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Remote Sensing for Agricultural Water Management (RSAWM)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 42851

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Special Issue Editors

Prof. Dr. Guido D’Urso

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Guest Editor

Department of Agricultural Sciences, University of Naples Federico II, 80055 Portici, NA, Italy
Interests: development of earth observation interpretation techniques for water management and land surface processes; distributed agro-hydrological models for water management and irrigation; in situ and remote active microwave sensing of agricultural land surfaces
Special Issues, Collections and Topics in MDPI journals

Dr. Onur Yüzügüllü

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Guest Editor

AgriCircle AG, Herrenberg 35, 8640 Rapperswil-Jona, Switzerland
Interests: remote sensing; precision agriculture; soil mapping; polarimetric SAR; machine learning

Dr. Kyle Knipper

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Guest Editor

Sustainable Agricultural Water Systems Research, Agricultural Research Service, United States Department of Agriculture, Davis, CA, USA
Interests: remote sensing; evapotranspiration; operational irrigation management; precision agriculture

Special Issue Information

Dear Colleagues,

During recent years, there has been much progress in understanding land surface–atmosphere processes and their parameterization in the management of water resources in agriculture. Earth observation techniques in different regions of the electromagnetic spectrum have been used for more than three decades to monitor land surface. Nowadays, these techniques are being transferred to operative applications for managing agricultural land and related inputs. At the same time, technological developments of the new generation of remote sensors with improved spatial and/or temporal resolution provide the opportunity for new observational and modeling perspectives. In this Special Issue, we solicit the presentation of papers on the advancement of operational tools and services based on Earth observation data for the management of water resources in agriculture, with a focus on irrigation and its optimization. Integrated approaches with active and passive sensors, as well as non-observation data, in situ sensors, and modeling techniques are more and more beneficially implemented for the management of agricultural water resources. However, this Special Issue will provide evidence that research advancements are being transferred to applications of effective usefulness in addressing everyday practices and in a problem-solving approach, from the farm to the basin scale.

Specific topics include but are not limited to:

Multi-spectral, hyperspectral, thermal, and SAR imaging systems in irrigated agriculture;
Temporal and spatial precision irrigation management using remote-sensing data;
Estimation of soil moisture under vegetation using SAR data;
Indicators for crop water stress and soil moisture deficit;
Integration of remote-sensing data and agricultural system models for irrigation management;
Novel sensing technologies and multi-platform data fusion for supporting irrigation management.

Prof. Dr. Guido D’Urso
Dr. Onur Yüzügüllü
Dr. Kyle Knipper
Guest Editors

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Keywords

Remote sensing
Irrigation management
Water balance
Crop–soil interactions
Precision agriculture

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Related Special Issue

Remote Sensing for Agricultural Water Management (RSAWM) (Second Edition) in Remote Sensing (4 articles)

Published Papers (10 papers)

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31 pages, 11169 KiB

Open AccessArticle

Remote Sensing for Agricultural Water Management in Jordan

by Jawad T. Al-Bakri, Guido D’Urso, Alfonso Calera, Eman Abdalhaq, Maha Altarawneh and Armin Margane

Remote Sens. 2023, 15(1), 235; https://doi.org/10.3390/rs15010235 - 31 Dec 2022

Cited by 8 | Viewed by 3766

Abstract

This study shows how remote sensing methods are used to support and provide means for improving agricultural water management (AWM) in Jordan through detailed mapping of irrigated areas and irrigation water consumption (IWC). Digital processing and classification methods were applied on multi-temporal data of Landsat 8 and Sentinel-2 to derive maps of irrigated areas for the period 2017–2019. Different relationships were developed between the normalized difference vegetation index (NDVI) and the crop coefficient (Kc) to map evapotranspiration (ET). Using ground data, ET maps were transferred to IWC for the whole country. Spatial analysis was then used to delineate hotspots where shifts between ET and groundwater abstraction were observed. Results showed that the applied remote sensing methods provided accurate maps of irrigated areas. The NDVI-Kc relationships were significant, with coefficients of determination (R²) ranging from 0.89 to 0.93. Subsequently, the ET estimates from the NDVI-Kc relationships were in agreement with remotely sensed ET modeled by SEBAL (NSE = 0.89). In the context of Jordan, results showed that irrigated areas in the country reached 98 thousand ha in 2019, with 64% of this area located in the highlands. The main irrigated crops were vegetables (55%) and fruit trees and olives (40%). The total IWC reached 702 MCM in 2019, constituting 56% of the total water consumption in Jordan, with 375 MCM of this amount being pumped from groundwater, while reported abstraction was only 235 MCM. The study identified the hotspots where illegal abstraction or incorrect metering of groundwater existed. Furthermore, it emphasized the roles of remote sensing in AWM, as it provided updated figures on groundwater abstraction and forecasts for future IWC, which would reach 986 MCM in 2050. Therefore, the approach of ET and IWC mapping would be highly recommended to map ET and to provide estimates of present and future IWC. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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21 pages, 3891 KiB

Open AccessArticle

Assessing Crop Water Requirement and Yield by Combining ERA5-Land Reanalysis Data with CM-SAF Satellite-Based Radiation Data and Sentinel-2 Satellite Imagery

by Anna Pelosi, Oscar Rosario Belfiore, Guido D’Urso and Giovanni Battista Chirico

Remote Sens. 2022, 14(24), 6233; https://doi.org/10.3390/rs14246233 - 9 Dec 2022

Cited by 6 | Viewed by 2149

Abstract

The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest data sources as input for crop–water balance models, thereby improving the crop water requirements (CWR) and yield estimates from the field to the regional scale. Satellite imagery and numerical weather prediction outputs offer high resolution (in time and space) gridded data that can compensate for the paucity of crop parameter field measurements and ground weather observations, as required for assessments of CWR and yield. In this study, the AquaCrop model was used to assess CWR and yield of tomato on a farm in Southern Italy by assimilating Sentinel-2 (S2) canopy cover imagery and using CM-SAF satellite-based radiation data and ERA5-Land reanalysis as forcing weather data. The prediction accuracy was evaluated with field data collected during the irrigation season (April–July) of 2021. Satellite estimates of canopy cover differed from ground observations, with a RMSE of about 11%. CWR and yield predictions were compared with actual data regarding irrigation volumes and harvested yield. The results showed that S2 estimates of crop parameters represent added value, since their assimilation into crop growth models improved CWR and yield estimates. Reliable CWR and yield estimates can be achieved by combining the ERA5-Land and CM-SAF weather databases with S2 imagery for assimilation into the AquaCrop model. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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19 pages, 4637 KiB

Open AccessArticle

Exploring the Ability of Solar-Induced Chlorophyll Fluorescence for Drought Monitoring Based on an Intelligent Irrigation Control System

by Wenhui Zhao, Jianjun Wu, Qiu Shen, Jianhua Yang and Xinyi Han

Remote Sens. 2022, 14(23), 6157; https://doi.org/10.3390/rs14236157 - 5 Dec 2022

Cited by 7 | Viewed by 1770

Abstract

Drought is one of the most devastating disasters and a serious constraint on agricultural development. The reflectance-based vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), have been widely used for drought monitoring, but there is a lag in the response of VIs to the changes of photosynthesis induced by drought. Solar-induced chlorophyll fluorescence (SIF) is closely related to photosynthesis of vegetation and can capture changes induced by drought timely. This study investigated the capability of SIF for drought monitoring. An intelligent irrigation control system (IICS) utilizing the Internet of Things was designed and constructed. The soil moisture of the experiment plots was controlled at 60–80% (well-watered, T1), 50–60% (mild water stress, T2), 40–50% (moderate water stress, T3) and 30–40% (severe water stress, T4) of the field water capacity using the IICS based on data collected by soil moisture sensors. Meanwhile, SIF, NDVI, Normalized Difference Red Edge (NDRE) and Optimized Soil Adjusted Vegetation Index (OSAVI) were collected for a long time series using an automated spectral monitoring system. The differences in the responses of SIF, NDVI, NDRE and OSAVI to different drought intensities were fully analyzed. This study illustrates that the IICS can realize precise irrigation management strategies and the construction of regulated deficit irrigation treatments. SIF significantly decreased under mild stress, while NDVI, NDRE and OSAVI only significantly decreased under moderate and severe stress, indicating that SIF is more sensitive to drought. This study demonstrates the excellent ability of SIF for drought monitoring and lays the foundation for the future application of SIF in agricultural drought monitoring. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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18 pages, 25834 KiB

Open AccessArticle

Agricultural Drought Assessment in a Typical Plain Region Based on Coupled Hydrology–Crop Growth Model and Remote Sensing Data

by Yuliang Zhang, Zhiyong Wu, Vijay P. Singh, Juliang Jin, Yuliang Zhou, Shiqin Xu and Lei Li

Remote Sens. 2022, 14(23), 5994; https://doi.org/10.3390/rs14235994 - 26 Nov 2022

Cited by 2 | Viewed by 2142

Abstract

An agricultural drought assessment is the basis for formulating agricultural drought mitigation strategies. Traditional agricultural drought assessment methods reflect agricultural drought degree by using the soil water deficit, e.g., Soil Moisture Anomaly Percentage Index (SMAPI). However, due to varying water demands for different crops, a given soil water deficit results in varying crop water deficits and agricultural droughts. This variation often leads to a misinterpretation of agricultural drought classification when one only considers the soil water deficit. To consider the influence of crop growth, this study proposes an agricultural drought assessment method by coupling hydrological and crop models (variable infiltration capacity-environmental policy integrated climate, VIC-EPIC). Agricultural drought in Jiangsu Province, China was evaluated using the VIC-EPIC model and crop water anomaly percentage index (CWAPI). The validation results based on the actual drought records showed that the correlation coefficients (0.79 and 0.82, respectively) of the statistical values and CWAPI simulated values of light and moderate drought area rates were greater than those for SMAPI (0.72 and 0.81, respectively), indicating that the simulation results of the VIC-EPIC model in Jiangsu Province were highly reasonable. The temporal and spatial variation characteristics of the drought grade in typical large-scale drought events in Jiangsu Province were also analyzed. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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24 pages, 5463 KiB

Open AccessArticle

Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning

by Mehmet Furkan Celik, Mustafa Serkan Isik, Onur Yuzugullu, Noura Fajraoui and Esra Erten

Remote Sens. 2022, 14(21), 5584; https://doi.org/10.3390/rs14215584 - 5 Nov 2022

Cited by 33 | Viewed by 6633

Abstract

Soil moisture (SM) is an important biophysical parameter by which to evaluate water resource potential, especially for agricultural activities under the pressure of global warming. The recent advancements in different types of satellite imagery coupled with deep learning-based frameworks have opened the door for large-scale SM estimation. In this research, high spatial resolution Sentinel-1 (S1) backscatter data and high temporal resolution soil moisture active passive (SMAP) SM data were combined to create short-term SM predictions that can accommodate agricultural activities in the field scale. We created a deep learning model to forecast the daily SM values by using time series of climate and radar satellite data along with the soil type and topographic data. The model was trained with static and dynamic features that influence SM retrieval. Although the topography and soil texture data were taken as stationary, SMAP SM data and Sentinel-1 (S1) backscatter coefficients, including their ratios, and climate data were fed to the model as dynamic features. As a target data to train the model, we used in situ measurements acquired from the International Soil Moisture Network (ISMN). We employed a deep learning framework based on long short-term memory (LSTM) architecture with two hidden layers that have 32 unit sizes and a fully connected layer. The accuracy of the optimized LSTM model was found to be effective for SM prediction with the coefficient of determination (

R^{2}

) of 0.87, root mean square error (RMSE) of 0.046, unbiased root mean square error (ubRMSE) of 0.045, and mean absolute error (MAE) of 0.033. The model’s performance was also evaluated concerning above-ground biomass, land cover classes, soil texture variations, and climate classes. The model prediction ability was lower in areas with high normalized difference vegetation index (NDVI) values. Moreover, the model can better predict in dry climate areas, such as arid and semi-arid climates, where precipitation is relatively low. The daily prediction of SM values based on microwave remote sensing data and geophysical features was successfully achieved by using an LSTM framework to assist various studies, such as hydrology and agriculture. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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21 pages, 5016 KiB

Open AccessArticle

Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

by Chunfeng Ma, Kasper Johansen and Matthew F. McCabe

Remote Sens. 2022, 14(5), 1205; https://doi.org/10.3390/rs14051205 - 1 Mar 2022

Cited by 14 | Viewed by 4383

Abstract

Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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19 pages, 152668 KiB

Open AccessArticle

Assessing the Performance of Irrigation Systems in Large Scale Urban Parks: Application to the Case of Valdebebas, Madrid (Spain)

by Freddy Canales-Ide, Sergio Zubelzu, Daniel Segovia-Cardozo and Leonor Rodríguez-Sinobas

Remote Sens. 2022, 14(5), 1060; https://doi.org/10.3390/rs14051060 - 22 Feb 2022

Cited by 4 | Viewed by 2482

Abstract

This paper presents a novel approach to assess spatial and temporal irrigation performance in urban parks and can assist park manager/operator decisions in irrigation management. First, irrigation needs are estimated by traditional irrigation scheduling and the irrigation zones with similar water needs that share the same electric valve (hydrozones) are identified. Then, irrigation performance is calculated using the relative water supply (RWS) indicator and mapped (GIS software). This approach can be adapted to various spatial and temporal scales. In this study, it was applied to the Valdebebas urban development VBB (Madrid) between the 2017 and 2019 irrigation seasons. The results for the VBB parks showed high spatio-temporal variation in irrigation performance among plant typologies within an irrigation season, which can be explained by the landscape coefficient K_L variation across the parks. Likewise, this variation was also observed among the three evaluated seasons; explained among other factors by differences in irrigation management. For each hydrozone, the estimation of the NDVI index by Sentinel-2A satellite images in 2019 showed a threshold on irrigation performance. Thus, the remote sensing data together with the proposed approach can be a valuable tool for helping park managers/technicians adopt better decisions on irrigation practices. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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21 pages, 6330 KiB

Open AccessArticle

Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem

by Luis A. Reyes Rojas, Italo Moletto-Lobos, Fabio Corradini, Cristian Mattar, Rodrigo Fuster and Cristián Escobar-Avaria

Remote Sens. 2021, 13(20), 4105; https://doi.org/10.3390/rs13204105 - 13 Oct 2021

Cited by 10 | Viewed by 3270

Abstract

Evapotranspiration (ET) is key to assess crop water balance and optimize water-use efficiency. To attain sustainability in cropping systems, especially in semi-arid ecosystems, it is necessary to improve methodologies of ET estimation. A method to predict ET is by using land surface temperature (LST) from remote sensing data and applying the Operational Simplified Surface Energy Balance Model (SSEBop). However, to date, LST information from Landsat-8 Thermal Infrared Sensor (TIRS) has a coarser resolution (100 m) and longer revisit time than Sentinel-2, which does not have a thermal infrared sensor, which compromises its use in ET models as SSEBop. Therefore, in the present study we set out to use Sentinel-2 data at a higher spatial-temporal resolution (10 m) to predict ET. Three models were trained using TIRS’ images as training data (100 m) and later used to predict LST at 10 m in the western section of the Copiapó Valley (Chile). The models were built on cubist (Cub) and random forest (RF) algorithms, and a sinusoidal model (Sin). The predicted LSTs were compared with three meteorological stations located in olives, vineyards, and pomegranate orchards. RMSE values for the prediction of LST at 10 m were 7.09 K, 3.91 K, and 3.4 K in Cub, RF, and Sin, respectively. ET estimation from LST in spatial-temporal relation showed that RF was the best overall performance (R² = 0.710) when contrasted with Landsat, followed by the Sin model (R² = 0.707). Nonetheless, the Sin model had the lowest RMSE (0.45 mm d⁻¹) and showed the best performance at predicting orchards’ ET. In our discussion, we argue that a simplistic sinusoidal model built on NDVI presents advantages over RF and Cub, which are constrained to the spatial relation of predictors at different study areas. Our study shows how it is possible to downscale Landsat-8 TIRS’ images from 100 m to 10 m to predict ET. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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38 pages, 17827 KiB

Open AccessArticle

Assessing Daily Evapotranspiration Methodologies from One-Time-of-Day sUAS and EC Information in the GRAPEX Project

by Ayman Nassar, Alfonso Torres-Rua, William Kustas, Joseph Alfieri, Lawrence Hipps, John Prueger, Héctor Nieto, Maria Mar Alsina, William White, Lynn McKee, Calvin Coopmans, Luis Sanchez and Nick Dokoozlian

Remote Sens. 2021, 13(15), 2887; https://doi.org/10.3390/rs13152887 - 23 Jul 2021

Cited by 27 | Viewed by 4334

Abstract

Daily evapotranspiration (ET_d) plays a key role in irrigation water management and is particularly important in drought-stricken areas, such as California and high-value crops. Remote sensing allows for the cost-effective estimation of spatial evapotranspiration (ET), and the advent of small unmanned aerial systems (sUAS) technology has made it possible to estimate instantaneous high-resolution ET at the plant, row, and subfield scales. sUAS estimates ET using “instantaneous” remote sensing measurements with half-hourly/hourly forcing micrometeorological data, yielding hourly fluxes in W/m² that are then translated to a daily scale (mm/day) under two assumptions: (a) relative rates, such as the ratios of ET-to-net radiation (R_n) or ET-to-solar radiation (R_s), are assumed to be constant rather than absolute, and (b) nighttime evaporation (E) and transpiration (T) contributions are negligible. While assumption (a) may be reasonable for unstressed, full cover crops (no exposed soil), the E and T rates may significantly vary over the course of the day for partially vegetated cover conditions due to diurnal variations of soil and crop temperatures and interactions between soil and vegetation elements in agricultural environments, such as vineyards and orchards. In this study, five existing extrapolation approaches that compute the daily ET from the “instantaneous” remotely sensed sUAS ET estimates and the eddy covariance (EC) flux tower measurements were evaluated under different weather, grapevine variety, and trellis designs. Per assumption (b), the nighttime ET contribution was ignored. Each extrapolation technique (evaporative fraction (EF), solar radiation (R_s), net radiation-to-solar radiation (R_n/R_s) ratio, Gaussian (GA), and Sine) makes use of clear skies and quasi-sinusoidal diurnal variations of hourly ET and other meteorological parameters. The sUAS ET estimates and EC ET measurements were collected over multiple years and times from different vineyard sites in California as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Optical and thermal sUAS imagery data at 10 cm and 60 cm, respectively, were collected by the Utah State University AggieAir sUAS Program and used in the Two-Source Energy Balance (TSEB) model to estimate the instantaneous or hourly sUAS ET at overpass time. The hourly ET from the EC measurements was also used to validate the extrapolation techniques. Overall, the analysis using EC measurements indicates that the R_s, EF, and GA approaches presented the best goodness-of-fit statistics for a window of time between 1030 and 1330 PST (Pacific Standard Time), with the R_s approach yielding better agreement with the EC measurements. Similar results were found using TSEB and sUAS data. The 1030–1330 time window also provided the greatest agreement between the actual daily EC ET and the extrapolated TSEB daily ET, with the R_s approach again yielding better agreement with the ground measurements. The expected accuracy of the upscaled TSEB daily ET estimates across all vineyard sites in California is below 0.5 mm/day, (EC extrapolation accuracy was found to be 0.34 mm/day), making the daily scale results from TSEB reliable and suitable for day-to-day water management applications. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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20 pages, 64463 KiB

Open AccessPerspective

Towards Monitoring Waterlogging with Remote Sensing for Sustainable Irrigated Agriculture

by Nadja den Besten, Susan Steele-Dunne, Richard de Jeu and Pieter van der Zaag

Remote Sens. 2021, 13(15), 2929; https://doi.org/10.3390/rs13152929 - 26 Jul 2021

Cited by 20 | Viewed by 8946

Abstract

Waterlogging is an increasingly important issue in irrigated agriculture that has a detrimental impact on crop productivity. The above-ground effect of waterlogging on crops is hard to distinguish from water deficit stress with remote sensing, as responses such as stomatal closure and leaf wilting occur in both situations. Currently, waterlogging as a source of crop stress is not considered in remote sensing-based evaporation algorithms and this may therefore lead to erroneous interpretation for irrigation scheduling. Monitoring waterlogging can improve evaporation models to assist irrigation management. In addition, frequent spatial information on waterlogging will provide agriculturalists information on land trafficability, assist drainage design, and crop choice. This article provides a scientific perspective on the topic of waterlogging by consulting literature in the disciplines of agronomy, hydrology, and remote sensing. We find the solution to monitor waterlogging lies in a multi-sensor approach. Future scientific routes should focus on monitoring waterlogging by combining remote sensing and ancillary data. Here, drainage parameters deduced from high spatial resolution Digital Elevation Models (DEMs) can play a crucial role. The proposed approaches may provide a solution to monitor and prevent waterlogging in irrigated agriculture. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

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