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Remote Sensing in Irrigated Crop Water Stress Assessment

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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 (31 March 2022) | Viewed by 79808

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Special Issue Editors

Dr. Abdelghani Chehbouni

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Guest Editor

1. International Water Research Institute, Mohammed VI Polytechnic University, Hay My Rachid, Ben Guerir 43150, Morocco
2. Institut de Recherche Pour le Développement (IRD), Unité Mixte de Recherche (UMR), Centre D’études Spatiales de la Biosphère (Cesbio), 31401 Toulouse, France
Interests: evapotranspiration; remote sensing; crop modelling; data assimilation

Prof. Dr. Salah Er-Raki

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Guest Editor

1. ProcEDE, Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakech, B.P 549, Av. Abdelkarim Elkhattabi, Guéliz Marrakech, Morocco
2. Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco
Interests: monitoring crop water requirements; crop water stress detection; multispectral remote sensing for agricultural applications; agronomic modeling; data assimilation; retrieval of biophysical crop variables from a multisensor remote sensing approach
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Special Issue Information

Dear Colleagues,

Optimizing water management in agriculture is essential over arid and semi-arid regions in order to preserve water resources, which are already scarce due to human activities and climate change. In these regions, water scarcity is one of the main factors limiting agricultural development and, thus, food security. The impact of such water scarcity is amplified by inefficient irrigation practices, especially since irrigation consumes more than 85% of the available water in these regions. Thus, in order to increase water use efficiency in agriculture, it is necessary to improve on-farm irrigation management by adjusting the irrigation amount to crop water requirements along the crop growing season.

Crop water stress can be detected by measuring the in situ root zone soil moisture in order to trigger the irrigations. However, in situ point measurements are generally expensive, time consuming, and not available over extended areas. Therefore, remote sensing provides an alternative and cost-effective method for mapping and monitoring broad areas and can be used to assess crop water stress through retrieving different biophysical crop variables from a multisensor/multiwavelength.

In this Special Issue, we are seeking original scientific contributions on assessment of Crop Water Stress Based on Different Remote Sensing data. Specific topics include but are not limited to the following:

Detecting crop water stress from remote sensing;
Multispectral and hyperspectral remote sensing for mapping plant water status;
Developing crop water stress indices based on remote sensing data;
Retrieval of biophysical crop variables from a multisensor remote sensing;
Time-series analysis of remote sensing data;
Management and irrigation triggering using crop modeling combined with satellite data;
Evapotranspiration and remote sensing data;
Improving irrigation water use efficiency based on remote sensing data;
Precision farming with high resolution from multispectral and hyperspectral satellite data or drones;
ICT tools for real-time irrigation management with remote sensing data;
Decision support tools and irrigation decision making;
IoT-based smart farming.

Dr. Abdelghani Chehbouni
Prof. Dr. Salah Er-Raki
Guest Editors

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Keywords

Crop water stress indices
Remote sensing data
Multispectral and hyperspectral images
Evapotranspiration
Irrigation triggering
Water use efficiency
Thermal, visible, and microwave imaging
Decision support tools

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Published Papers (13 papers)

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Editorial

Jump to: Research, Review

3 pages, 182 KiB

Open AccessEditorial

Remote Sensing in Irrigated Crop Water Stress Assessment

by Salah Er-Raki and Abdelghani Chehbouni

Remote Sens. 2023, 15(4), 911; https://doi.org/10.3390/rs15040911 - 7 Feb 2023

Cited by 3 | Viewed by 2021

Abstract

Optimizing water management in agriculture is of crucial importance, especially in arid and semi-arid regions where the existing water shortage is exacerbated by human activities and climate change [...] Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

Research

Jump to: Editorial, Review

25 pages, 7217 KiB

Open AccessArticle

Accounting for Almond Crop Water Use under Different Irrigation Regimes with a Two-Source Energy Balance Model and Copernicus-Based Inputs

by Christian Jofre-Čekalović, Héctor Nieto, Joan Girona, Magi Pamies-Sans and Joaquim Bellvert

Remote Sens. 2022, 14(9), 2106; https://doi.org/10.3390/rs14092106 - 27 Apr 2022

Cited by 15 | Viewed by 3403

Abstract

Accounting for water use in agricultural fields is of vital importance for the future prospects for enhancing water use efficiency. Remote sensing techniques, based on modelling surface energy fluxes, such as the two-source energy balance (TSEB), were used to estimate actual evapotranspiration (ET_a) on the basis of shortwave and thermal data. The lack of high temporal and spatial resolution of satellite thermal infrared (TIR) missions has led to new approaches to obtain higher spatial resolution images with a high revisit time. These new approaches take advantage of the high spatial resolution of Sentinel-2 (10–20 m), and the high revisit time of Sentinel-3 (daily). The use of the TSEB model with sharpened temperature (TSEB_S2+S3) has recently been applied and validated in several study sites. However, none of these studies has applied it in heterogeneous row crops under different water status conditions within the same orchard. This study assessed the TSEB_S2+S3 modelling approach to account for almond crop water use under four different irrigation regimes and over four consecutive growing seasons (2017–2020). The energy fluxes were validated with an eddy covariance system and also compared with a soil water balance model. The former reported errors of 90 W/m² and 87 W/m² for the sensible (H) and latent heat flux (LE), respectively. The comparison of ET_a with the soil water balance model showed a root-mean-square deviation (RMSD) ranging from 0.6 to 2.5 mm/day. Differences in cumulative ET_a between the irrigation treatments were estimated, with maximum differences obtained in 2019 of 20% to 13% less in the most water-limited treatment compared to the most well-watered one. Therefore, this study demonstrates the feasibility of using the TSEB_S2+S3 for monitoring ET_a in almond trees under different water regimes. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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17 pages, 7641 KiB

Open AccessArticle

Soil Salinity Detection and Mapping in an Environment under Water Stress between 1984 and 2018 (Case of the Largest Oasis in Africa-Morocco)

by Abdellatif Rafik, Hassan Ibouh, Abdelhafid El Alaoui El Fels, Lhou Eddahby, Daoud Mezzane, Mohamed Bousfoul, Abdelhakim Amazirh, Salah Ouhamdouch, Mohammed Bahir, Abdelali Gourfi, Driss Dhiba and Abdelghani Chehbouni

Remote Sens. 2022, 14(7), 1606; https://doi.org/10.3390/rs14071606 - 27 Mar 2022

Cited by 34 | Viewed by 4512

Abstract

Water stress is one of the factors controlling agricultural land salinization and is also a major problem worldwide. According to FAO and the most recent estimates, it already affects more than 400 million hectares. The Tafilalet plain in Southeastern Morocco suffers from soil salinization. In this regard, the GIS tools and remote sensing were used in the processing of 19 satellite images acquired from Landsat 4–5, (Landsat 7), (Landsat 8), and (Sentinel 2) sensors. The most used indices in the literature were (16 indices) tested and correlated with the results obtained from 25 samples taken from the first soil horizon at a constant depth of 0.20 m from the 2018 campaign. The linear model, at first, allows the selection of five better indices of the soil salinity discrimination (SI-Khan, VSSI, BI, S3, and SI-Dehni). These last indices were the subject of the application of a logarithmic model and polynomial models of degree two and four to increase the prediction of saline soil.. After studies and analysis, we concluded that the second-degree polynomial model of the salinity index (SI-KHAN) is the most efficient one for detecting and mapping soil salinity in the Tafilalet oasis, with a coefficient of determination (R²) and the Nash–Sutcliffe efficiency (NSE) equal to 0.93 and 0.86, respectively. Percent bias (PBIAS) calculated for this model equal was 1.868% < 10%, and the low value of the root mean square error (RMSE) confirms its very good performance. The drought cyclicity led to the intensification of the soil salinization process and accelerated soil degradation. The standardized precipitation anomaly index (SPAI) is strongly correlated to soil salinity. The hydroclimate condition is the factor that further controls this phenomenon. An increase in salinized surfaces is observed during the periods of 1984–1996 and 2000–2005, which cover a surface of 11.50 and 24.20 km², respectively, while a decrease of about 50% is observed during the periods of 1996–2000 and 2005–2018. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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17 pages, 5348 KiB

Open AccessArticle

Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

by Huailin Zhou, Guangsheng Zhou, Xingyang Song and Qijin He

Remote Sens. 2022, 14(3), 584; https://doi.org/10.3390/rs14030584 - 26 Jan 2022

Cited by 24 | Viewed by 4560

Abstract

A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01–0.41 kg m⁻²), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m⁻² for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m⁻². Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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18 pages, 2205 KiB

Open AccessArticle

Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress

by Shujie Gu, Qi Liao, Shaoyu Gao, Shaozhong Kang, Taisheng Du and Risheng Ding

Remote Sens. 2021, 13(22), 4710; https://doi.org/10.3390/rs13224710 - 21 Nov 2021

Cited by 22 | Viewed by 5410

Abstract

The crop water stress index (CWSI), based on canopy temperature (T_c), has been widely used in evaluating plant water status and planning irrigation scheduling, but whether CWSI can diagnose the stress status of crops and predict the physiological traits and growth under combined water and salt stress remains to be further studied. Here, a model of CWSI was established based on the continuous measurements of T_c for two maize genotypes (ZD958 and XY335) under two water and salt conditions, combined with growth stage-specific non-water-stressed baselines (NWSB). The relationships between physiology, growth, and yield of maize with CWSI were analyzed. There were significant differences in NWSB between the two maize genotypes at the same and different growth stages; thus, growth stage-specific NWSBs were used. The difference in NWSB was due to the difference and change in effective leaf width. CWSI was closely related to leaf water potential, stomatal conductance, and net photosynthetic rate under different water and salt stress, and also explained the variations in leaf area index, biomass, water use, and yield. Collectively, CWSI can be used as a proxy indicator of high-throughput phenotyping maize performance under combined water and salt stress, which will be valuable for predicting yield and improving water use efficiency. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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27 pages, 6309 KiB

Open AccessArticle

Optimizing the Sowing Date to Improve Water Management and Wheat Yield in a Large Irrigation Scheme, through a Remote Sensing and an Evolution Strategy-Based Approach

by Salwa Belaqziz, Saïd Khabba, Mohamed Hakim Kharrou, El Houssaine Bouras, Salah Er-Raki and Abdelghani Chehbouni

Remote Sens. 2021, 13(18), 3789; https://doi.org/10.3390/rs13183789 - 21 Sep 2021

Cited by 18 | Viewed by 4080

Abstract

This study aims to investigate the effects of an optimized sowing calendar for wheat over a surface irrigation scheme in the semi-arid region of Haouz (Morocco) on irrigation water requirements, crop growth and development and on yield. For that, a scenario-based simulation approach based on the covariance matrix adaptation–evolution strategy (CMA-ES) was proposed to optimize both the spatiotemporal distribution of sowing dates and the irrigation schedules, and then evaluate wheat crop using the 2011–2012 growing season dataset. Six sowing scenarios were simulated and compared to identify the most optimal spatiotemporal sowing calendar. The obtained results showed that with reference to the existing sowing patterns, early sowing of wheat leads to higher yields compared to late sowing (from 7.40 to 5.32 t/ha). Compared with actual conditions in the study area, the spatial heterogeneity is highly reduced, which increased equity between farmers. The results also showed that the proportion of plots irrigated in time can be increased (from 40% to 82%) compared to both the actual irrigation schedules and to previous results of irrigation optimization, which did not take into consideration sowing dates optimization. Furthermore, considerable reduction of more than 40% of applied irrigation water can be achieved by optimizing sowing dates. Thus, the proposed approach in this study is relevant for irrigation managers and farmers since it provides an insight on the consequences of their agricultural practices regarding the wheat sowing calendar and irrigation scheduling and can be implemented to recommend the best practices to adopt. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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21 pages, 5147 KiB

Open AccessArticle

Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries

by Muhammad Shahzaman, Weijun Zhu, Irfan Ullah, Farhan Mustafa, Muhammad Bilal, Shazia Ishfaq, Shazia Nisar, Muhammad Arshad, Rashid Iqbal and Rana Waqar Aslam

Remote Sens. 2021, 13(16), 3294; https://doi.org/10.3390/rs13163294 - 20 Aug 2021

Cited by 74 | Viewed by 6685

Abstract

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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17 pages, 7033 KiB

Open AccessArticle

Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach

by Abdelhakim Amazirh, El Houssaine Bouras, Luis Enrique Olivera-Guerra, Salah Er-Raki and Abdelghani Chehbouni

Remote Sens. 2021, 13(16), 3181; https://doi.org/10.3390/rs13163181 - 11 Aug 2021

Cited by 5 | Viewed by 3761

Abstract

Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water–surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water–surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation ‘k = 10’, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error ‘RMSE’, bias and correlation coefficient ‘R’). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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25 pages, 9740 KiB

Open AccessArticle

Remote Sensing Indices for Spatial Monitoring of Agricultural Drought in South Asian Countries

by Muhammad Shahzaman, Weijun Zhu, Muhammad Bilal, Birhanu Asmerom Habtemicheal, Farhan Mustafa, Muhammad Arshad, Irfan Ullah, Shazia Ishfaq and Rashid Iqbal

Remote Sens. 2021, 13(11), 2059; https://doi.org/10.3390/rs13112059 - 23 May 2021

Cited by 77 | Viewed by 9680

Abstract

Drought is an intricate atmospheric phenomenon with the greatest impacts on food security and agriculture in South Asia. Timely and appropriate forecasting of drought is vital in reducing its negative impacts. This study intended to explore the performance of the evaporative stress index (ESI), vegetation health index (VHI), enhanced vegetation index (EVI), and standardized anomaly index (SAI) based on satellite remote sensing data from 2002–2019 for agricultural drought assessment in Afghanistan, Pakistan, India, and Bangladesh. The spatial maps were generated against each index, which indicated a severe agricultural drought during the year 2002, compared to the other years. The results showed that the southeast region of Pakistan, and the north, northwest, and southwest regions of India and Afghanistan were significantly affected by drought. However, Bangladesh faced substantial drought in the northeast and northwest regions during the drought year (2002). The longest drought period of seven months was observed in India followed by Pakistan and Afghanistan with six months, while, only three months were perceived in Bangladesh. The correlation between drought indices and climate variables such as soil moisture has remained a significant drought-initiating variable. Furthermore, this study confirmed that the evaporative stress index (ESI) is a good agricultural drought indicator, being quick and with greater sensitivity, and thus advantageous compared to the VHI, EVI, and SAI vegetation indices. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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22 pages, 7974 KiB

Open AccessArticle

Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco

by Mohamed Hakim Kharrou, Vincent Simonneaux, Salah Er-Raki, Michel Le Page, Saïd Khabba and Abdelghani Chehbouni

Remote Sens. 2021, 13(6), 1133; https://doi.org/10.3390/rs13061133 - 16 Mar 2021

Cited by 37 | Viewed by 5760

Abstract

This study aims to evaluate a remote sensing-based approach to allow estimation of the temporal and spatial distribution of crop evapotranspiration (ET) and irrigation water requirements over irrigated areas in semi-arid regions. The method is based on the daily step FAO-56 Soil Water Balance model combined with a time series of basal crop coefficients and the fractional vegetation cover derived from high-resolution satellite Normalized Difference Vegetation Index (NDVI) imagery. The model was first calibrated and validated at plot scale using ET measured by eddy-covariance systems over wheat fields and olive orchards representing the main crops grown in the study area of the Haouz plain (central Morocco). The results showed that the model provided good estimates of ET for wheat and olive trees with a root mean square error (RMSE) of about 0.56 and 0.54 mm/day respectively. The model was then used to compare remotely sensed estimates of irrigation requirements (RS-IWR) and irrigation water supplied (WS) at plot scale over an irrigation district in the Haouz plain through three growing seasons. The comparison indicated a large spatio-temporal variability in irrigation water demands and supplies; the median values of WS and RS-IWR were 130 (175), 117 (175) and 118 (112) mm respectively in the 2002–2003, 2005–2006 and 2008–2009 seasons. This could be attributed to inadequate irrigation supply and/or to farmers’ socio-economic considerations and management practices. The findings demonstrate the potential for irrigation managers to use remote sensing-based models to monitor irrigation water usage for efficient and sustainable use of water resources. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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23 pages, 4292 KiB

Open AccessArticle

On the Utility of High-Resolution Soil Moisture Data for Better Constraining Thermal-Based Energy Balance over Three Semi-Arid Agricultural Areas

by Bouchra Ait Hssaine, Abdelghani Chehbouni, Salah Er-Raki, Said Khabba, Jamal Ezzahar, Nadia Ouaadi, Nitu Ojha, Vincent Rivalland and Olivier Merlin

Remote Sens. 2021, 13(4), 727; https://doi.org/10.3390/rs13040727 - 17 Feb 2021

Cited by 15 | Viewed by 3761

Abstract

Over semi-arid agricultural areas, the surface energy balance and its components are largely dependent on the soil water availability. In such conditions, the land surface temperature (LST) retrieved from the thermal bands has been commonly used to represent the high spatial variability of the surface evaporative fraction and associated fluxes. In contrast, however, the soil moisture (SM) retrieved from microwave data has rarely been used thus far due to the unavailability of high-resolution (field scale) SM products until recent times. Soil evaporation is controlled by the surface SM. Moreover, the surface SM dynamics is temporally related to root zone SM, which provides information about the water status of plants. The aim of this work was to assess the gain in terms of flux estimates when integrating microwave-derived SM data in a thermal-based energy balance model at the field scale. In this study, SM products were derived from three different methodologies: the first approach inverts SM, labeled hereafter as ‘SM_O20’, from the backscattering coefficient and the interferometric coherence derived from Sentinel-1 products in the water cloud model (WCM); the second approach inverts SM from Sentinel-1 and Sentinel-2 data based on machine learning algorithms trained on a synthetic dataset simulated by the WCM noted ‘SM_E16’; and the third approach disaggregates the soil moisture active and passive SM at 100 m resolution using Landsat optical/thermal data ‘SM_O19’. These SM products, combined with the Landsat based vegetation index and LST, are integrated simultaneously within an energy balance model (TSEB-SM) to predict the latent (LE) and sensible (H) heat fluxes over two irrigated and rainfed wheat crop sites located in the Haouz Plain in the center of Morocco. H and LE were measured over each site using an eddy covariance system and their values were used to evaluate the potential of TSEB-SM against the classical two source energy balance (TSEB) model solely based on optical/thermal data. Globally, TSEB systematically overestimates LE (mean bias of 100 W/m²) and underestimates H (mean bias of −110 W/m²), while TSEB-SM significantly reduces those biases, regardless of the SM product used as input. This is linked to the parameterization of the Priestley Taylor coefficient, which is set to α_PT = 1.26 by default in TSEB and adjusted across the season in TSEB-SM. The best performance of TSEB-SM was obtained over the irrigated field using the three retrieved SM products with a mean R² of 0.72 and 0.92, and a mean RMSE of 31 and 36 W/m² for LE and H, respectively. This opens up perspectives for applying the TSEB-SM model over extended irrigated agricultural areas to better predict the crop water needs at the field scale. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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22 pages, 5701 KiB

Open AccessArticle

Application of Remote Sensing Techniques to Discriminate the Effect of Different Soil Management Treatments over Rainfed Vineyards in Chianti Terroir

by Àngela Puig-Sirera, Daniele Antichi, Dylan Warren Raffa and Giovanni Rallo

Remote Sens. 2021, 13(4), 716; https://doi.org/10.3390/rs13040716 - 16 Feb 2021

Cited by 14 | Viewed by 3864

Abstract

The work aimed to discriminate among different soil management treatments in terms of beneficial effects by high-resolution thermal and spectral vegetation imagery using an unmanned aerial vehicle and open-source GIS software. Five soil management treatments were applied in two organic vineyards (cv. Sangiovese) from Chianti Classico terroir (Tuscany, Italy) during two experimental years. The treatments tested consisted of conventional tillage, spontaneous vegetation, pigeon bean (Vicia faba var. minor Beck) incorporated in spring, mixture of barley (Hordeum vulgare L.) and clover (Trifolium squarrosum L.) incorporated or left as dead mulch in late spring. The images acquired remotely were analyzed through map-algebra and map-statistics in QGIS and correlated with field ecophysiological measurements. The surface temperature, crop water stress index (CWSI) and normalized difference vegetation index (NDVI) of each vine row under treatments were compared based on frequency distribution functions and statistics descriptors of position. The spectral vegetation and thermal-based indices were significantly correlated with the respective leaf area index (R² = 0.89) and stem water potential measurements (R² = 0.59), and thus are an expression of the crop vigor and water status. The gravel and active limestone soil components determined the spatial variability of vine biophysical (e.g., canopy vigor) and physiological characteristics (e.g., vine chlorophyll content) in both farms. The vine canopy surface temperature, and CWSI were lower on the spontaneous and pigeon bean treatments in both farms, thus evidencing less physiological stress on the vine rows derived from the cover crop residual effect. In conclusion, the proposed methodology showed the capacity to discriminate across soil management practices and map the spatial variability within vineyards. The methodology could serve as a simple and non-invasive tool for precision soil management in rainfed vineyards to guide producers on using the most efficient and profitable practice. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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Review

Jump to: Editorial, Research

26 pages, 3350 KiB

Open AccessReview

A Review of Crop Water Stress Assessment Using Remote Sensing

by Uzair Ahmad, Arturo Alvino and Stefano Marino

Remote Sens. 2021, 13(20), 4155; https://doi.org/10.3390/rs13204155 - 17 Oct 2021

Cited by 75 | Viewed by 18527

Abstract

Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved. Full article

(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)

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