scholarly journals Managing Agricultural Water Considering Water Allocation Priority Based on Remote Sensing Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1536
Author(s):  
Biao Luo ◽  
Fan Zhang ◽  
Xiao Liu ◽  
Qi Pan ◽  
Ping Guo
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Grain Yield ◽  
Water Allocation ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Proportional Fairness ◽  
Agricultural Output ◽  
Sensing Data ◽  
Spatial Method

To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the water supply response unit was divided according to canal system conditions. Then, a spatialization method was used for generating spatial agricultural output value (income from planting industry) and grain yield (yield of food crops) with the help of NDVI and the potential yield of farmland. Third, the AHP-TOPSIS method was employed to calculate the water allocation priority based on the above information. Finally, the evaluation results were integrated with a nonlinear multiobjective model to optimally allocate agricultural land and water resources, considering the combined objective of minimum envy and proportional fairness. The method was applied to Hetao irrigation area, an arid agriculture-dominant region in Northwest China. After solving the model, optimization alternatives were obtained, which indicate that: (1) the spatial method of agricultural output value can improve the accuracy by around 16% compared with the traditional method, and the spatial method of grain yield also have good accuracy (MAPE = 14.66%); (2) the rank of water allocation priority can reflect more spatial information, and provide practical decision support for the distribution of water resources; (3) the envy index can better improve the efficiency of an allocation system compared to the Gini coefficient method.

scholarly journals Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2014
Author(s):  
Celina Aznarez ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Juan Pablo Pacheco ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ecosystem Services ◽  
Water Resources ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Erosion Control ◽  
Sensing Data ◽  
Extreme Precipitation Events ◽  
The Impact

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

77. Producing grain yield maps by merging combine harvester and remote sensing data

2021 ◽  
Author(s):  
M. Karampoiki ◽  
A. Heiß ◽  
G.M. Sharipov ◽  
S. Mahmood ◽  
L.C. Todman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Combine Harvester

Local sensitivity analysis of Satellite Monitoring of Irrigation software (SAMIR) over Semi-Arid climate of Morocco

2021 ◽  
Author(s):  
Aicha Moumni ◽  
Alhousseine Diarra ◽  
Abderrahman Lahrouni
Keyword(s):  
Remote Sensing ◽  
Sensitivity Analysis ◽  
Water Resources ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Soil Parameters ◽  
Sensing Data ◽  
Annual Crops ◽  
Irrigated Area ◽  
Semi Arid

<p>Nowadays, the assessment of agricultural management is based mainly on the good management of water resources (i.e., to estimate the crops water consumption and provide their irrigation requirements). In this context, several agro-environmental models, (i.e., STICS, AQUACROP, TSEB, …) have been developed to assess the agricultural needs such as grain yield and/or irrigation demand prediction. These models are mainly based on the remote sensing data which contribute highly to the knowledge of some key-variables of crop models, in particular their time and space variations. The study area is the Haouz plain located in central Morocco. The climate of the plain is semi-arid continental type characterized by strong spatiotemporal irregular rains (mean annual precipitation up to 250 mm).The region relies mainly on the agricultural activities. Therefore, about 85% of available water is used for irrigated crops within the plain. The irrigated area is covered by 25% tree plantations and 75% annual crops. However, the annual crops extent depends strongly on the water availability during the season. Hence, for sustainable monitoring and optimal use of water resources (using physical modeling, satellite images and ground data), SAMIR software is developed in order to spatialize the irrigation water budget over Haouz plain. SAMIR (Simonneaux et al., 2009; Saadi et al., 2015; Tazekrit et al., 2018) is a tool for irrigation management based mainly on the use of remote sensing data. It estimates the crop evapotranspiration (ET) based on the FAO-56 model. This model requires three types of data: climatic variables for calculation of reference Evapotranspiration (ET0), land cover for computing crop coefficient Kc, and periodical phonological information for adjusting the Kc. SAMIR offers the possibility to calculate the ET of a large agricultural areas, with different land use/ land cover types, and subsequently deduce the necessary water irrigation for these areas. This model has been calibrated and validated over R3 perimeter (Diarra et al., 2017). In the present work, we studied the sensitivity (local sensibility analysis) of SAMIR software to the variations of each input parameter (i.e., ET0, precipitations, soil parameters, and irrigation configuration “real or automatic”). The simulations were made using the ground truth observations and irrigation dataset of the agricultural season of 2011/2012 over an irrigated area of Haouz plain. For the climatic variables, the obtained results showed that the effect of the ET0 is more significant compared to the effect of precipitations. It led to large shifts of the actual ET simulated by SAMIR compared to all tested parameters. For soil parameters, the sensitivity analysis illustrates that the effect is almost linear for all parameters. But the proportion of total available water, P, is the high sensitive parameter (Lenhart, et al., 2002). Finally, the comparison between the simulation of real evapotranspiration using automatic irrigation or real irrigation configuration offers an interesting result. The obtained ET values are similar for both configurations. Thus, this result offers the possibility of using only automatic irrigation configuration, in case of non-availability of the real irrigation.</p>

The use of remote sensing data to extract information from agricultural land with emphasis on soil salinity

Soil Research ◽  
2003 ◽  
Vol 41 (7) ◽  
pp. 1243 ◽  
Author(s):  
F. M. Howari
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Large Scale ◽  
Agricultural Land ◽  
Rio Grande Valley ◽  
Remote Sensing Data ◽  
Rio Grande ◽  
The United States ◽  
Sensing Data ◽  
Matching Techniques

The rapid growth of information technologies has provided exciting new sources of data, interpretation tools, and modelling techniques to soil research and education communities at all levels. This paper presents some examples of the capability of remote sensing data such as Landsat ETM+, airborne visible/infrared imaging spectrometer (AVIRIS), colour infrared aerial photos (CIR), and high-resolution field spectroradiometer (GER 3700) to extract surface information about soil salinity. The study used image processing techniques such as supervised classification, spectral extraction, and matching techniques to investigate types and occurrences of salts in the Rio Grande Valley on the United States–Mexico border. Soil salinity groups were established using soil physico-chemical properties and image elements (absorption-reflectivity profiles, band combinations, grey tones of the investigated images, and textures of soil and vegetation covers as they appear in images). The lack of vegetation or scattered vegetation on salt-affected soil (SAS) surfaces makes it possible to detect salt in several locations of the investigated area. The presented remote sensing datasets reveal the presence of gypsum and halite as the dominant salt crusts in the Rio Grande Valley. This information can help agricultural scientists and engineers to produce large-scale maps of salt-affected lands, which will help improve salinity management in watersheds and ecosystems.

scholarly journals Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1279
Author(s):  
Dominik Scholand ◽  
Britta Schmalz
Keyword(s):  
Remote Sensing ◽  
Success Rate ◽  
Soil Conservation ◽  
Soil Loss ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Mountain Range ◽  
Sensing Data ◽  
P Factor

The P-factor for support practice of the Universal Soil Loss Equation (USLE) accounts for soil conservation measures and leads to a significant reduction in the modelled soil loss. However, in the practical application, the P-factor is the most neglected factor overall due to high effort for determining or lack of input data. This study provides a new method for automatic derivation of the main cultivation direction from seed rows and tramlines on agricultural land parcels using the Fast Line Detector (FLD) of the Open Computer Vision (OpenCV) package and open remote sensing data from Google Earth™. Comparison of the cultivation direction with the mean aspect for each land parcel allows the determination of a site-specific P-factor for the soil conservation measure contouring. After calibration of the FLD parameters, the success rate in a first application in the low mountain range Fischbach catchment, Germany, was 77.7% for 278 agricultural land parcels. The main reasons for unsuccessful detection were problems with headland detection, existing soil erosion, and widely varying albedo within the plots as well as individual outliers. The use of a corrected mask and enhanced parameterization offers promising improvements for a higher success rate of the FLD.

Sign in / Sign up

Export Citation Format

Share Document