Open Access
On the influence of acquisition geometry in backscatter time series over wheat
(Elsevier, 2022) Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Gobierno de Navarra / Nafarroako Gobernua
Dense time series of Sentinel-1 imagery are an invaluable information source for agricultural applications. Multiple orbits can observe a specific area and their combination could improve the temporal resolution of the time series. However, the orbits have different acquisition geometries regarding incidence and azimuth angles that need to be considered. Furthermore, crops are dynamic canopies and the influence of incidence and azimuth angles might change during the agricultural season due to different phenological stages. The main objective of this letter is to evaluate the influence of different acquisition geometries in Sentinel-1 backscatter time series over wheat canopies, and to propose a strategy for their correction. A large dataset of wheat parcels (∼40,000) was used and 344 Sentinel-1 images from three relative orbits were processed during two agricultural seasons. The first analysis was a monthly evaluation of the influence of incidence angle on backscatter (σ0) and terrain flattened backscatter (γ0). It showed that terrain flattening significantly reduced the backscatter dependence on incidence angle, being negligible in VH polarization but not completely in VV polarization. Incidence angle influence in VV backscatter changed in time due to wheat growth dynamics. To further reduce it, an incidence angle normalization technique followed by an azimuthal anisotropy correction were applied. In conclusion, γ0 enabled a reasonable combination of different relative orbits, that may be sufficient for many applications. However, for detailed analyses, the correction techniques might be implemented to further reduce orbit differences, especially in bare soil periods or winter months.
Open Access
New methodology for wheat attenuation correction at C-Band VV-polarized backscatter time series
(IEEE, 2022) Arias Cuenca, María; Campo-Bescós, Miguel; Arregui Odériz, Luis Miguel; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Agronomia, Bioteknologia eta Elikadura; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Agronomía, Biotecnología y Alimentación; Ingeniería
Wheat is one of the most important crops worldwide, and thus the use of remote sensing data for wheat monitoring has attracted much interest. Synthetic Aperture Radar (SAR) observations show that, at C-band and VV polarization, wheat canopy attenuates the surface scattering component from the underlying soil during a significant part of its growth cycle. This behavior needs to be accounted for or corrected before soil moisture retrieval is attempted. The objective of this paper is to develop a new method for wheat attenuation correction (WATCOR) applicable to Sentinel-1 VV time series and based solely on the information contained in the time series itself. The hypothesis of WATCOR is that without attenuation, VV backscatter would follow a stable long-term trend during the agricultural season, with short-term variations caused by soil moisture dynamics. The method relies on time series smoothing and changing point detection, and its implementation follows a series of simple steps. The performance of the method was compared by evaluating the correlation between backscatter and soil moisture content in six wheat fields with available soil moisture data. The Water Cloud Model (WCM) was also applied as a benchmark. The results showed that WATCOR successfully removed the attenuation in the time series, and achieved the highest correlation with soil moisture, improving markedly the correlation of the original backscatter. WATCOR can be easily implemented, as it does not require parameterization or any external data, only an approximate indication of the period where attenuation is likely to occur.
Open Access
Assessing soil properties controlling interrill erosion: an empirical approach under Mediterranean conditions
(Wiley, 2017) Ollobarren del Barrio, Paul; Giménez Díaz, Rafael; Campo-Bescós, Miguel; Landa Ingeniaritza eta Proiektuak; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Proyectos e Ingeniería Rural; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Soil erodibility is a complex phenomenon that comprises a number of different soil properties. However, most current (empirical) erodibility indices are based on only a few soil properties. A feasible soil characterization of interrill erosion (IE) prediction at large scale should be based on simple, quick and inexpensive tests to perform. The objective of this work was to identify and assess those soil properties that best reflect soil vulnerability to IE. Twenty‐three agricultural soil samples located in Spain and Italy were studied. Forty‐nine different physical and chemical soil properties that presumably underpin IE were defined. Experiments were carried out in the field (in microplots using simulated rainfall) and in the lab. The most relevant variables were detected using multivariate analysis. Six key variables were finally identified: RUSLE K factor, a granulometric/organic matter content index, exchangeable sodium percentage, shear strength, penetration resistance and permeability of soil seal. The latter is proposed as a useful technique to evaluate soil susceptibility to crusting even when the crust is not present at the time of the field survey. The selected variables represented a wide range of soil properties, and they could also be successfully applied to different soils with different characteristics than those evaluated in our experiments.
Open Access
Toward optimal irrigation management at the plot level: evaluation of commercial water potential sensors
(MDPI, 2023) Campo-Bescós, Miguel; Virto Quecedo, Íñigo; Giménez Díaz, Rafael; Aldaz Lusarreta, Alaitz; Ciencias; Zientziak; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Proper irrigation practice consists of applying the optimum amount of water to the soil at the right time. The porous characteristics of the soil determine the capacity of the soil to absorb, infiltrate, and store water. In irrigation, it is not sufficient to only determine the water content of the soil; it is also necessary to determine the availability of water for plants: water potential. In this paper, a comprehensive laboratory evaluation—accuracy and variability—of the world’s leading commercial water potential sensors is carried out. No such comprehensive and exhaustive comparative evaluation of these devices has been carried out to date. Ten pairs of representative commercial sensors from four different families were selected according to their principle of operation (tensiometers, capacitive sensors, heat dissipation sensors, and resistance blocks). The accuracy of the readings (0 kPa–200 kPa) was determined in two soils of contrasting textures. The variability in the recordings—repeatability and reproducibility—was carried out in a homogeneous and inert material (sand) in the same suction range. The response in terms of accuracy and value dispersion of the different sensor families was different according to the suction range considered. In the suction range of agronomic interest (0–100 kPa), the heat dissipation sensor and the capacitive sensors were the most accurate. In both families, registrations could be extended up to 150–200 kPa. The scatter in the readings across the different sensors was due to approximately 80% of the repeatability or intrinsic variability in the sensor unit and 20% of the reproducibility. Some sensors would significantly improve their performance with ad hoc calibrations.
Open Access
Evaluation of 2D models for the prediction of surface depression storage using realistic reference values
(Wiley, 2016) Giménez Díaz, Rafael; Mezkiritz Barberena, Irantzu; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Martínez de Aguirre Escobar, Alejandro; Casalí Sarasíbar, Javier; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Depression storage (DS) is the maximum storage of precipitation and runoff in the soil surface at a given slope. The DS is determined by soil roughness that in agricultural soils is largely affected by tillage. The direct measurement of DS is not straightforward because of the natural permeability of the soil. Therefore, DS has generally been estimated from 2D/3D empirical relationships and numerical algorithms based on roughness indexes and height measurements of the soil surface, respectively. The objective of this work was to evaluate the performance of some 2D models for DS, using direct and reliable measurements of DS in an agricultural soil as reference values. The study was carried out in experimental microplots where DS was measured in six situations resulting from the combination of three types of tillage carried out parallel and perpendicular to the main slope. Those data were used as reference to evaluate four empirical models and a numerical method. Longitudinal altitudinal profiles of the relief were obtained by a laser profilometer. Infiltration measurements were carried out before and after tillage. The DS was largely affected by tillage and its direction. Highest values of DS are found on rougher surfaces mainly when macroforms cut off the dominant slope. The empirical models had a limited performance while the numerical method was the most effective, even so, with an important variability. In addition, a correct hydrological management should take into account that each type of soil tillage affects infiltration rate differently.
Open Access
Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain
(MDPI, 2020) Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; Ingeniería; Ingeniaritza
Crop classification provides relevant information for crop management, food security assurance and agricultural policy design. The availability of Sentinel-1 image time series, with a very short revisit time and high spatial resolution, has great potential for crop classification in regions with pervasive cloud cover. Dense image time series enable the implementation of supervised crop classification schemes based on the comparison of the time series of the element to classify with the temporal signatures of the considered crops. The main objective of this study is to investigate the performance of a supervised crop classification approach based on crop temporal signatures obtained from Sentinel-1 time series in a challenging case study with a large number of crops and a high heterogeneity in terms of agro-climatic conditions and field sizes. The case study considered a large dataset on the Spanish province of Navarre in the framework of the verification of Common Agricultural Policy (CAP) subsidies. Navarre presents a large agro-climatic diversity with persistent cloud cover areas, and therefore, the technique was implemented both at the provincial and regional scale. In total, 14 crop classes were considered, including different winter crops, summer crops, permanent crops and fallow. Classification results varied depending on the set of input features considered, obtaining Overall Accuracies higher than 70% when the three (VH, VV and VH/VV) channels were used as the input. Crops exhibiting singularities in their temporal signatures were more easily identified, with barley, rice, corn and wheat achieving F1-scores above 75%. The size of fields severely affected classification performance, with ~14% better classification performance for larger fields (>1 ha) in comparison to smaller fields (<0.5 ha). Results improved when agro-climatic diversity was taken into account through regional stratification. It was observed that regions with a higher diversity of crop types, management techniques and a larger proportion of fallow fields obtained lower accuracies. The approach is simple and can be easily implemented operationally to aid CAP inspection procedures or for other purposes. © 2020 by the authors.
Open Access
Highway paving in the southwestern Amazon alters long-term trends and drivers of regional vegetation dynamics
(Elsevier, 2018) Klarenberg, Geraldine; Muñoz Carpena, Rafael; Campo-Bescós, Miguel; Perz, Steve G.; Ingeniería; Ingeniaritza
Infrastructure development, specifically road paving, contributes socio-economic benefits to society worldwide. However, detrimental environmental effects of road paving have been documented, most notably increased deforestation. Beyond deforestation, we hypothesize that road paving introduces “unseen” regional scale effects on forests, due to changes to vegetation dynamics. To test this hypothesis, we focus on the tri-national frontier in the southwestern Amazon that has been subject to construction of the Inter-Oceanic Highway (IOH) between 1987 and 2010. We use a long-term remotely sensed vegetation index as a proxy for vegetation dynamics and combine these with field-based socio-ecological data and biophysical data from global datasets. We find 4 areas of shared vegetation dynamics associated with increasing extent of road paving. Applying Dynamic Factor Analysis, an exploratory dimension-reduction time series analysis technique, we identify common trends and covariates in each area. Common trends, indicating underlying unexplained effects, become relatively less important as paving increases, and covariates increase in importance. The common trends are dominated by lower frequency signals possibly embodying long-term climate variability. Human-related covariates become more important in explaining vegetation dynamics as road paving extent increases, particularly family density and travel time to market. Natural covariates such as minimum temperature and soil moisture become less important. The change in vegetation dynamics identified in this study indicates a possible change in ecosystem services along the disturbance gradient. While this study does not include all potential factors controlling dynamics and disturbance of vegetation in the region, it offers important insights for management and mitigation of effects of road paving projects. Infrastructure planning initiatives should make provisions for more detailed vegetation monitoring after road completion, with a broader focus than just deforestation. The study highlights the need to mitigate population-driven pressures on vegetation like family density and access to new markets.
Open Access
Dissolved solids and suspended sediment dynamics from five small agricultural watersheds in Navarre, Spain: a 10-year study
(Elsevier, 2019) Merchán Elena, Daniel; Luquin Oroz, Eduardo Adrián; Hernández García, Iker; Campo-Bescós, Miguel; Giménez Díaz, Rafael; Casalí Sarasíbar, Javier; Valle de Lersundi, Jokin del; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Dissolved solids (DS) and suspended sediment (SS) loads are considered relevant environmental problems. They are related to a wide range of on-site and off-site impacts, such as soil erosion or salinization of water bodies. In this study, the dynamics of DS and SS concentrations and loads were assessed in five small watersheds covering representative agricultural land uses in Navarre (Spain). To this end, discharge, DS and SS concentration data were collected during ten hydrological years at each watershed outlet, and loads were computed from discharge and concentration values. DS concentration followed a seasonal pattern imposed by the availability of water, with higher concentrations recorded in low-flow periods and lower concentration in the high-flow period. SS concentration was extremely variable, with a range of 2–4 orders of magnitude in concentration for any specific discharge. Temporal variations (both intra- and inter-annual) in DS loads were explained by differences in runoff, whereas those of SS were not, being the SS loads associated mainly with specific high flow events. These temporal patterns were observed for both agricultural (this study) and non-agricultural (literature) watersheds. From the data in the Navarrese watersheds and those available in the literature, we inferred that agricultural land use, in general, tends to increase the concentration of both DS and SS. Regarding DS and SS yields, the effects of agricultural land use on DS yields are controlled by the changes in runoff rather than the (small) changes in DS concentration. In this sense, land uses changes expected to increase runoff (i.e., a shift from forested to arable or from rainfed to irrigated agriculture) would increase DS yields. On the other hand, agricultural land use tends to increase SS yields, although the effect is highly variable depending on site-specific factors, both natural (e.g., watershed shape) and anthropogenic (e.g., degree of soil conservation practices). In the Navarrese watersheds, DS yields ranged from 1.1 to 2.2 Mg ha−1 year−1 whereas SS yields ranged from 0.3 to 4.3 Mg ha−1 year−1. DS yields seem to dominate under non-agricultural conditions and in most agricultural land uses at the small watershed scale. On the other hand, SS yields dominate in watersheds with increased soil erosion as a consequence of arable land use over erosion-prone watersheds.
Open Access
Extended assessment of sprinkler irrigation uniformity in greenhouses using GIS and hydraulic modeling
(MDPI, 2022) Barberena Ruiz, Íñigo; Campo-Bescós, Miguel; Casalí Sarasíbar, Javier; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Traditionally, distribution uniformity has been obtained by using rain gauges, which makes it a very expensive process. This paper sought to create a simulation strategy using QGIS and EPANET, both free software, that allowed the simulation of the water application results of all the emitters of an irrigation installation. In this way, it was possible to obtain the geospatial representation of the applied water and finally to know the distribution uniformity in the whole installation. The simulation finally fulfilled its objective and was compared with a study of distribution uniformity with rain gauges. The biggest difference between the measured and simulated data was a difference of 5.76% among the sectors. The simulated uniformity was very similar to the measured uniformity, which allowed us to affirm that the proposed simulation methodology was adequate. We believe that the methodology proposed in this article could be very useful in improving the management of sprinkler irrigation systems, particularly those in which distribution uniformity is of special importance. These improvements in management can also result in savings in water and other inputs, which are becoming increasingly important in the current context of climate change and the reduction in the impact of agriculture on the environment. Finally, similar studies could be carried out with the same tools for other pressurized irrigation systems, such as sprinkler irrigation outside greenhouses and drip irrigation.
Open Access
Demonstrating correspondence between decision-support models and dynamics of real-world environmental systems
(Elsevier, 2016) Huffaker, Ray; Muñoz Carpena, Rafael; Campo-Bescós, Miguel; Southworth, Jane; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
There are increasing calls to audit decision-support models used for environmental policy to ensure that they correspond with the reality facing policy makers. Modelers can establish correspondence by providing empirical evidence of real-world behavior that their models skillfully simulate. Since real-world behavior—especially in environmental systems—is often complex, credibly modeling underlying dynamics is essential. We present a pre-modeling diagnostic framework based on Nonlinear Time Series (NLTS) methods for reconstructing real-world environmental dynamics from observed data. The framework is illustrated with a case study of saltwater intrusion into coastal wetlands in Everglades National Park, Florida, USA. We propose that environmental modelers test for systematic dynamic behavior in observed data before resorting to conventional stochastic exploratory approaches unable to detect this valuable information. Reconstructed data dynamics can be used, along with other expert information, as a rigorous benchmark to guide specification and testing of environmental decision-support models corresponding with real-world behavior.