Integrating SEBAL with in-Field Crop Water Status Measurement for Precision Irrigation Applications—A Case Study

The surface energy balance algorithm for land (SEBAL) has been demonstrated to provide accurate estimates of crop evapotranspiration (ET) and yield at different spatial scales even under highly heterogeneous conditions. However, validation of the SEBAL using in-field direct and indirect measurements of plant water status is a necessary step before deploying the algorithm as an irrigation scheduling tool. To this end, a study was conducted in a maize field located near the Venice Lagoon area in Italy. The experimental area was irrigated using a 274 m long variable rate irrigation (VRI) system with 25-m sections. Three irrigation management zones (IMZs; high, medium and low irrigation requirement zones) were defined combining soil texture and normalized difference vegetation index (NDVI) data. Soil moisture sensors were installed in the different IMZs and used to schedule irrigation. In addition, SEBAL-based actual evapotranspiration (ETr) and biomass estimates were calculated throughout the season. VRI management allowed crop water demand to be matched, saving up to 42 mm (−16%) of water when compared to uniform irrigation rates. The high irrigation amounts applied during the growing season to avoid water stress resulted in no significant differences among the IMZs. SEBAL-based biomass estimates agreed with in-season measurements at 72, 105 and 112 days after planting (DAP; r2 = 0.87). Seasonal ET matched the spatial variability observed in the measured yield map at harvest. Moreover, the SEBAL-derived yield map largely agreed with the measured yield map with relative errors of 0.3% among the IMZs and of 1% (0.21 t ha-1) for the whole field. While the FAO method-based stress coefficient (Ks) never dropped below the optimum condition (Ks = 1) for all the IMZs and the uniform zone, SEBAL Ks was sensitive to changes in water status and remained below 1 during most of the growing season. Using SEBAL to capture the daily spatial variation in crop water needs and growth would enable the definition of transient, dynamic IMZs. This allows farmers to apply proper irrigation amounts increasing water use efficiency.

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Remote Sensing for Plant Water Content Monitoring: A Review

Remote Sensing ◽

10.3390/rs13112088 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2088

Author(s):

Carlos Quemada ◽

José M. Pérez-Escudero ◽

Ramón Gonzalo ◽

Iñigo Ederra ◽

Luis G. Santesteban ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Continuous Wave ◽

Technological Development ◽

Water Status ◽

Irrigation Management ◽

Coefficient Of Determination ◽

Plant Water ◽

Monitoring Methods

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

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An Empirical Approach for Detecting Crop Water Stress Using Multispectral Airborne Sensors

HortTechnology ◽

10.21273/horttech.7.1.9 ◽

1997 ◽

Vol 7 (1) ◽

pp. 9-16 ◽

Cited By ~ 36

Author(s):

Thomas R. Clarke

Keyword(s):

Water Stress ◽

Vegetation Index ◽

Near Infrared ◽

Water Status ◽

Irrigation Scheduling ◽

Stress Index ◽

Crop Water ◽

Crop Water Stress Index ◽

Airborne Sensors ◽

Crop Water Stress

Irrigation scheduling can be improved by directly monitoring plant water status rather than depending solely on soil water content measurements or modeled evapotranspiration estimates. Plants receiving sufficient water through their roots have cooler leaves than those that are water stressed, leading to the development of the crop water stress index, which uses hand-held infrared thermometers as tools for scheduling irrigations. However, substantial error can occur in partial canopies when a downward-pointing infrared thermometer measures leaf temperature and the temperature of exposed, hot soil. To overcome this weakness, red and near-infrared images were combined mathematically as a vegetation index, which was used to provide a crop-specific measure of vegetative cover. Coupling the vegetation index with the paired radiant surface temperature from a thermal image, a trapezoidal two-dimensional index was empirically derived capable of detecting water stress even with a low percentage of canopy cover. Images acquired with airborne sensors over subsurface drip-irrigated muskmelon (Cucumis melo L.) fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks. Although the procedure needs to be automated for faster image processing, the approach is an advance in irrigation scheduling and water stress detection technology.

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Assessing the Water-Stress Baselines by Thermal Imaging for Irrigation Management in Almond Plantations under Water Scarcity Conditions

Water ◽

10.3390/w12051298 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1298

Author(s):

Saray Gutiérrez-Gordillo ◽

Iván Francisco García-Tejero ◽

Víctor Hugo Durán Zuazo ◽

Amelia García Escalera ◽

Fernando Ferrera Gil ◽

...

Keyword(s):

Water Stress ◽

Thermal Imaging ◽

Water Status ◽

Irrigation Management ◽

Irrigation Treatment ◽

Canopy Temperature ◽

Irrigation Scheduling ◽

Stress Index ◽

Crop Water ◽

Crop Water Stress Index

This work examines the use of thermal imaging to determine the crop water status in young almond trees under sustained deficit irrigation strategies (SDIs). The research was carried out during two seasons (2018–2019) in three cultivars (Prunus dulcis Mill., cvs. Guara, Lauranne, and Marta) subjected to three irrigation treatments: a full irrigation treatment (FI) at 100% of irrigation requirements (IR), and two SDIs that received 75% and 65% of the IR, respectively. Crop water monitoring was done by measurements of canopy temperature, leaf water potential (Ψleaf), and stomatal conductance. Thermal readings were used to define the non-water-stress baselines (NWSB) and water-stress baselines (WSB) for each treatment and cultivar. According to our findings, Ψleaf was the most responsive parameter to reflect differences in almond water status. In addition, NWSB and WSB allowed the determination of the crop water-stress index (CWSI) and the increment of canopy temperature (ITC) for each SDI treatment, obtaining threshold values of CWSI (0.12–0.15) and ITC (~1 °C) that would ensure maximum water savings by minimizing the effects on yield. The findings highlight the importance of determining the different NWSB and WSB for different almond cultivars and its potential use for proper irrigation scheduling.

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Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

Mathematical Problems in Engineering ◽

10.1155/2017/5209173 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Siqin Tong ◽

Yuhai Bao ◽

Rigele Te ◽

Qiyun Ma ◽

Si Ha ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Pearson Correlation ◽

Growing Season ◽

Vegetation Coverage ◽

Semiarid Region ◽

Linear Regression Method ◽

Upward Trend ◽

Drought Effect ◽

Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

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Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation

Sensors ◽

10.3390/s18113676 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3676 ◽

Cited By ~ 5

Author(s):

Hao Chen ◽

Xiangnan Liu ◽

Chao Ding ◽

Fang Huang

Keyword(s):

Trend Analysis ◽

Land Degradation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growing Season ◽

Practical Method ◽

Large Area ◽

Modis Ndvi ◽

Daily Precipitation Data ◽

Improved Model

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000–2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

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Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

Remote Sensing ◽

10.3390/rs10101601 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1601 ◽

Cited By ~ 7

Author(s):

Carl Talsma ◽

Stephen Good ◽

Diego Miralles ◽

Joshua Fisher ◽

Brecht Martens ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Scales ◽

Laboratory Model ◽

Moderate Resolution Imaging Spectroradiometer ◽

Input Variables ◽

Added Uncertainty ◽

Model Components ◽

The Individual

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

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Impacts of temperature extremes on European vegetation during the growing season

10.5194/bg-2017-189 ◽

2017 ◽

Author(s):

Lukas Baumbach ◽

Jonatan F. Siegmund ◽

Magdalena Mittermeier ◽

Reik V. Donner

Keyword(s):

Land Cover ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Extreme Temperature ◽

Terrestrial Ecosystems ◽

Growing Season ◽

Temperature Extremes ◽

Minor Effect ◽

European Continent

Abstract. Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.

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Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Remote Sensing ◽

10.3390/rs10081293 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1293 ◽

Cited By ~ 26

Author(s):

Yunpeng Luo ◽

Tarek S. El-Madany ◽

Gianluca Filippa ◽

Xuanlong Ma ◽

Bernhard Ahrens ◽

...

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Growing Season ◽

Plant Phenology ◽

Mediterranean Ecosystem ◽

Near Infrared Reflectance ◽

Seasonally Dry ◽

Growing Season Length ◽

Dry Down

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

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Improved analysis of thermally sensed crop water status and mapping spatial variability for site specific irrigation scheduling

10.32747/2008.7613873.bard ◽

2008 ◽

Author(s):

Victor Alchanatis ◽

Steven Evett ◽

Shabtai Cohen ◽

Yafit Cohen ◽

Moshe Meron ◽

...

Keyword(s):

Spatial Variability ◽

Water Status ◽

Irrigation Scheduling ◽

Crop Water ◽

Site Specific

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Inter-comparison of remotely-sensed actual evapotranspiration products in the Zayandehrud river basin, Iran

10.5194/egusphere-egu21-103 ◽

2021 ◽

Author(s):

Neda Abbasi ◽

Hamideh Nouri ◽

Sattar Chavoshi Borujeni ◽

Pamela Nagler ◽

Christian Opp ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Field Measurements ◽

Water Shortage ◽

Remotely Sensed ◽

Irrigation Scheduling ◽

Accurate Estimation ◽

Agricultural Areas ◽

Eta Products

<p>Accurate estimation of evapotranspiration (ET) helps to create a better understanding of water allocation, irrigation scheduling, and crop management especially in arid and semiarid regions where agricultural areas are far more affected by water shortage and drought events. Remote sensing (RS) facilitates estimating the ET in regions where long-term field measurements are missed.&#160; In this study, we compare the performance of free open-access remotely sensed actual ET products at eleven counties of the Zayandehrud basin. The Zayandehrud basin, one of the major watersheds of Iran, suffers from recurrent droughts and long-term impacts of aridity. The RS products used in this study are namely WaPOR (2009-2019), MOD16A2 (2003-2019), SSEBOp (2003-2019). We also merged the two products of SSEBOp and WaPOR and assessed its performance. To prepare the Merged ETa Product (MEP), WaPOR was resampled to the spatial resolution of SSEBOp. Then, the average pixel values of the resampled ETa product and SSEBOp were calculated. To compare ETa estimations over croplands in each county, maximum Normalized Difference Vegetation Index (NDVI) maps at annual scale (2003-2019) were prepared using LANDSAT 5, 7, and 8 images. Annual mean ETa estimations were then extracted over croplands by using annual maximum NDVI layers. We compared the RS-based ETa with reported long-term ETa values extracted from the local available literature. Our results showed a consistent underestimation of MOD16A2 in all counties. The MEP and WaPOR outperformed other products in the estimation of ETa in seven. Estimations of WaPOR and SSEBOp agreed in most of the counties. Our analysis displayed that, although MOD16A2 underestimated ETa values, it could together with SSEBOp capture the drought better than that of WaPOR and MEP in the lower reaches of the basin. Further study is needed to evaluate the monthly and seasonal performance of RS-based ETa products.</p>

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