The significant climatic variability present in Latin America and the Caribbean (LAC) generate marked regional differences in needs, availability, and management of water resources for agriculture. The availability of large-scale technological tools for monitoring crops and environmental conditions within their surroundings, becomes imperative in scenarios with a lack of water availability. To address this challenge, irrigation specialists from INIA-Chile, INTA-Argentina, Agrosavia-Colombia, INIA-Uruguay, Irrigation-Mendoza, and the Institute of Regional Development of UCLM-Spain, with co-financial support from FONTAGRO, are developing an initiative called "New technologies for increasing water use efficiency in LAC agriculture by 2030". The main objective of the initiative is the modernization of technological tools for efficient management of water resource in agriculture. Our conceptual framework for analyzing crop water consumption is based in the standardized methodology FAO-Manual N°56 with the improvement of use satellite vegetation index information. Based on the availability of a time series of satellite imagery (NDVI index) as a technological basis, we provide detailed analysis and advice for irrigation management at plot scale, as well as at regional areas where a soil water-balance model is implemented to estimate regional water consumption. At both working scales, the validation of this conceptual-technological framework has been carried out using technological pilots. Our results confirm the operability of the proposed framework in both scales under various agricultural contexts. Therefore, the effectiveness of technologies for crop monitoring, for precise estimation of water consumption, and for the ability to improve water use efficiency has been validated.
Crop phenology is a key parameter for precision farming and necessary in crop models for improving water and nutrients management in space and time. It has been traditionally determined at field, with observations of biophysical parameters in correspondence with different time-scales, but depending on plot size, it is not full representative of the variability inside it and of the variation between plots. Remote sensing techniques may increase the accessibility of high frequency spatialized data that in combination with meteorological information provide a tool for monitoring crops. Particularly, an important variety of sensors suited for agricultural applications on board of unmanned aerial systems, including spectral bands coherent with satellites and field radiometry, are being applied to describe within plot variability. With this purpose, an experiment along the year 2017 has been designed, to study the behavior of more than forty varieties of barley and wheat in an intensive experiment composed of 576 micro-plots of 1.4 × 10 m size. They have been monitored at field, registering the phenology in the BBCH scale and a complete set of soil and plant biophysical parameters in coincidence to sixteen multispectral very high-resolution images, using the number of days after sowing (NADS), the accumulated growing degree days (AGDD) and accumulated reference evapotranspiration (AcETo) as temporal scales, for studying the spatio-temporal distribution response of crop. The images were supplied with a parrot sequoia® multispectral camera, at a ground sample distance of 10 cm which allow to determine the variability into the micro-plots and obtaining representative results between them. The meteorological parameters were registered in a weather station close to the experimental area. The results show that the vegetation index in combination to the growing degree days scale or the accumulate reference evapotranspiration can set the emerging, flowering and maturity stages that are crucial inputs for management and crop models. AGDD and AcETo show a better-defined plateau between flag leave and early maturity. The ratio between the TNDVI along the reproductive phase (from BBCH = 55 to 89) and the growing cycle for barley show values of 0.31 ± 0.05 in NADS, 0.45 ± 0.03 in AGDD and 0.48 ± 0.03 in AcETo. For durum wheat the 0.32 ± 0.05 (NADS), 0.46 ± 0.03 (AGDD) and 0.49 ± 0.05 (AcETo). In case of bread wheat, the values are 0.27 ± 0.03 (NADS), 0.53 ± 0.06 (AGDD) and 0.54 ± 0.05 (AcETo). These results show that proximal remote sensing is very useful in intensive experiments as prospective techniques to explore new crop varieties that could be implanted in the experimental area and setting up the tools for satellite applications.
Monitoring Land Surface Temperature (LST) from satellite remote sensing plays a key role in climatic, environmental, hydrological and agricultural applications. A Single Band Atmospheric Correction (SBAC) tool was recently introduced and tested with Landsat 7/ETM+. SBAC provides pixel-by-pixel atmospheric correction parameters regardless of the pixel size using atmospheric profiles from National Centers of Environmental Prediction (NCEP) reanalysis products as inputs, accounting also for the pixel elevation through a Digital Elevation Model (DEM). This work deals now with the assessment of SBAC applied to Landsat 8/TIRS data since no operational LST product is still available. A new experiment was conducted in summer 2018, covering a variety of crops and surface conditions in the Barrax test site, Spain (39º 03’ N, 2º 06’ W) concurrent to L8/TIRS overpasses. Ground temperatures were measured using a set of hand-held infrared radiometers (IRTs) Apogee MI-210. Results show differences within ±3.5 K for all cases. Average results for SBAC show small bias (- 0.8K) and standard deviation (±1.3 K), yielding a RMSE of ±1.5 K. Finally, a comparison is established with results obtained using the NASA Atmospheric Correction Parameter Calculator tool (ACP) applied to the center of ”Las Tiesas” site coordinates. A similar standard deviation (±1.4 K) was obtained, with a larger bias, close to -1.5 K in this case, and a resulting RMSE of ±2.0 K. These results reinforce the potential of SBAC for the operational pixel-by-pixel atmospheric correction of full Landsat 8/TIRS images.
The traditional limitation in the lower spatial resolution of Thermal Infrared (TIR) versus Visible Near Infrared (VNIR) satellite data can be faced by applying recent disaggregation techniques. These techniques are based on the VNIR-TIR variable regressions at coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. A comprehensive analysis of different disaggregation methods in the literature using MODIS and Landsat images was already addressed by [1] in a previous publication. The aim of this work is to evaluate the performance of the downscaling method that showed the best results, when applied now to the MODIS/Sentinel-2 tandem for the estimation of daily land surface temperature (LST) at 10 m spatial resolution. An experiment was carried out in an agricultural area located in the Barrax test site, Spain (39º 03’ 35’’ N, 2º 06’ W), for the summer of 2018. Ground measurements of LST transects centered in the MODIS overpasses, and covering a variety of crops and surface conditions, were used for a robust local validation of the disaggregation approach. An additional set of Landsat-7/ETM+ images were also used for a more extended assessment of the LST product generated. Data from 6 different dates were available for this study, covering 10 different crop fields. Despite the large range of temperatures registered (300-325 K), differences within ±4.0K are obtained, with an average estimation error of ±2.2K and a systematic deviation of 0.6K for the full dataset. A similar error was obtained for the extended assessment of the high resolution LST products, based on the pixel-to-pixel comparison between Landsat and disaggregated Sentinel-2 LST products.
We propose the use of temporal series of remote-sensing images (RS) for the characterization of the dynamics of the crop canopy throughout the growing and development cycle. Crop phenology, meteorological data, and normalized difference vegetation index (NDVI) were obtained during the period 2008 to 2016 for commercial fields planted with wheat. Three temporal scales based on the number of days, the growing degree-days (GDD), and the reference evapotranspiration (ETo) were analyzed for the intercomparison of the growing cycles. The use of the accumulated value of ETo as the reference scale for the temporal evolution of NDVI allowed for a better analysis of the differences among the fields. This scale also improves the estimation of the duration of the cycles and the prediction of flowering and physiological maturity. The analysis of the accumulated NDVI indicated that flowering occurs during the middle of the growing cycle and that the accumulated NDVI in the vegetative and reproductive phases is similar if the growing cycle is analyzed in terms of ETo or GDD. In addition, the estimation of the green-up based on RS data allows for the definition of the beginning of the growing period for this crop even in the absence of planting dates data.
We present and evaluate an experimental relationship between the fraction of ground cover (FV) and multispectral vegetation indices (VI) derived from medium resolution images (Landsat 5-TM) in vertical shoot trellised vineyards. The results indicate a strong linear relationship between FV and the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), resulting in correlation coefficients greater than 0.90. These relationships were evaluated for the effect of variations in illumination angles and shadow enlargement using two analytical approaches: the Linear Spectral Mixture Analysis techniques and a radiative transfer approach with the Markov-chain canopy reflectance model, with additions to simulate the row structure. Previous to this analysis, both models were evaluated by comparing the model results with VIs in row vineyards obtained from satellite images, performing fairly well. The exploratory analysis demonstrated that the use of a single relationship based on the NDVI index could result in significant inaccuracies for larger zenith angles and row directions perpendicular to the sun azimuth at the satellite acquisition time. In contrast, the SAVI improved the linearity of the relationships and resulted in less sensitivity to changes in the sun angles and row directions.
A two-source energy balance model that separates surface fluxes of the soil and canopy was applied to a drip-irrigated
vineyard in central Spain, using a series of nine Landsat-5 images acquired during the summer of 2007. The model
partitions the available energy, using surface radiometric temperatures to constrain the sensible heat flux, and computing ET as a residual of the energy balance. Flux estimations from the model are compared with half-hourly and daily values obtained by an eddy covariance flux tower installed on the site during the experiment. The performance of the twosource model to estimate ET under the low vegetation cover and semiarid conditions of the experiment, with RMSD between observed and model data equal to 49 W m-2 for half-hourly estimations and RMSD=0.5 mm day-1 at daily scale, is regarded as acceptable for irrigation management purposes. Model results in the separation of the beneficial (transpiration) and non-beneficial (evaporation from the soil) fractions, which is key information for the quest to improve water productivity, are also reported. However, the lack of measures of these components makes it difficult to draw conclusions about the final use of the water.
In the context of a sustainable agriculture, a controlled and efficient irrigation management is required to avoid negative
effects of the increasing water scarcity, especially in arid and semi-arid regions.
Within this background, the project 'Participatory multi-Level EO-assisted tools for Irrigation water management and
Agricultural Decision-Support' (PLEIADeS: http://www.pleiades.es) addressed the efficient and sustainable use of water
for food production in water-scarce environments. Economical, environmental, technical, social and political dimensions
are considered by means of a synergy of leading-edge technologies and participatory approaches. Project partners,
represented by a set of nine pilot case studies, include a broad range of conditions characteristic for the European,
Southern Mediterranean and American regions.
PLEIADeS aimed at improving the performance of irrigation schemes by means of a range of measures, made possible
through wide space-time coverage of Earth observation (E.O.) data and interactive networking capabilities of
Information and Communication Technologies (ICT).
Algorithms for a number of basic products to estimate Irrigation Water Requirements (IWR) in an operational context
are defined. In this study, the pilot zone at the Nurra site in Sardinia, Italy, is chosen to test, validate and apply these
methodologies.
The project DEMETER (DEMonstration of Earth observation TEchnologies in Routine
irrigation advisory services) was designed to assess and demonstrate improvements introduced by
Earth observation (EO) and Information and Communication Technologies (ICT) in farm and
Irrigation Advisory Service (IAS) day-to-day operations. The DEMETER concept of near-real-time
delivery of EO-based irrigation scheduling information to IAS and farmers has proven to be valid. The
operationality of the space segment was demonstrated in three different pilot zones in South Europe
during the 2005 irrigation campaigns. Extra-fast image delivery and quality controlled operational
processing make the EO-based crop coefficient maps available at the same speed and quality as
ground-based data (point samples), while significantly extending the spatial coverage and reducing
service cost. The new online Space-Assisted Irrigation Advisory Service (e-SAIAS) is the central
outcome of the project. Its key feature is the operational generation of irrigation scheduling
information products from a virtual constellation of high-resolution EO satellites and their delivery to
farmers in near-real-time using leading-edge on-line analysis and visualization tools. First feedback of
users at IAS and farmer level is encouraging. The paper gives an overview of the project and its main
achievements.
KEYWORDS: Satellites, Information technology, Agriculture, Earth observing sensors, Spatial resolution, Near infrared, Remote sensing, Short wave infrared radiation, Landsat, Internet
Irrigation Advisory Services (IAS) are the natural management instruments to achieve a better efficiency in the use of water for irrigation. IAS help farmers to apply water according to the actual crop water requirements and thus, to optimize production and cost-effectiveness. The project DEMETER (DEMonstration of Earth observation TEchnologies in Routine irrigation advisory services) aims at assessing and demonstrating how the performance and cost-effectiveness of IAS is substantially improved by the incorporation of Earth observation (EO) techniques and Information Society Technology (IT) into their day-to-day operations. EO allows for efficiently monitoring crop water requirements of each field in extended areas. The incorporation of IT in the generation and distribution of information makes that information easily available to IAS and to its associated farmers (the end-users) in a personalized way. This paper describes the methodology and selected results.
Several satellite sensor systems useful in Earth observation and monitoring have recently been launched and their derived products are being used in regional and global vegetation studies. The joint use of these multi-resolution sensors offers many opportunities for vegetation studies. Spectral vegetation indices obtained from Landsat, Spot, IRS and other sensors are now widely available for monitoring ecosystem dynamics. However, the joint use of data from different satellites requires inter-satellite cross-calibration. We will use a multi-temporal data synthesising procedure for this purpose.
In this paper we analyze the broadband reflectance and NDVI relationships among the various relevant sensors. The key to the method is in using synchronous or nearsynchronous imagery from different sensors.
Comparison between reflectances for different bands shows that a linear function fits well to describe the relation between different sensors. Observations made from different sensors at different spatial scales can be reliably compared only if they are spatially aggregated to an adequate grid size. This minimum spatial aggregation size depends on the spatial resolution of the sensors involved in the comparison. In any case, it must be at least 3 x 3 pixels of the coarser resolution sensor.
A linear relationship between NDVI and basal crop coefficient (Kcb) allows to compute the spectral crop coefficient (Krcb). Due to the influence of soil variations varying surface humidity on NDVI, five soil optimized indices have been used to obtain a linear relationship normalized for soil background effect (SAVI, OSAVI, TSAVI, MSAVI and
GESAVI). Data used on this work have been obtained from a field campaign for corn in the area of Barrax (Spain), describing crop growth stages with green fraction cover (GFC), and leaf area index (LAI). SAVI with optimized factor L set to 0.5 is a good estimator of Krcb from sparse to dense vegetation, nevertheless the soil line based index ( GESAVI) due to a wider range of variation are more sensitive to leaf variations at high levels of vegetation amount. Spectral crop coefficients obtained from SAVI and soil line based GESAVI are sensitive to crop hazards by weather anomalies and
estimates in real time the basal crop coefficients to estimate the amount of water removed by the crop from the active root zone.
This work describes the validation of a distributed model for estimating direct recharge and evapotranspiration over arid and semiarid regions. This validation was performed for a lysimeter-site planted to festuca (grown under controlled irrigated treatment) and for two months, June and July 2003. The model, which can be classified as a distributed water balance model, puts its emphasis on two devising aspects. First, a detailed description of the effect of the land use on the water balance through processes of evaporation/transpiration and the evolution in time of the vegetated surfaces on the area. Second, the operational character of the model. The model was conceived to run integrated into a Geographical Information System and incorporates the pre-processing of the needed input parameters. This pre-processing comprises the use of remote sensing observations to monitor the plants status and their dynamics. In this study, agrometeorogical station records and information on irrigation scheduling, soil hydraulic properties and the festuca culture were used to run the model, whereas lysimeter measurements were used as validation data. Moreover, the performance of the model was checked for contrasting water conditions of the soil: completely wet and dried out.
More than 80% of the total water consumption in Mediterranean countries is used for agricultural practices. When this water comes from subterranean reservoirs (aquifers), the evaluation of extracted volumes is very complex using the traditional methods. The exploitation of subterranean waters in Span has become, in some cases, an environmental problem since it has affected highly valuable wetlands and river flows by depressing the water table levels. In this work, we introduce a methodology based on Remote Sensing and Geographic Information Systems (GIS) techniques that allows to evaluate the extracted water volumes for irrigation, with high accuracy levels and relatively low economic costs. The developed system is based on the elaboration of crop maps that are obtained from the analysis of multitemporal Landsat TM images. The used procedure is a supervised classification of images that combines maximum likelihood algorithms and decision-tree criterions. The resulting data are integrated in a GIS and crossed with the values of water volumes needed per crop. The obtained output allows quantifying not only the spatial distribution of the water consumption, but also its evolution through time.
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