In regions where the irrigated area is increasing and water availability is reduced, such as the West of the Bahia state, Brazil, the use of techniques that contribute to improving water use efficiency is paramount. One of the ways to improve irrigation is by improving the calculation of actual evapotranspiration (ETa), which among other factors is influenced by soil drying, so it is important to understand this relationship, which is usually accounted for in irrigation management models through the water stress coefficient (Ks). This study aimed to estimate the water stress coefficient (Ks) through information obtained via remote sensing, combined with field data. For this, a study was carried out in the municipality of São Desidério, an area located in western Bahia, using images of the Landsat-8 satellite. Ks was calculated by the relationship between crop evapotranspiration and ETa, calculated by the Simple Algorithm for Evapotranspiration Retrieving (SAFER). The Ks estimated by remote sensing showed, for the development and medium stages, average errors on the order of 5.50%. In the final stage of maize development, the errors obtained were of 23.2%.

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