scholarly journals Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

2019 ◽  
Vol 11 (18) ◽  
pp. 2127
Author(s):  
Polinova ◽  
Salinas ◽  
Bonfante ◽  
Brook
Keyword(s):  
Water Management ◽  
Water Supply ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Future Climate Change ◽  
Weather Forecasts ◽  
Red Edge ◽  
Cotton Fields

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

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

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.  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>

scholarly journals Improved Mapping of Mountain Shrublands Using the Sentinel-2 Red-Edge Band

2019 ◽  
Vol 11 (23) ◽  
pp. 2807 ◽  
Author(s):  
Arthur Bayle ◽  
Bradley Carlson ◽  
Vincent Thierion ◽  
Marc Isenmann ◽  
Philippe Choler
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Model Performance ◽  
Mountain Range ◽  
Rhododendron Ferrugineum ◽  
Red Edge ◽  
Rocky Habitats ◽  
Edge Band ◽  
Sentinel 2

Shrub encroachment into grassland and rocky habitats is a noticeable land cover change currently underway in temperate mountains and is a matter of concern for the sustainable management of mountain biodiversity. Current land cover products tend to underestimate the extent of mountain shrublands dominated by Ericaceae (Vaccinium spp. (species) and Rhododendron ferrugineum). In addition, mountain shrubs are often confounded with grasslands. Here, we examined the potential of anthocyanin-responsive vegetation indices to provide more accurate maps of mountain shrublands in a mountain range located in the French Alps. We relied on the multi-spectral instrument onboard the Sentinel-2A and 2B satellites and the availability of red-edge bands to calculate a Normalized Anthocyanin Reflectance Index (NARI). We used this index to quantify the autumn accumulation of anthocyanin in canopies dominated by Vaccinium spp. and Rhododendron ferrugineum and compared the effectiveness of NARI to Normalized Difference Vegetation Index (NDVI) as a basis for shrubland mapping. Photointerpretation of high-resolution aerial imagery, intensive field campaigns, and floristic surveys provided complementary data to calibrate and evaluate model performance. The proposed NARI-based model performed better than the NDVI-based model with an area under the curve (AUC) of 0.92 against 0.58. Validation of shrub cover maps based on NARI resulted in a Kappa coefficient of 0.67, which outperformed existing land cover products and resulted in a ten-fold increase in estimated area occupied by Ericaceae-dominated shrublands. We conclude that the Sentinel-2 red-edge band provides novel opportunities to detect seasonal anthocyanin accumulation in plant canopies and discuss the potential of our method to quantify long-term dynamics of shrublands in alpine and arctic contexts.

scholarly journals Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data

2020 ◽  
Vol 12 (24) ◽  
pp. 4058
Author(s):  
Hassan Bazzi ◽  
Nicolas Baghdadi ◽  
Ibrahim Fayad ◽  
François Charron ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Water Management ◽  
Time Series ◽  
Vegetation Index ◽  
Irrigation Schedule ◽  
Threshold Value ◽  
Climatic Conditions ◽  
Irrigated Agriculture ◽  
Sar Image ◽  
Detection Model

Better management of water consumption and irrigation schedule in irrigated agriculture is essential in order to save water resources, especially at regional scales and under changing climatic conditions. In the context of water management, the aim of this study is to monitor irrigation activities by detecting the irrigation episodes at plot scale using the Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) time series over intensively irrigated grassland plots located in the Crau plain of southeast France. The method consisted of assessing the newly developed irrigation detection model (IDM) at plot scale over the irrigated grassland plots. First, four S1-SAR time series acquired from four different S1-SAR acquisitions (different S1 orbits), each at six-day revisit time, were obtained over the study site. Next, the IDM was applied at each available SAR image from each S1-SAR series to obtain an irrigation indicator at each SAR image (no, low, medium, or high irrigation possibility). Then, the irrigation indicators obtained at each image from each S1-SAR time series (four series) were added and combined by threshold value criteria to determine the existence or absence of an irrigation event. Finally, the performance of the IDM for irrigation detection was assessed by comparing the in situ recorded irrigation events at each plot and the detected irrigation events. The results show that using only the VV polarization, 82.4% of the in situ registered irrigation events are correctly detected with an F_score value reaching 73.8%. Less accuracy is obtained using only the VH polarization, where 79.9% of the in situ irrigation events are correctly detected with an F_score of 72.2%. The combined use of the VV and VH polarization showed that 74.1% of the irrigation events are detected with a higher F_score value of 76.4%. The analysis of the undetected irrigation events revealed that, in the presence of very well-developed vegetation cover (normalized difference of vegetation index (NDVI) ≥ 0.8); higher uncertainty in irrigation detection is observed, where 80% of the undetected events correspond to an NDVI value greater than 0.8. The results also showed that small-sized plots encounter more false irrigation detections than large-sized plots certainly because the pixel spacing of S1 data (10 m × 10 m) is not adapted to small size plots. The obtained results prove the efficiency of the S1 C-band data and the IDM for detecting irrigation events at the plot scale, which would help in improving the irrigation water management at large scales especially with availability and global coverage of the S1 product.

scholarly journals Evapotranspiration Partition and Dual Crop Coefficients in Apple Orchard with Dwarf Stocks and Dense Planting in Arid Region, Aksu Oasis, Southern Xinjiang

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1167
Author(s):  
Hui Cao ◽  
Hongbo Wang ◽  
Yong Li ◽  
Abdoul Kader Mounkaila Hamani ◽  
Nan Zhang ◽  
...  
Keyword(s):  
Water Management ◽  
Water Productivity ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Apple Orchard ◽  
Irrigation Amount ◽  
Late Season ◽  
Apple Production ◽  
Crop Coefficients ◽  
Southern Xinjiang

Crop coefficients are critical to developing irrigation scheduling and improving agricultural water management in farmland ecosystems. Interest in dwarf cultivation with high density (DCHD) for apple production increases in Aksu oasis, southern Xinjiang. The lack of micro-irrigation scheduling limits apple yield and water productivity of the DCHD-cultivated orchard. A two-year experiment with the DCHD-cultivated apple (Malus × domestica ‘Royal Gala’) orchard was conducted to determine crop coefficients and evapotranspiration (ETa) with the SIMDualKc model, and to investigate apple yield and water productivity (WP) in response to different irrigation scheduling. The five levels of irrigation rate were designed as W1 of 13.5 mm, W2 of 18.0 mm, W3 of 22.5 mm, W4 of 27.0 mm, and W5 of 31.5 mm. The mean value of basal crop coefficient (Kcb) at the initial-, mid-, and late-season was 1.00, 1.30, and 0.89, respectively. The Kc-local (ETa/ET0) range for apple orchard with DCHD was 1.11–1.20, 1.33–1.43, and 1.09–1.22 at the initial, middle, and late season, respectively. ETa of apple orchard in this study ranged between 415.55–989.71 mm, and soil evaporation accounted for 13.85–29.97% of ETa. Relationships between total irrigation amount and apple yield and WP were developed, and W3 was suggested as an optimum irrigation schedule with an average apple yield of 30,540.8 kg/ha and WP of 4.45 kg/m3 in 2019–2020. The results have implications in developing irrigation schedules and improving water management for apple production in arid regions.

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

2019 ◽  
Vol 11 (17) ◽  
pp. 2069 ◽  
Author(s):  
Gobbo ◽  
Presti ◽  
Martello ◽  
Panunzi ◽  
Berti ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Water Status ◽  
Irrigation Management ◽  
Growing Season ◽  
Venice Lagoon ◽  
Irrigation Scheduling ◽  
Crop Water ◽  
Yield Map

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.

scholarly journals Simulating the Leaf Area Index of Rice from Multispectral Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3663
Author(s):  
Shenzhou Liu ◽  
Wenzhi Zeng ◽  
Lifeng Wu ◽  
Guoqing Lei ◽  
Haorui Chen ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Estimation Accuracy ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Area Index ◽  
Red Edge ◽  
Extreme Gradient Boosting

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals IRRIGATION SCHEDULING CALCULATOR (ISC) TO IMPROVE WATER MANAGEMENT ON FIELD LEVEL IN EGYPT

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Samiha Abou El-Fetouh Hamed Ouda
Keyword(s):  
Water Management ◽  
Root Zone ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Weather Data ◽  
Irrigation Amount ◽  
Daily Weather Data ◽  
Daily Evapotranspiration ◽  
Simulation Results ◽  
Monteith Equation

The developed model is MS excel sheet called “Irrigation Scheduling Calculator, ISC”. The model requires to input daily weather data to calculate daily evapotranspiration using Penman-Monteith equation. The model calculates water depletion from the root zone to determine when to irrigate and how much water should be applied. The charge from irrigation pump is used to calculate how many hours should the farmer run the pump to deliver the needed amount of water. ISC model was used to developed irrigation schedule for wheat and maize planted in El-Gharbia governorate. The developed schedules were compared to the actual schedules for both crops. Furthermore, CropSyst model was calibrated for both crops and run using the developed schedules by ISC model. The simulation results indicated that the calculated irrigation amount by ISC model for wheat was lower than actual schedule by 6.0 mm. Furthermore, the simulated wheat productivity by CropSyst was higher than measured grain and biological by 2%. Similarly, the calculated applied irrigation amount by ISC model for maize was lower than actual schedule by 79.0 mm and the productivity was not changed.

scholarly journals Estimación de la evapotranspiración de maíz forrajero apoyada con sensores remotos y mediciones in situ.

2019 ◽  
Vol 37 (3) ◽  
pp. 279 ◽  
Author(s):  
Arturo Reyes González ◽  
David Guadalupe Reta Sánchez ◽  
Juan Isidro Sánchez Duarte ◽  
Esmeralda Ochoa Martínez ◽  
Karla Rodríguez Hernández ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation System ◽  
Vegetation Indices ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Crop Development ◽  
Subsurface Drip

Irrigated agriculture requires better estimates of crop water demand. The aim of this study was to estimate the evapotranspiration (ETc) in forage corn through vegetation indices obtained in situ and estimated with remote sensing in the Comarca Lagunera, Mexico. The research was carried out in 2011 and 2012 in four 900 m2 plots irrigated with a subsurface drip irrigation system. Normalized Difference Vegetation Index (NDVI) and crop coeff icient (Kc) during crop development were determined. The initial, maximum and f inal NDVI values were 0.13, 0.79 and 0.63 for both methods and in both cycles. The maximum Kc values were obtained 54 and 48 days after sowing (DDS) with GreenSeeker, and at 61 and 59 DDS with satellite images in 2011 and 2012, respectively. The results showed a good relationship between ETc estimated in situ and ETc estimated with remote sensing (r = 0.98) for both years. Although the variation of ETc using both methods was 1.2 mm day‑1, early in the cycle and 7.4 mm day-1 to flowering start-milky grains. Water needs of forage corn were estimated with similar precision using remote sensing and in situ measurements. Therefore, both methods can be used to improve irrigation scheduling and preserve water resources in agriculture.

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