Special Issue "Remote Sensing for Crop Water Management"

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 January 2019).

Special Issue Editors

Prof. Dr. Guido D’Urso
E-Mail Website
Guest Editor
Department of Agricultural Sciences, University of Naples Federico II, Corso Umberto I, 40, 80138 Napoli NA, Italy
Tel. +393472310830
Interests: remote sensing; irrigation scheduling; evapotranspiration; digital and precision agriculture; soil-crop-atmosphere; soil water balance
Prof. Dr. Alfonso Calera
E-Mail Website
Guest Editor
Institute for Regional Development, University of Castilla La Mancha, 02071 Albacete
Interests: Remote Sensing; Evapotranspiration; Soil Water Balance; Irrigation; Water Governance; webGIS platforms
Dr. Pablo J. Zarco-Tejada
E-Mail Website
Guest Editor
QuantaLab Remote Sensing Laboratory, Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo, s/n, E-14004 Córdoba, Spain
Fax: +(34) 957 499 252
Interests: hyperspectral, thermal, manned platforms; Unmanned Aerial Vehicles (UAV, UAS, RPAS); fluorescence; radiative transfer models
Special Issues and Collections in MDPI journals
Dr. Francesco Vuolo
E-Mail Website
Guest Editor
Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU), Austria
Interests: Irrigation; Evapotranspiration; Agriculture Crop; Digital Farming; Remote Sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Whilst the pressure of sustainable exploitation of water resources in agriculture is increasing, the evolution of remote sensing techniques, in recent years, has opened new perspectives for supporting crop water management. Enhanced spectral, spatial and temporal resolution of spaceborne sensors, such Sentinel-2 and technological developments of miniature hyperspectral and thermal cameras on board unmanned platforms (UAVs, UAS, RPAS) offers unprecedented possibilities for an efficient monitoring of crop water demands at different application scales and purposes. Analysis and interpretation models of remote sensing data have also evolved for improving the accuracy and the reliability of the information data delivered to final users.

This Special Issue aims at providing the state-of-the-art of remote sensing techniques for crop water management, with a special focus on operational applications targeted to the needs of final users, i.e., farmers, farmer associations, water-use managers and regulating authorities. Successful case-studies of real world applications—coupled with rigorous methodologies—are sought in order to fully describe the current capabilities of remote sensing to address the challenges of water stress detection and crop water management in the era of digital farming.

Prof. Eng. Guido D’Urso
Prof. Dr. Alfonso Calera
Dr. Pablo J. Zarco-Tejada
Dr. Francesco Vuolo
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • irrigation services
  • crop water stress
  • evapotranspiration
  • surface energy balance
  • digital farming
  • water governance
  • Earth Observation
  • manned platforms
  • unmanned aerial vehicles and systems (UAVs, UAS)
  • radiative transfer models

Published Papers (10 papers)

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Open AccessArticle
Monitoring the Effects of Water Stress in Cotton Using the Green Red Vegetation Index and Red Edge Ratio
Remote Sens. 2019, 11(7), 873; https://doi.org/10.3390/rs11070873 - 10 Apr 2019
Cited by 2
Abstract
The main objective of this work was to study the feasibility of using the green red vegetation index (GRVI) and the red edge ratio (RE/R) obtained from UAS imagery for monitoring the effects of soil water deficit and for predicting fibre quality in [...] Read more.
The main objective of this work was to study the feasibility of using the green red vegetation index (GRVI) and the red edge ratio (RE/R) obtained from UAS imagery for monitoring the effects of soil water deficit and for predicting fibre quality in a surface-irrigated cotton crop. The performance of these indices to track the effects of water stress on cotton was compared to that of the normalised difference vegetation index (NDVI) and crop water stress index (CWSI). The study was conducted during two consecutive seasons on a commercial farm where three irrigation frequencies and two nitrogen rates were being tested. High-resolution multispectral images of the site were acquired on four dates in 2017 and six dates in 2018, encompassing a range of matric potential values. Leaf stomatal conductance was also measured at the image acquisition times. At harvest, lint yield and fibre quality (micronaire) were determined for each treatment. Results showed that within each year, the N rates tested (> 180 kg N ha−1) did not have a statistically significant effect on the spectral indices. Larger intervals between irrigations in the less frequently irrigated treatments led to an increase (p < 0.05) in the CWSI and a reduction (p < 0.05) in the GRVI, RE/R, and to a lesser extent in the NDVI. A statistically significant and good correlation was observed between the GRVI and RE/R with soil matric potential and stomatal conductance at specific dates. The GRVI and RE/R were in accordance with the soil and plant water status when plants experienced a mild level of water stress. In most of the cases, the GRVI and RE/R displayed long-term effects of the water stress on plants, thus hampering their use for determinations of the actual soil and plant water status. The NDVI was a better predictor of lint yield than the GRVI and RE/R. However, both GRVI and RE/R correlated well (p < 0.01) with micronaire in both years of study and were better predictors of micronaire than the NDVI. This research presents the GRVI and RE/R as good predictors of fibre quality with potential to be used from satellite platforms. This would provide cotton producers the possibility of designing specific harvesting plans in the case that large fibre quality variability was expected to avoid discount prices. Further research is needed to evaluate the capability of these indices obtained from satellite platforms and to study whether these results obtained for cotton can be extrapolated to other crops. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessArticle
Determination of Crop Water Stress Index by Infrared Thermometry in Grapefruit Trees Irrigated with Saline Reclaimed Water Combined with Deficit Irrigation
Remote Sens. 2019, 11(7), 757; https://doi.org/10.3390/rs11070757 - 28 Mar 2019
Cited by 1
Abstract
Water is not always accessible for agriculture due to its scarcity. In order to successfully develop irrigation strategies that optimize water productivity characterization of the plant, the water status is necessary. We assessed the suitability of thermal indicators by infrared thermometry (IRT) to [...] Read more.
Water is not always accessible for agriculture due to its scarcity. In order to successfully develop irrigation strategies that optimize water productivity characterization of the plant, the water status is necessary. We assessed the suitability of thermal indicators by infrared thermometry (IRT) to determine the water status of grapefruit in a commercial orchard with long term irrigation using saline reclaimed water (RW) and regulated deficit irrigation (RDI) in Southeastern Spain. The results showed that Tc-Ta differences were positive in a wide range of vapor pressure deficits (VPD), and the major Tc-Ta were found at 10.00 GMT, before and after the highest daily values of VPD and solar radiation, respectively, were reached. In addition, we evaluated the relationships between Tc-Ta and VPD to establish the Non-Water Stressed Baselines (NWSBs), which are necessary to accurately calculate the crop water stress index (CWSI). Two important findings were found, which include i) the best significant correlations (p < 0.005) found at 10.00 GMT and their slopes were positive, and ii) NWSBs showed a marked hourly and seasonal variation. The hourly shift was mainly explained by the variation in solar radiation since both the NWSB-slope and the NWSB-intercept were significantly correlated with a zenith solar angle (θZ) (p < 0.005). The intercept was greater when θZ was close to 0 (at midday) and the slope displayed a marked hysteresis throughout the day, increasing in the morning and decreasing in the afternoon. The NWSBs determination, according to the season improved most of their correlation coefficients. In addition, the relationship significance of Tc-Ta versus VPD was higher in the period where the intercept and Tc-Ta were low. CWSI was the thermal indicator that showed the highest level of agreement with the stem water potential of the different treatments even though Tc and Tc-Ta were also significantly correlated. We highlight the suitability of thermal indicators measured by IRT to determine the water status of grapefruits under saline (RW) and water stress (RDI) conditions. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessArticle
Global Satellite-Based ET Products for the Local Level Irrigation Management: An Application of Irrigation Performance Assessment in the Sugarbelt of Swaziland
Remote Sens. 2019, 11(6), 705; https://doi.org/10.3390/rs11060705 - 23 Mar 2019
Cited by 2
Abstract
Remote sensing techniques have been shown, in several studies, to be an extremely effective tool for assessing the performance of irrigated areas at various scales and diverse climatic regions across the world. Open access, ready-made, global ET products were utilized in this first-ever-countrywide [...] Read more.
Remote sensing techniques have been shown, in several studies, to be an extremely effective tool for assessing the performance of irrigated areas at various scales and diverse climatic regions across the world. Open access, ready-made, global ET products were utilized in this first-ever-countrywide irrigation performance assessment study. The study aimed at identifying ‘bright spots’, the highest performing sugarcane growers, and ‘hot spots’, or low performing sugarcane growers. Four remote sensing-derived irrigation performance indicators were applied to over 302 sugarcane growers; equity, adequacy, reliability and crop water productivity. The growers were segmented according to: (i) land holding size or grower scale (ii) management regime, (iii) location of the irrigation schemes and (iv) irrigation method. Five growing seasons, from June 2005 to October 2009, were investigated. The results show while the equity of water distribution is high across all management regimes and locations, adequacy and reliability of water needs improvement in several locations. Given the fact that, in general, water supply was not constrained during the study period, the observed issues with adequacy and reliability of irrigation in some of the schemes were mostly due to poor scheme and farm level water management practices. Sugarcane crop water productivity showed the highest variation among all the indicators, with Estate managed schemes having the highest CWP at 1.57 kg/m3 and the individual growers recording the lowest CWP at 1.14 kg/m3, nearly 30% less. Similarly center pivot systems showed to have the highest CWP at 1.63 kg/m3, which was 30% higher than the CWP in furrow systems. This study showcases the applicability of publicly available global remote sensing products for assessing performance of the irrigated crops at the local level in several aspects. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessArticle
Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery
Remote Sens. 2019, 11(3), 267; https://doi.org/10.3390/rs11030267 - 29 Jan 2019
Cited by 3
Abstract
Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic [...] Read more.
Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic accuracy. Simplified CWSI (CWSIsi) was used to diagnose water stress for cotton that has received four different irrigation treatments (no stress, mild stress, moderate stress, and severe stress) at the flowering and boll stage. High resolution thermal infrared and multispectral images were taken using an Unmanned Aerial Vehicle remote sensing platform at midday (local time 13:00), and stomatal conductance (gs), transpiration rate (tr), and cotton root zone soil volumetric water content (θ) were concurrently measured. The soil background pixels of thermal images were eliminated using the Canny edge detection to obtain a unimodal histogram of pure canopy temperatures. Then the wet reference temperature (Twet), dry reference temperature (Tdry), and mean canopy temperature (Tl) were obtained from the canopy temperature histogram to calculate CWSIsi. The other two methods of CWSI evaluation were empirical CWSI (CWSIe), in which the temperature parameters were determined by measuring natural reference cotton leaves, and statistical CWSI (CWSIs), in which Twet was the mean of the lowest 5% of canopy temperatures and Tdry was the air temperature (Tair) + 5 °C. Compared with CWSIe, CWSIs and spectral indices (NDVI, TCARI, OSAVI, TCARI/OSAVI), CWSIsi has higher correlation with gs (R2 = 0.660) and tr (R2 = 0.592). The correlation coefficient (R) for θ (0–45 cm) and CWSIsi is also high (0.812). The plotted high-resolution map of CWSIsi shows the different distribution of cotton water stress in different irrigation treatments. These findings demonstrate that CWSIsi, which only requires parameters from a canopy temperature histogram, may potentially be applied to precision irrigation management. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessArticle
Spatial and Temporal Analysis of Precipitation and Effective Rainfall Using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin
Remote Sens. 2018, 10(12), 2058; https://doi.org/10.3390/rs10122058 - 18 Dec 2018
Abstract
Accurate spatial and temporal precipitation estimates are important for hydrological studies of irrigation depletion, net irrigation requirement, natural recharge, and hydrological water balances in defined areas. This analysis supports the verification of water savings (reduced depletion) from deficit irrigation of pastures in the [...] Read more.
Accurate spatial and temporal precipitation estimates are important for hydrological studies of irrigation depletion, net irrigation requirement, natural recharge, and hydrological water balances in defined areas. This analysis supports the verification of water savings (reduced depletion) from deficit irrigation of pastures in the Upper Colorado River Basin. The study area has diverse topography with scattered fields and few precipitation gauges that are not representative of the basin. Gridded precipitation products from TRMM-3B42, PRISM, Daymet, and gauge observations were evaluated on two case studies located in Colorado and Wyoming during the 2014–2016 irrigation seasons. First, the resolution at the farm level is discussed. Next, bias occurrence at different time scales (daily to monthly) is evaluated and addressed. Then, the coverage area of the gauge station, along with the impact of the dominant wind direction on the shape of the coverage area, is evaluated. Ultimately, available actual ET maps derived from the METRIC model are used to estimate spatial effective rainfall. The results show that the spatial resolutions of TRMM and PRISM are not adequate at the farm level, while Daymet is a better fit but lacks the adequate latency versus TRMM and PRISM. When compared against local weather station records, all three spatial datasets were found to have a bias that decreases at coarser temporal intervals. However, the performance of Daymet and PRISM at the monthly time step is acceptable, and they can be used for water resource management at the farm level. The adequacy of an existing gauge station for a given farm location depends on the willingness to accept the risk of the bias associated with a non-persistent, non-symmetric gauge coverage area that is highly correlated with the dominant wind direction. Among all goodness of fit statistics considered in the study, the interpretation of the summation of error makes more sense for quantifying the rainfall bias and risk for the user. Finally, based on the USDA-SCS model and actual spatial ET, overall, seasonal effective rainfall tends to be less than 60% of total rainfall for agricultural lands. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessEditor’s ChoiceArticle
Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing
Remote Sens. 2018, 10(12), 2001; https://doi.org/10.3390/rs10122001 - 10 Dec 2018
Cited by 3
Abstract
In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods [...] Read more.
In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (Kcb), or potential crop water use. The soil water evaporation coefficient (Ke) was measured from microlysimeters. The water stress coefficient (Ks) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ψstem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum Kcb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an R2 ≥ 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm·day−1 for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessArticle
Improving the Accuracy of Remotely Sensed Irrigated Areas Using Post-Classification Enhancement Through UAV Capability
Remote Sens. 2018, 10(5), 712; https://doi.org/10.3390/rs10050712 - 05 May 2018
Cited by 8
Abstract
Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing [...] Read more.
Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa. Landsat 8 imageries for 2015 were used, applying the maximum likelihood supervised classifier to delineate the agricultural fields. The normalized difference vegetation index (NDVI) applied on Landsat imageries on the mapped fields during the dry season (July to August) was used to identify irrigated areas, because years of satellite data analysis suggest that healthy crop conditions during dry seasons are only possible with irrigation. Ground truth points totaling 137 were collected during fieldwork for pre-processing and accuracy assessment. An accuracy of 96% was achieved on the mapped agricultural fields, yet the irrigated area map produced an initial accuracy of only 71%. This study explains and improves the 29% error margin from the irrigated areas. Accuracy was enhanced through post-classification correction (PCC) using 74 post-classification points randomly selected from the 2015 irrigated area map. High resolution aerial photographs of the 74 sample fields were acquired by an unmanned aerial vehicle (UAV) to give a clearer picture of the irrigated fields. The analysis shows that mapped irrigated fields that presented anomalies included abandoned croplands that had green invasive alien species or abandoned fruit plantations that had high NDVI values. The PCC analysis improved irrigated area mapping accuracy from 71% to 95%. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessFeature PaperArticle
Effects of Heterogeneity within Tree Crowns on Airborne-Quantified SIF and the CWSI as Indicators of Water Stress in the Context of Precision Agriculture
Remote Sens. 2018, 10(4), 604; https://doi.org/10.3390/rs10040604 - 13 Apr 2018
Cited by 8
Abstract
This research focused on understanding the effects of structural heterogeneity within tree crowns on the airborne retrieval of solar-induced chlorophyll fluorescence (SIF) and the Crop Water Stress Index (CWSI). We explored the SIF and CWSI variability observed within crowns of trees subjected to [...] Read more.
This research focused on understanding the effects of structural heterogeneity within tree crowns on the airborne retrieval of solar-induced chlorophyll fluorescence (SIF) and the Crop Water Stress Index (CWSI). We explored the SIF and CWSI variability observed within crowns of trees subjected to different water stress regimes and its effect on the relationships with leaf physiological measurements. High-resolution (20 cm) hyperspectral imagery was acquired to assess fluorescence retrieval from sunlit portions of the tree crowns using the Fraunhofer line depth method, and from entire crowns using automatic object-based tree crown detection methods. We also measured the canopy temperature distribution within tree crowns using segmentation algorithms based on temperature percentiles applied to high-resolution (25 cm) thermal imagery. The study was conducted in an almond orchard cultivated under three watering regimes in Cordoba, in southern Spain. Three airborne campaigns took place during the summer of 2015 using high-resolution hyperspectral and thermal cameras on board a manned aircraft. Relationships between SIF and the assimilation rate improved significantly when the sunlit tree crown pixels extracted through segmentation were used for all flight dates. By contrast, the SIF signal extracted from the entire tree crowns was highly degraded due to the canopy heterogeneity observed within tree crowns. The quartile crown segmentations applied to the thermal images showed that the CWSI values obtained were within the theoretically expected CWSI range only when the pixels were extracted from the 50th percentile class. However, the CWSI values were biased in the upper quartile (Q75) for all watering regimes due to the soil background effects on the calculated mean crown temperature. The relationship between the CWSI and Gs was heavily affected by the crown segmentation levels applied and improved remarkably when the CWSI values were calculated from the middle quartile crown segmentation (Q50), corresponding to the coldest and purest vegetation pixels (r2 = 0.78 in pure vegetation pixels vs. r2 = 0.52 with the warmer pixels included in the upper quartile). This study highlights the importance of using high-resolution hyperspectral and thermal imagery for pure-object segmentation extractions from tree crowns in the context of precision agriculture and water stress detection. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessTechnical Note
Satellite-Based Mapping of Cultivated Area in Gash Delta Spate Irrigation System, Sudan
Remote Sens. 2018, 10(2), 186; https://doi.org/10.3390/rs10020186 - 26 Jan 2018
Cited by 2
Abstract
In this study, a simple methodology for mapping the seasonal cultivated area of the Gash Delta Spate Irrigation System based on satellite images was developed. The methodology combined information from multiple bands to characterize the land surface in terms of spectral indices (e.g., [...] Read more.
In this study, a simple methodology for mapping the seasonal cultivated area of the Gash Delta Spate Irrigation System based on satellite images was developed. The methodology combined information from multiple bands to characterize the land surface in terms of spectral indices (e.g., Normalized Difference Vegetation Index (NDVI), and surface temperature (Ts)). Visual interpretations of a conveniently selected image were undertaken to identify and select sample points of interest. The NDVI and Ts values (computed from multi-date images that represented the crop growing period) of the sample points were used to developed typical NDVI and Ts plots. By analyzing these plots and the cropping calendar, an NDVI and Ts threshold-based algorithm was developed to extract the cultivated area of a given season. Analysis of the developed algorithm showed that it was simple, easily modifiable, and had interpretable rules and threshold values. Comparing the extracted cultivated area with the field report area showed a promising application of the methodology to map and estimate the cultivated area from only remote sensing data. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessTechnical Note
Irrigation Performance Assessment in Table Grape Using the Reflectance-Based Crop Coefficient
Remote Sens. 2017, 9(12), 1276; https://doi.org/10.3390/rs9121276 - 08 Dec 2017
Cited by 4
Abstract
In this paper, we present the results of our study on the operational application of the reflectance-based crop coefficient for assessing table grape irrigation requirements. The methodology was applied to provide irrigation advice and to assess the irrigation performance. The net irrigation water [...] Read more.
In this paper, we present the results of our study on the operational application of the reflectance-based crop coefficient for assessing table grape irrigation requirements. The methodology was applied to provide irrigation advice and to assess the irrigation performance. The net irrigation water requirements (NIWR) simulated using the reflectance-based basal crop coefficient were provided to the farmer during the growing season and compared with the actual irrigation volumes applied. Two treatments were implemented in the field, increasing and reducing the irrigation doses by 25%, respectively, compared to the regular management. The experiment was carried out in a commercial orchard during three consecutive growing seasons in Northern Chile. The NIWR based on the model was approximately 900 mm per season for the orchard at tree maturity. The experimental results demonstrate that the regular irrigation applied covered only 76% of the NIWR for the whole season, and the analysis of monthly and weekly accumulated values indicates several periods of water shortage. The regular management system tended to underestimate the water requirements from October to January and overestimate the water requirements after harvest from February to April. The level of the deficit of water was quantified using such plant physiological parameters as stem water potential, vegetative development (coverage), and fruit productivity. The estimated NIWR was roughly covered in the treatment where the irrigation dose was increased, and the analyses of the crop production and fruit quality point to the relative advantage of this treatment. Finally, we conclude that the proposed approach allows the analysis of irrigation performance on the scale of commercial fields. These analytic capabilities are based on the well-demonstrated relationship of the crop evapotranspiration with the information provided by satellite images, and provide valuable information for irrigation management by identifying periods of water shortage and over-irrigation. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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