scholarly journals Coupling Remote Sensing Data and AquaCrop Model for Simulation of Winter Wheat Growth under Rainfed and Irrigated Conditions in a Mediterranean Environment

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2265
Author(s):  
Marie Therese Abi Saab ◽  
Razane El Alam ◽  
Ihab Jomaa ◽  
Sleiman Skaf ◽  
Salim Fahed ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Canopy Cover ◽  
Mediterranean Environment ◽  
Wheat Growth ◽  
Aquacrop Model ◽  
Rainfed Conditions ◽  
Sentinel 2

The coupling of remote sensing technology and crop growth models represents a promising approach to support crop yield prediction and irrigation management. In this study, five vegetation indices were derived from the Copernicus-Sentinel 2 satellite to investigate their performance monitoring winter wheat growth in a Mediterranean environment in Lebanon’s Bekaa Valley. Among those indices, the fraction of canopy cover was integrated into the AquaCrop model to simulate biomass and yield of wheat grown under rainfed conditions and fully irrigated regimes. The experiment was conducted during three consecutive growing seasons (from 2017 to 2019), characterized by different precipitation patterns. The AquaCrop model was calibrated and validated for different water regimes, and its performance was tested when coupled with remote sensing canopy cover. The results showed a good fit between measured canopy cover and Leaf Area Index (LAI) data and those derived from Sentinel 2 images. The R2 coefficient was 0.79 for canopy cover and 0.77 for LAI. Moreover, the regressions were fitted to relate biomass with Sentinel 2 vegetation indices. In descending order of R2, the indices were ranked: Fractional Vegetation Cover (FVC), LAI, the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI). Notably, FVC and LAI were highly correlated with biomass. The results of the AquaCrop calibration showed that the modeling efficiency values, NSE, were 0.99 for well-watered treatments and 0.95 for rainfed conditions, confirming the goodness of fit between measured and simulated values. The validation results confirmed that the simulated yield varied from 2.59 to 5.36 t ha−1, while the measured yield varied from 3.08 to 5.63 t ha−1 for full irrigation and rainfed treatments. After integrating the canopy cover into AquaCrop, the % of deviation of simulated and measured variables was reduced. The Root Mean Square Error (RMSE) for yield ranged between 0.08 and 0.69 t ha−1 before coupling and between 0.04 and 0.42 t ha−1 after integration. This result confirmed that the presented integration framework represents a promising method to improve the prediction of wheat crop growth in Mediterranean areas. Further studies are needed before being applied on a larger scale.

scholarly journals Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

2021 ◽  
Vol 13 (6) ◽  
pp. 1144
Author(s):  
Mahendra Bhandari ◽  
Shannon Baker ◽  
Jackie C. Rudd ◽  
Amir M. H. Ibrahim ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Grain Filling ◽  
Unmanned Aerial Systems ◽  
High Temporal Resolution ◽  
Wheat Growth ◽  
Spatial Domains ◽  
Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

scholarly journals Determination of Crop Health Monitoring in MPKV – Rahuri, Using Remote Sensing Approach

2021 ◽  
pp. 11-16
Author(s):  
Ankita P. Kamble ◽  
A. A. Atre ◽  
Payal A. Mahadule ◽  
C. B. Pande ◽  
N. S. Kute ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Monitoring ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Remotely Sensed Data ◽  
Pests And Diseases ◽  
Crop Health ◽  
Sentinel 2

Pests and diseases cause major harm during crop development. Also plant stress affects crop quality and quantity. Recent developments in high resolution remotely sensed data has seen a great potential in mapping cropland areas infected by pests and diseases, as well as potential vulnerable areas over expansive areas. Crop health monitoring in this study was carried out using remote sensing techniques. The present study was carried out in MPKV, Rahuri, Ahmednagar District, Maharashtra. Vegetation indices like Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to classify the crops into healthy and dead or unhealthy one. Sentinel-2 image data from October 2019 to January 2020 processed in Arc GIS 10.1 were used for this study. Vegetation is a key component of the ecosystem and plays an important role in stabilizing the global environment. The result showed that the average vegetation cover was decreased in the month of November and healthy vegetation was found more in month of October as compared to December and January. This shows that NDVI and SAVI indices for Sentinel-2 images can be used for crop health monitoring.

An attempt to understand corn and winter wheat temporal behavior by means of Sentinel-1 and Sentinel-2 data and its relation with phonological stages.

2021 ◽  
Author(s):  
Marta Pasternak ◽  
Kamila Pawluszek-Filipiak
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Strong Correlation ◽  
Vegetation Index ◽  
Radar Data ◽  
Temporal Behavior ◽  
Spectral Indices ◽  
Chlorophyll Index ◽  
Sar Data ◽  
Sentinel 2

&lt;p&gt;Crops are of the fundamental food sources for humanity. Due to the population growth as well as climate change, monitoring of the crops is important to sustain agriculture and conserve natural resources. Development of the remote sensing techniques especially in terms of revisiting time opens new avenues to study crops temporal behaviors from space. Moreover, thanks to the Copernicus program, which guarantees optical as well as radar data to be freely available, there are opportunities to utilize them in an operative way. Additionally, utilization of spectral as well as radar data allows for the synergetic application of both datasets. However, to utilize this data in the operational crop monitoring, it is very important to understand the temporal variations of the remote sensing signal. Therefore, we make an attempt to understand spectral as well as radar remote sensing temporal behavior and its relation with phonological stages.&lt;/p&gt;&lt;p&gt;For the analysis, 14 cloud-free Sentinel-2 (S-2) acquisitions as well as 34 Sentinel-1 (S-1) acquisitions are utilized. S-2 data were collected with 2A-level while S-1 data was captured in the format of Single Look Complex (SLC) in the Interferometric Wide (IW) swath mode. SLC products consist of complex SAR data preserving phase information which allows studying polarimetric indicators. All remote sensing (spectral as well as SAR) data cover the time period from 04/05/2020 to 07/11/2020. During this time, also 14 field visits were carried out to capture information about phonological stages of corn and wheat according to the BBCH scale (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie). Additionally, to better understand the temporal behavior of S-1/S-2 signal, weather information from the Institute of Meteorology and Water Management (IMGW) was captured.&lt;/p&gt;&lt;p&gt;Based on various spectral bands of S-2 data, 12 spectral indices were calculated e.g., GNDVI (Green Normalized Vegetation Index), IRECI (Inverted Red-Edge Chlorophyll Index), MCARI (Modified Chlorophyll Absorption in Reflectance Index), MSAVI (Modified Soil-Adjusted Vegetation Index), MTCI (MERIS Terrestrial Chlorophyll Index), NDVI (Normalized Difference Vegetation Index), PSSRa (Pigment Specific Simple Ratio) and others. After radiometric calibration and the Lee speckle filtering, backscattering coefficients (&amp;#963;&lt;sub&gt;VV&lt;/sub&gt;&lt;sup&gt;o&lt;/sup&gt; ,&amp;#963;&lt;sub&gt;VH&lt;/sub&gt;&lt;sup&gt;o&lt;/sup&gt;) of S-1 images were calculated as well as its backscattering ratio (&amp;#963;&lt;sub&gt;VH&lt;/sub&gt;&lt;sup&gt;o&lt;/sup&gt;/ &amp;#963;&lt;sub&gt;VV&lt;/sub&gt;&lt;sup&gt;o&lt;/sup&gt;).&amp;#160; All images were then converted from linear to decibel (dB). Additionally, 2 &amp;#215; 2 covariance matrix delivered from S-1 was extracted from the scattering matrix of each SLC image using PolSARpro version 6.0.2 software. After speckle filtration, total scattered power was derived which allows calculating the Shannon Entropy. This value measures the randomness of the scattering within a pixel.&lt;/p&gt;&lt;p&gt;Time series of many S-2 indices reveal the strong correlation between the development of phenology stages of corn and wheat and the increase of S2 delivered values of spectral indices. However, such a strong correlation cannot be observed within many of S-1 indices. Some of them very poorly indicate the correlation between the development of phenology stages of corn and wheat and increase of S-1 indices values. Additionally, it was observed that values of S1/S2 indices for the same phenology stage very between corn and winter wheat.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

2018 ◽  
Vol XLII-3 ◽  
pp. 1381-1387 ◽  
Author(s):  
M. Piragnolo ◽  
G. Lusiani ◽  
F. Pirotti
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Monitoring And Control ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

scholarly journals Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve

2018 ◽  
Vol 10 (9) ◽  
pp. 3308 ◽  
Author(s):  
Fabio Recanatesi ◽  
Chiara Giuliani ◽  
Maria Ripa
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Urban Forest ◽  
Forest Health ◽  
Canopy Cover ◽  
High Rate ◽  
Health Conditions ◽  
The Mediterranean ◽  
Sentinel 2

Climate change and human activities in particular are important causes of the possible variations in Mediterranean basin forest health conditions. Over the last decades, deciduous oak-forest mortality has been a recurrent problem in central and southern Italy. Despite the perception of increasingly visible damage in oak forests in drought sites, the role of various environmental factors in their decline is not completely clear. Among the modern methods of monitoring terrestrial ecosystems, remote sensing is of prime importance thanks to its ability to provide synoptic information on large areas with a high frequency of acquisition. This paper reports the preliminary results regarding a replicable and low cost monitoring tool planned to quantify forest health conditions based on the application of the Normalized Difference Vegetation Index (NDVI), using the diachronic images provided by the Sentinel-2 satellite. The study area is represented by a peri-urban forest of natural Mediterranean deciduous oaks, characterized by a high variability in the composition of the species and in the silvicultural structures. In order to monitor the health conditions of a specific forest canopy cover with remote sensing data, it is necessary to classify the forest canopy cover in advance to separate it from other species and from the Mediterranean scrub. This is due to the spatial distribution of vegetation and the high rate of biodiversity in the Mediterranean natural environment. To achieve this, Light Detection and Ranging (LiDAR) data, forest management data and field sampling data were analyzed. The main results of this research show a widespread decline in oak health conditions over the observed period (2015–2017). Specifically, for the studied area, thanks to the specific localization of the oak canopy cover, we detected a high potential concerning the Sentinel-2 data application in monitoring forest health conditions by NDVI application.

scholarly journals Validating the Predictive Power of Statistical Models in Retrieving Leaf Dry Matter Content of a Coastal Wetland from a Sentinel-2 Image

2019 ◽  
Vol 11 (16) ◽  
pp. 1936 ◽  
Author(s):  
Abebe Mohammed Ali ◽  
Roshanak Darvishzadeh ◽  
Kasra Rafiezadeh Shahi ◽  
Andrew Skidmore
Keyword(s):  
Remote Sensing ◽  
Dry Matter ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Vegetation Indices ◽  
Matter Content ◽  
Process Models ◽  
Dry Matter Content ◽  
Leaf Dry Matter Content ◽  
Sentinel 2

Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R2) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R2 of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R2 = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland.

scholarly journals Comparison of Proximal and Remote Sensing for the Diagnosis of Crop Status in Site-Specific Crop Management

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 19
Author(s):  
Jiří Mezera ◽  
Vojtěch Lukas ◽  
Igor Horniaček ◽  
Vladimír Smutný ◽  
Jakub Elbl
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Nitrogen Fertilizers ◽  
Wheat Crop ◽  
Significant Difference ◽  
Winter Wheat Crop ◽  
The Individual ◽  
Sentinel 2

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).

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.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

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