Impact on wheat production of anthropic soil erosion by recent gully filling at the Campiña landscape in Southern Spain

2020 ◽  
Author(s):  
Carlos Castillo ◽  
Rafael Pérez ◽  
Miguel Vallejo Orti
Keyword(s):  
Crop Yield ◽  
Vegetation Index ◽  
Southern Spain ◽  
Solar Irradiation ◽  
Slope Aspect ◽  
Topographic Index ◽  
Wheat Production ◽  
Explanatory Factors ◽  
Vegetation Indexes ◽  
Sentinel 2

<p>            Gully erosion is one of the main drivers of environmental degradation on intensively managed agricultural fields in Southern Spain. Ephemeral and permanent gullies develop after intense rainfall events, which leads to significant loss of arable land. In the study area, productivity is also affected atn gully surroundings since gully filling (by using the top soil scraped from the vicinity of the gully) is a common practice among local farmers.</p><p>            The aim of this communication is to analyze the impact of gully filling practices on wheat production during two growing years (2017 and 2019) in a medium-sized catchment (94 ha) at the Galapagares watershed. The study area is close to the city of Córdoba (Spain) and belongs to the Campiña landscape (rolling landscape on vertic soils). The catchment under study is divided in five subcatchments, two of them not affected by gully filling in the last eight years while in the other three, the soil was scraped and displaced into the gully within the study period (last two years).</p><p>            Firstly, a series of topographic and spatial factors (insolation, topographic index, slope, aspect, drainage area, distance to the gully) and a soil-related variable calculated prior to the growing season (soil color from the Sentinel-2 visible band) were selected as posible explanatory factors for remote sensing-based Vegetation Indexes (VI) derived from Sentinel-2 (the Normalized Difference Vegetation Index - NDVI and Enhanced Vegetation Index - EVI). Both indexes were considered potential proxies for crop yield for 2017 and 2019 campaigns. Furthermore, the differences in VI were compared between potentially affected areas by soil scraping close to gullies and non-affected areas. At last, a field survey on crop production (kg of wheat grain per ha, 15 % moisture) was carried out during the harvest period to determine the relation between vegetation indexes and crop yield.</p><p>            Results show that the most relevant explanatory factors for NDVI and EVI variance were solar irradiation, topographic index, aspect (positively correlated), soil colour (inverse correlation) and distance to the gully (positive correlation), in this order of importance. A general linear model explained 40% of NDVI and 55% of the EVI variances Nevertheless, when gully adjacent (<30m to the gully) and non adjacent (>30m) areas were analyzed separately, significant diferences were detected. Non-adjacent areas presented higher VI values and homogeinity pixelwise. Moreover, the distance to the gully became the second most significant explanatory factor for VI in adjacent areas (with higher VI values for more distant locations), whereas it remained non significant for non-adjacent pixels. In addition, those subcatchments impacted by recent gully filling showed larger variability in VI values before and after the operations as compared to non-affected subcatchments.</p>

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

scholarly journals Vegetation indexes and delineation of management zones for soybean1

2017 ◽  
Vol 47 (2) ◽  
pp. 168-177 ◽  
Author(s):  
Amanda Carolina Marx Bacellar Kuiawski ◽  
José Lucas Safanelli ◽  
Eduardo Leonel Bottega ◽  
Antonio Mendes de Oliveira Neto ◽  
Naiara Guerra
Keyword(s):  
Crop Yield ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Growth Stages ◽  
Soybean Yield ◽  
Management Zones ◽  
Soil Attributes ◽  
Vegetation Indexes ◽  
Simple Ratio ◽  
Yield Map

ABSTRACT The delimitation of site-specific management zones may be an operational and economically feasible approach in precision agriculture. This study aimed at investigating the spatial correlations between spectral indexes sampled during different growth stages of soybean and crop yield. Soil attributes stratified in each zone and the influence of altitude were also assessed. The simple ratio index, normalized difference vegetation index and soil-adjusted vegetation index were calculated for soybean at the V6, R5 and R5.5 stages. Spatial dependence analysis via semivariogram was performed for the vegetation indexes, soybean yield and terrain elevation. The crop yield map was taken as a reference to assess the spatial agreement with the different maps generated from the spectral indexes. The average values for chemical and granulometric soil attributes were calculated and analyzed by their means among the zones delineated. The field division into two management zones, due to the combination of altitude, simple ratio index of the V6 stage and soil-adjusted vegetation index of the R5.5 stage, showed the highest agreement with the soybean yield map. Differences between the delineated zones were identified for the phosphorus, clay and silt contents.

scholarly journals VEGETATION MAPPING OF SENTINEL-1 AND 2 SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND RANDOM FOREST WITH THE AID OF DUAL-POLARIZED AND OPTICAL VEGETATION INDEXES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 435-440
Author(s):  
N. Ghasemian Sorboni ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Vegetation Index ◽  
Near Infrared ◽  
False Positive Rate ◽  
Vegetation Mapping ◽  
Vegetation Indexes ◽  
Positive Rate ◽  
Dual Polarized ◽  
Sentinel 2

Abstract. Vegetation mapping is one of the most critical challenges of remote sensing society in forestry applications. Sentinel-1 dataset has the potential of vegetation mapping, but because of its limited number of polarizations, full polarized vegetation indexes are not accessible. The Sentinel-2 dataset is more suitable for vegetation mapping because a wide variety of vegetation indexes can be extracted from them. Handling this large number of vegetation indexes needs a robust feature extractor. Convolutional Neural Networks (CNN) extract relevant features through their deep feature layers structure and throw out disturbances from small to large scales. Hence, they can be far useful for classifying remote sensing data when the number of input bands is considerable. After pre-processing Sentinel-1 and 2 datasets and extracting the dual-polarized and optical vegetation indexes, we fed the sentinel-1 vegetation indexes alongside the VV and VH sigma Nought bands to a Random Forest (RF) and 1D CNN classifier. Also, 13 spectral features of the Sentinel-2 and the extracted indexes like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR) were imported to a RF and 1D CNN. The classification result of Sentinel-1 data showed that Dual Polarized Soil Vegetation Index (DPSVI) is a good indicator for discriminating vegetation pixels. Also, the experiment on the Sentinel-2 dataset using 1D CNN resulted in True Positive Rate (TPR) and False Positive Rate of 0.839 and 0.034, respectively.

scholarly journals Agricultural Production on Erosion-Affected Land from the Perspective of Remote Sensing

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2216
Author(s):  
Bořivoj Šarapatka ◽  
Marek Bednář
Keyword(s):  
Crop Yield ◽  
Vegetation Index ◽  
Degraded Soil ◽  
Chernozem Soils ◽  
Total Crop ◽  
South Moravia ◽  
Indirect Information ◽  
The Relationship ◽  
Orthophoto Images ◽  
Sentinel 2

In this article, we discuss the influence of soil erosion on crop yield in the erosion-prone chernozem region of South Moravia. Erosional and depositional areas show significant differences in soil properties, which are also reflected in total crop yield. Plots of winter wheat, grown during the years 2016–2019 were used for analysis. The Enhanced Vegetation Index (EVI), referred to in literature as one of the best correlates of yield, was used to provide indirect information on yield. Although erosional areas are visible on orthophoto images on chernozem soils, the necessary orthophoto images are not always available. Thus, we have proposed a method for the identification of such erosion-affected areas based on the use of Sentinel 2 satellite images and NDVI or NBR2 indices. The relationship between yield and erosion was expressed through Pearson’s correlation on a sample of pixels randomly selected on the studied plots. The results showed a statistically significant linear reduction in yield depending on the level of degradation. All plots were further reclassified, according to level of degradation, as high, medium, or low state of degradation, where the average EVI values were subsequently calculated. Yield on non-degraded soil is 16 ± 1% higher on average.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

Landslide Susceptibility Mapping in the Commune of Oudka, Taounate Province, North Morocco: A Comparative Analysis of Logistic Regression, Multivariate Adaptive Regression Spline, and Artificial Neural Network Models

2020 ◽  
Vol 26 (2) ◽  
pp. 185-200
Author(s):  
Said Benchelha ◽  
Hasnaa Chennaoui Aoudjehane ◽  
Mustapha Hakdaoui ◽  
Rachid El Hamdouni ◽  
Hamou Mansouri ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Multivariate Adaptive Regression Spline ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Neural Network Models ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Artificial Neural

ABSTRACT Landslide susceptibility indices were calculated and landslide susceptibility maps were generated for the Oudka, Morocco, study area using a geographic information system. The spatial database included current landslide location, topography, soil, hydrology, and lithology, and the eight factors related to landslides (elevation, slope, aspect, distance to streams, distance to roads, distance to faults, lithology, and Normalized Difference Vegetation Index [NDVI]) were calculated or extracted. Logistic regression (LR), multivariate adaptive regression spline (MARSpline), and Artificial Neural Networks (ANN) were the methods used in this study to generate landslide susceptibility indices. Before the calculation, the study area was randomly divided into two parts, the first for the establishment of the model and the second for its validation. The results of the landslide susceptibility analysis were verified using success and prediction rates. The MARSpline model gave a higher success rate (AUC (Area Under The Curve) = 0.963) and prediction rate (AUC = 0.951) than the LR model (AUC = 0.918 and AUC = 0.901) and the ANN model (AUC = 0.886 and AUC = 0.877). These results indicate that the MARSpline model is the best model for determining landslide susceptibility in the study area.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

scholarly journals Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 505
Author(s):  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Water Vapor ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Linear Regression Models ◽  
Area Index ◽  
Estimation Performance ◽  
Red Edge ◽  
Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

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