scholarly journals Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing

2018 ◽  
Author(s):  
Joaquim Bellvert ◽  
Karine Adeline ◽  
Shahar Baram ◽  
Lars Pierce ◽  
Blake Sanden ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Remotely Sensed ◽  
Actual Evapotranspiration ◽  
Water Evaporation ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Field Scale ◽  
Crop Coefficients

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 (Ystem). 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.

scholarly journals Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing

2018 ◽  
Vol 10 (12) ◽  
pp. 2001 ◽  
Author(s):  
Joaquim Bellvert ◽  
Karine Adeline ◽  
Shahar Baram ◽  
Lars Pierce ◽  
Blake Sanden ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Remotely Sensed ◽  
Actual Evapotranspiration ◽  
Water Evaporation ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Field Scale ◽  
Crop Coefficients

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.

scholarly journals Estimation of Arecanut Crop Evapotranspiration Rate using Remote Sensing

2020 ◽  
Vol 9 (4) ◽  
pp. 1814-1817
Keyword(s):  
Remote Sensing ◽  
Hydrological Cycle ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Evapotranspiration Rate

Arecanut is a plantation crop sustains for decades and its crop water demand varies with the age. For scheduling and management of irrigation water, crop water requirement information is important. To calculate the crop water requirement, estimation of evapotranspiration is crucial. The term Evapotranspiration (ET) refers to transport of water molecules into the atmosphere from soil (soil evaporation) and vegetation (transpiration) surfaces. It is a most important component of hydrological cycle and also the most difficult factor to quantify. Crop water need is the amount of water required for balancing loss due to evapotranspiration. There are different methods proposed by researchers for the estimation of evapotranspiration. The conventional methods of evapotranspiration estimation from ground data are tedious. The advancement in remote sensing data provides estimation of evapotranspiration in a global scale. The invention of thermal remote sensing has benefitted greatly since it reduces the field data requirement for estimation of ET. It also helps to understand spatial distribution of landmass and different estimates also in estimation of evapotranspiration over a larger extent timely and periodically. In this study to estimate Arecanut crop evapotranspiration Hargreaves Samani, Penman Monteith and Priestly Taylor methods were used and compared. Arecanut crop evapotranspiration rate estimated form Landsat 8 and MODIS data are showed similar range of values between 3 to 4.45 mm/day. The study area covers an area of 835.3 hectares of Arecanut crop and the gross crop water need is found to be 23059 m 3 .

scholarly journals Water Stress Regime of Irrigated Crops Based on Remote Sensing and Ground-Based Data

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1117
Author(s):  
Anatoly Mikhailovich Zeyliger ◽  
Olga Sergeevna Ermolaeva
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Crop Yield ◽  
Satellite Monitoring ◽  
Agricultural Crop ◽  
Crop Water ◽  
Stress Regime ◽  
Irrigation Water Use ◽  
Crop Water Stress ◽  
Crop Irrigation

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.

scholarly journals Water stress coefficient determined by orbital remote sensing techniques

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

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

scholarly journals Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand

2019 ◽  
Vol 11 (14) ◽  
pp. 1684 ◽  
Author(s):  
Chao Zhang ◽  
Jiangui Liu ◽  
Taifeng Dong ◽  
Elizabeth Pattey ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Winter Wheat ◽  
Water Demand ◽  
Stress Condition ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Actual Evapotranspiration ◽  
Sensing Data ◽  
Water Stress Condition

Accurate information of crop growth conditions and water status can improve irrigation management. The objective of this study was to evaluate the performance of SAFYE (simple algorithm for yield and evapotranspiration estimation) crop model for simulating winter wheat growth and estimating water demand by assimilating leaf are index (LAI) derived from canopy reflectance measurements. A refined water stress function was used to account for high crop water stress. An experiment with nine irrigation scenarios corresponding to different levels of water supply was conducted over two consecutive winter wheat growing seasons (2013–2014 and 2014–2015). The calibration of four model parameters was based on the global optimization algorithms SCE-UA. Results showed that the estimated and retrieved LAI were in good agreement in most cases, with a minimum and maximum RMSE of 0.173 and 0.736, respectively. Good performance for accumulated biomass estimation was achieved under a moderate water stress condition while an underestimation occurred under a severe water stress condition. Grain yields were also well estimated for both years (R2 = 0.83; RMSE = 0.48 t∙ha−1; MRE = 8.4%). The dynamics of simulated soil moisture in the top 20 cm layer was consistent with field observations for all scenarios; whereas, a general underestimation was observed for total water storage in the 1 m layer, leading to an overestimation of the actual evapotranspiration. This research provides a scheme for estimating crop growth properties, grain yield and actual evapotranspiration by coupling crop model with remote sensing data.

scholarly journals Estimation of Crop Water Deficit in Lower Bari Doab, Pakistan Using Reflection-Based Crop Coefficient

2020 ◽  
Vol 9 (3) ◽  
pp. 173
Author(s):  
Muhammad Asif Javed ◽  
Sajid Rashid Ahmad ◽  
Wakas Karim Awan ◽  
Bilal Ahmed Munir
Keyword(s):  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Coefficient ◽  
Maximum Temperature ◽  
Filter Method ◽  
Command Area ◽  
Landsat 8 ◽  
Crop Water ◽  
Crop Coefficients

There is a global realization in all governmental setups of the need to provoke the efficient appraisal of crop water budgeting in order to manage water resources efficiently. This study aims to use the satellite remote sensing techniques to determine the water deficit in the crop rich Lower Bari Doab Canal (LBDC) command area. Crop classification was performed using multi-temporal NDVI profiles of Landsat-8 imagery by distinguishing the crop cycles based on reflectance curves. The reflectance-based crop coefficients (Kc) were derived by linear regression between normalized difference vegetation index (NDVI) cycles of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MYD13Q1 products and Food and Agriculture Organization (FAO) defined crop coefficients. A MODIS 250 m NDVI product of the last 10 years (2004-2013) was used to identify the best performing crop cycle using Fourier filter method. The meteorological parameters including rainfall and temperature substantiated the reference evapotranspiration (ET0) calculated using the Hargreaves method. The difference of potential ET and actual ET, derived from the reflectance-based Kc calculated using reference NDVI and current NDVI, generates the water deficit. Results depict the strong correlation between ET, temperature and rainfall, as the regions having maximum temperature resulted in high ET and low rainfall and vice versa. The derived Kc values were observed to be accurate when compared with the crop calendar. Results revealed maximum water deficit at middle stage of the crops, which were observed to be particularly higher at the tail of the canal command. Moreover, results also depicted that kharif (summer) crops suffer higher deficit in comparison to rabi (winter) crops due to higher ET demand caused by higher temperature. Results of the research can be utilized for rational allocation of canal supplies and guiding farmers towards usage of alternate sources to avoid crop water stress.

Monitoring evapotranspiration and water stress of Mediterranean oak savannas using optical and thermal remote sensing-based approaches

2020 ◽  
Author(s):  
Elisabet Carpintero ◽  
Ana Andreu ◽  
Pedro J. Gómez-Giráldez ◽  
María P. González-Dugo
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Energy Balance ◽  
Water Balance ◽  
Soil Water ◽  
Continuous Monitoring ◽  
Spectral Response ◽  
Balance Model ◽  
Landsat 8 ◽  
Management Actions

<p>In water-controlled systems, the evapotranspiration (ET) is a key indicator of the ecosystem health and the water status of the vegetation. Continuous monitoring of this variable over Mediterranean savannas (landscape consisting of widely-spaced oak trees combined with pasture, crops and shrubs) provides the baseline required to evaluate actual threats (e.g. vulnerable areas, land-use changes, invasive species, over-grazing, bush encroachment, etc.) and design management actions leading to reduce the economic and environmental vulnerability. However, the patched nature of these agropastoral ecosystems, with different uses (agricultural, farming, hunting), and their complex canopy structure, with various layers of vegetation and bare soil, pose additional difficulties. The combination of satellite mission with high/medium spatial/temporal resolutions provides appropriate information to characterize the variability of the Mediterranean savanna, assessing resource availability at local scales.</p><p>The aim of this work is to quantify ET and water stress at field-scale over a dehesa ecosystem located in Southern Spain, coupling remote sensing-based water and energy balance models. A soil water balance has been applied for five consecutive hydrological years (between 2012 and 2017) using the vegetation index (VI) based approach (VI-ETo model), on a daily scale and 30 m of spatial resolution. It combines FAO56 guidelines with the spectral response in the visible and near-infrared regions to compute more accurately the canopy transpiration. Landsat-8 and Sentinel-2 images, meteorological, and soil data have been used. This approach has been adapted to dehesa ecosystem, taking into account the double strata of annual grasses and tree canopies. However, the lack of available information about the spatial distribution of soil properties and the presence of multiple vegetation layers with very different root depths increase the uncertainty of water balance calculations. The combination with energy balance-based models may overcome these issues. In this case, the two-source energy balance model (TSEB) has been applied to explore the possibilities of integrating both approaches.  ET was estimated using TSEB in the days with available thermal data, more accurately assessing the reduction on ET due to soil water deficit, and allowing the adjustment of water stress coefficient in the VI-ETo model.</p><p>The modeled ET results have been validated with field observations (Santa Clotilde; 38º12’N, 4º17’ W; 736 m a.s.l.), measuring the energy balance components with an eddy covariance system and complementary instruments. The VI-ETo model has proven to be robust to monitor the vegetation water use of this complex ecosystem. However, the integration of the energy balance modelling has improved the estimations during the dry periods, with highly stressed vegetation, enabling a continuous monitoring of ET and water stress over this landscape.</p>

Crop Water Stress Detection Using Remote Sensing

2005 ◽  
Author(s):  
M. Susan Moran ◽  
Pablo J. Zarco-Tejada ◽  
Thomas R. Clarke
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Stress Detection ◽  
Crop Water ◽  
Crop Water Stress

scholarly journals Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard

2021 ◽  
Vol 13 (14) ◽  
pp. 2775
Author(s):  
Suyoung Park ◽  
Dongryeol Ryu ◽  
Sigfredo Fuentes ◽  
Hoam Chung ◽  
Mark O’Connell ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Data Acquisition ◽  
Irrigation Scheduling ◽  
Physiological Parameters ◽  
Acquisition Time ◽  
Plant Water ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Plant Water Stress

Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.

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