Irrigation Estimates from Remote Sensing Soil Moisture: A District-Scale Analysis in Spain

2021 ◽  
Author(s):  
Jacopo Dari ◽  
Pere Quintana-Seguí ◽  
María José Escorihuela ◽  
Vivien Stefan ◽  
Renato Morbidelli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Primary Source ◽  
Data Sets ◽  
Crop Evapotranspiration ◽  
Data Set ◽  
North East ◽  
The North ◽  
Food And Agriculture ◽  
Food And Agriculture Organization

<p>Irrigation represents a primary source of anthropogenic water consumption, whose effects impact on the natural distribution of water on the Earth’s surface and on food production. Over anthropized basins, irrigation often represents the missing variable to properly close the hydrological balance. Despite this, detailed information on the amounts of water actually applied for irrigation is lacking worldwide. In this study, a method to estimate irrigation volumes applied over a heavily irrigated area in the North East of Spain through high-resolution (1 km) remote sensing soil moisture is presented. Two DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled data sets have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity). The SMAP experiment covers the period from January 2016 to September 2017, while the SMOS experiment is referred to the time span from January 2011 to September 2017. The irrigation amounts have been retrieved through the SM2RAIN algorithm, in which the guidelines provided in the FAO (Food and Agriculture Organization) paper n.56 about the crop evapotranspiration have been implemented for a proper modeling of the crop evapotranspiration. A more detailed analysis has been performed in the context of the SMAP experiment. In fact, the spatial distribution and the temporal occurrence of the irrigation events have been investigated. Furthermore, the loss of accuracy of the irrigation estimates when using different sources for the evapotranspiration data has been assessed. In order to do this, the SMAP experiment has been repeated by forcing the SM2RAIN algorithm with several evapotranspiration data sets, both calculated and observed. Finally, the merging of the results obtained through the two experiments has produced a data set of almost 7 years of irrigation estimated from remote sensing soil moisture.</p>

The detection of irrigation through remote sensing soil moisture and a land surface model: a case study in Spain

2020 ◽  
Author(s):  
Jacopo Dari ◽  
Pere Quintana-Seguí ◽  
María José Escorihuela ◽  
Luca Brocca ◽  
Renato Morbidelli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Sets ◽  
Data Set ◽  
Agricultural Areas ◽  
Irrigation Practices

<p>Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.</p><p>The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (<em>Surface Externalisée - Interaction Sol Biosphère Atmosphère</em>) land surface model is used as a support for the performed analyses.</p><p>The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.</p>

scholarly journals Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region

2020 ◽  
Vol 12 (16) ◽  
pp. 2593
Author(s):  
Jacopo Dari ◽  
Luca Brocca ◽  
Pere Quintana-Seguí ◽  
María José Escorihuela ◽  
Vivien Stefan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Crop Evapotranspiration ◽  
Organization Model ◽  
Leading Role ◽  
Main Work ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Ocean Salinity ◽  
Correct Representation

Despite irrigation being one of the main sources of anthropogenic water consumption, detailed information about water amounts destined for this purpose are often lacking worldwide. In this study, a methodology which can be used to estimate irrigation amounts over a pilot area in Spain by exploiting remotely sensed soil moisture is proposed. Two high-resolution DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled soil moisture products have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) at 1 km. The irrigation estimates have been obtained through the SM2RAIN algorithm, in which the evapotranspiration term has been improved to adequately reproduce the crop evapotranspiration over irrigated areas according to the FAO (Food and Agriculture Organization) model. The experiment exploiting the SMAP data at 1 km represents the main work analyzed in this study and covered the period from January 2016 to September 2017. The experiment with the SMOS data at 1 km, for which a longer time series is available, allowed the irrigation estimates to be extended back to 2011. For both of the experiments carried out, the proposed method performed well in reproducing the magnitudes of the irrigation amounts that actually occurred in four of the five pilot irrigation districts. The SMAP experiment, for which a more detailed analysis was performed, also provided satisfactory results in representing the spatial distribution and the timing of the irrigation events. In addition, the investigation into which term of the SM2RAIN algorithm plays the leading role in determining the amount of water entering into the soil highlights the importance of correct representation of the evapotranspiration process.

scholarly journals Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

2021 ◽  
Vol 11 (13) ◽  
pp. 5911
Author(s):  
Vanesa Martos ◽  
Ali Ahmad ◽  
Pedro Cartujo ◽  
Javier Ordoñez
Keyword(s):  
Remote Sensing ◽  
Sustainable Agriculture ◽  
Information Acquisition ◽  
Industrial Revolution ◽  
Structural Parameters ◽  
Special Focus ◽  
World Population ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Zero Hunger

Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

scholarly journals Sources of light-absorbing aerosol in arctic snow and their seasonal variation

2010 ◽  
Vol 10 (6) ◽  
pp. 13755-13796 ◽  
Author(s):  
D. A. Hegg ◽  
S. G. Warren ◽  
T. C. Grenfell ◽  
S. J. Doherty ◽  
A. D. Clarke
Keyword(s):  
Seasonal Variation ◽  
Biomass Burning ◽  
Chemical Constituents ◽  
Dominant Role ◽  
Data Sets ◽  
Source Attribution ◽  
Data Set ◽  
Pmf Model ◽  
The North ◽  
Absorbing Aerosols

Abstract. Two data sets consisting of measurements of light absorbing aerosols (LAA) in arctic snow together with suites of other corresponding chemical constituents are presented; the first from Siberia, Greenland and near the North Pole obtained in 2008, and the second from the Canadian arctic obtained in 2009. A preliminary differentiation of the LAA into black carbon (BC) and non-BC LAA is done. Source attribution of the light absorbing aerosols was done using a positive matrix factorization (PMF) model. Four sources were found for each data set (crop and grass burning, boreal biomass burning, pollution and marine). For both data sets, the crops and grass biomass burning was the main source of both LAA species, suggesting the non-BC LAA was brown carbon. Depth profiles at most of the sites allowed assessment of the seasonal variation in the source strengths. The biomass burning sources dominated in the spring but pollution played a more significant (though rarely dominant) role in the fall, winter and, for Greenland, summer. The PMF analysis is consistent with trajectory analysis and satellite fire maps.

scholarly journals An Overview of Evapotranspiration Guide for Irrigated Crops in Turkey

2019 ◽  
Vol 7 (2) ◽  
pp. 266
Author(s):  
Melek Yiğen ◽  
Murat Tekiner
Keyword(s):  
Agricultural Research ◽  
Action Plan ◽  
Electronic Media ◽  
Crop Evapotranspiration ◽  
Irrigation Water Requirement ◽  
Climatic Regions ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Reference Plant ◽  
Irrigated Crops

The “Action Plan for the Water Use in Agriculture Activation Program” in the Tenth Five-Year Development Plan, prepared by The Ministry of Development for the period 2014-2018, is one of the most important indicators of the increasing importance of water resources and irrigation. It’s published according to Blaney-Criddle method with the data of the Crop Evapotranspiration Guideline in 1982, for a guide to be updated by General Directorate of Agricultural Research And Policies (TAGEM) and General Directorate of State Hydraulic Works (DSI) in 2013, using the Food and Agriculture Organization (FAO) Penman Monteith method. While DSİ undertakes the calculation of reference plant water consumption (ET0), effective precipitation and irrigation water requirement of this update, TAGEM is responsible for the compilation of information obtained from irrigation researches, calculation of plant coefficient (kc) values, publication of guides in written and electronic media realized. In this study, five guides (Balıkesir, Çanakkale, Edirne, Kocaeli and Tekirdağ) in three different climatic regions (Akdeniz Ardı, Marmara and İç Geçit-Marmara) were used in this guide, which the two institutions jointly organize and update with intensive effort, TUIK 2012-2016 considering the data, the county made on the basis of agriculture and untreated plants, guide place whether to get cases examined, the importance and aspects to be developed contacts have tried to reveal.

Agricultural Growth Accounting and Total Factor Productivity in Jordan

2020 ◽  
pp. 1709-1723
Author(s):  
Samia Nadeem Akroush ◽  
Boubaker Dhehibi ◽  
Aden Aw-Hassan
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Agricultural Research ◽  
Agricultural Productivity ◽  
Primary Source ◽  
Factor Productivity ◽  
Factors Affecting ◽  
Tfp Growth ◽  
Food And Agriculture ◽  
Food And Agriculture Organization

This article develops new estimates of historical agricultural productivity growth in Jordan. It investigates how public policies such as agricultural research, investment in irrigation capital, and water pricing have contributed to agricultural productivity growth. The Food and Agriculture Organization (FAO) annual time series from 1961 to 2011 of all crops and livestock productions are the primary source for agricultural outputs and inputs used to construct the Törnqvist Index for the case of Jordan. The log-linear form of regression equation was used to examine the relationship between Total Factor Productivity (TFP) growth and different factors affecting TFP growth. The results showed that human capital has positive and direct significant impact on TFP implying that people with longer life expectancy has a significant impact on TFP growth. This article concludes that despite some recent improvement, agricultural productivity growth in Jordan continues to lag behind just about every other region of the world.

Data Sets

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Model Building ◽  
Careful Analysis ◽  
Logical Structure ◽  
Fortran Program ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Wind Speeds ◽  
The North ◽  
The One

Hurricane data originate from careful analysis of past storms by operational meteorologists. The data include estimates of the hurricane position and intensity at 6-hourly intervals. Information related to landfall time, local wind speeds, damages, and deaths, as well as cyclone size, are included. The data are archived by season. Some effort is needed to make the data useful for hurricane climate studies. In this chapter, we describe the data sets used throughout this book. We show you a work flow that includes importing, interpolating, smoothing, and adding attributes. We also show you how to create subsets of the data. Code in this chapter is more complicated and it can take longer to run. You can skip this material on first reading and continue with model building in Chapter 7. You can return here when you have an updated version of the data that includes the most recent years. Most statistical models in this book use the best-track data. Here we describe these data and provide original source material. We also explain how to smooth and interpolate them. Interpolations are needed for regional hurricane analyses. The best-track data set contains the 6-hourly center locations and intensities of all known tropical cyclones across the North Atlantic basin, including the Gulf of Mexico and Caribbean Sea. The data set is called HURDAT for HURricane DATa. It is maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA) at the National Hurricane Center (NHC). Center locations are given in geographic coordinates (in tenths of degrees) and the intensities, representing the one-minute near-surface (∼10 m) wind speeds, are given in knots (1 kt = .5144 m s−1) and the minimum central pressures are given in millibars (1 mb = 1 hPa). The data are provided in 6-hourly intervals starting at 00 UTC (Universal Time Coordinate). The version of HURDAT file used here contains cyclones over the period 1851 through 2010 inclusive. Information on the history and origin of these data is found in Jarvinen et al (1984). The file has a logical structure that makes it easy to read with a FORTRAN program. Each cyclone contains a header record, a series of data records, and a trailer record.

Marlim R3D: A realistic model for controlled-source electromagnetic simulations — Phase 2: The controlled-source electromagnetic data set

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. E293-E299
Author(s):  
Jorlivan L. Correa ◽  
Paulo T. L. Menezes
Keyword(s):  
A Priori ◽  
Synthetic Data ◽  
Realistic Model ◽  
Earth Model ◽  
Data Sets ◽  
Data Set ◽  
Geoelectric Model ◽  
Controlled Source ◽  
The North ◽  
Electromagnetic Simulations

Synthetic data provided by geoelectric earth models are a powerful tool to evaluate a priori a controlled-source electromagnetic (CSEM) workflow effectiveness. Marlim R3D (MR3D) is an open-source complex and realistic geoelectric model for CSEM simulations of the postsalt turbiditic reservoirs at the Brazilian offshore margin. We have developed a 3D CSEM finite-difference time-domain forward study to generate the full-azimuth CSEM data set for the MR3D earth model. To that end, we fabricated a full-azimuth survey with 45 towlines striking the north–south and east–west directions over a total of 500 receivers evenly spaced at 1 km intervals along the rugged seafloor of the MR3D model. To correctly represent the thin, disconnected, and complex geometries of the studied reservoirs, we have built a finely discretized mesh of [Formula: see text] cells leading to a large mesh with a total of approximately 90 million cells. We computed the six electromagnetic field components (Ex, Ey, Ez, Hx, Hy, and Hz) at six frequencies in the range of 0.125–1.25 Hz. In our efforts to mimic noise in real CSEM data, we summed to the data a multiplicative noise with a 1% standard deviation. Both CSEM data sets (noise free and noise added), with inline and broadside geometries, are distributed for research or commercial use, under the Creative Common License, at the Zenodo platform.

scholarly journals Precipitation Trends over the Indus Basin

Climate ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 116 ◽  
Author(s):  
Nir Y. Krakauer ◽  
Tarendra Lakhankar ◽  
Ghulam H. Dars
Keyword(s):  
Remote Sensing ◽  
Large Population ◽  
Tropical Rainfall Measuring Mission ◽  
Indus Basin ◽  
Data Sets ◽  
Precipitation Trend ◽  
Precipitation Data ◽  
Data Set ◽  
Station Data ◽  
Precipitation Trends

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

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