Assessment of PRISMA imaging spectrometer data for the estimation of topsoil properties of agronomic interest at the field scale

2020 ◽  
Author(s):  
Raffaele Casa ◽  
Stefano Pignatti ◽  
Simone Pascucci ◽  
Victoria Ionca ◽  
Nada Mzid ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Precision Agriculture ◽  
Central Italy ◽  
Bare Soil ◽  
Hyperspectral Data ◽  
Least Squares Regression ◽  
Field Scale ◽  
Sampling Campaign ◽  
Spectrometer Data ◽  
Sentinel 2

<p>On the 22 March 2019 the Italian Space Agency (ASI) launched the PRISMA satellite, having onboard a hyperspectral imager covering the 400-2500 nm range with 234 spectral bands and about 10 nm of bandwidth. The ground spatial resolution is 30 m, plus a panchromatic camera with 5 m spatial resolution. One of the potential application areas of this scientific mission is for precision agriculture applications, among which the mapping of field-scale variability of topsoil properties is of particular interest.</p><p>PRISMA clear-sky hyperspectral images were acquired in autumn and spring 2019 on the Maccarese farm in Central Italy, in the framework of the PRISCAV project, which is aimed at a first assessment of the PRISMA data. An intensive soil sampling campaign was performed, using a ground sampling scheme adapted to PRISMA and Sentinel-2 spatial resolutions, in the fields where bare soil was exposed at the satellite acquisition dates. Soil texture (clay, silt, sand), carbonates, pH and soil organic carbon (SOC) for the collected soil samples were then determined in the laboratory.</p><p>The dataset was then used to test calibration and validation of PLSR (Partial Least Squares Regression) and RF (Random Forest) models developed using PRISMA surface reflectance data. To this aim, several pre-treatment tests were performed, including pan-sharpening at 5 m using PRISMA panchromatic data as well as Sentinel-2 multispectral data.</p><p>The results show that the good results could be obtained in particular for clay estimation. The best-performing algorithm for topsoil properties retrieval using PRISMA hyperspectral data was RF algorithm as compared with PLSR.</p>

Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

2020 ◽  
Author(s):  
Nada Mzid ◽  
Stefano Pignatti ◽  
Irina Veretelnikova ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Central Italy ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Field Scale ◽  
Umbria Region ◽  
Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

scholarly journals A PLSR model to predict soil salinity using Sentinel-2 MSI data

2021 ◽  
Vol 13 (1) ◽  
pp. 977-987
Author(s):  
Ghada Sahbeni
Keyword(s):  
Soil Salinity ◽  
Spectral Characteristics ◽  
Management Strategies ◽  
Salt Content ◽  
Soil Surface ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Remotely Sensed Data ◽  
Sampling Campaign ◽  
Sentinel 2

Abstract Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R 2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies.

scholarly journals Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology

2021 ◽  
Vol 13 (1) ◽  
pp. 123
Author(s):  
Anting Guo ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Huichun Ye ◽  
Huiqin Ma ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Yellow Rust ◽  
Vegetation Indices ◽  
Hyperspectral Images ◽  
Disease Monitoring ◽  
Least Squares Regression ◽  
Field Scale ◽  
Near Surface ◽  
Infection Period ◽  
Monitoring Accuracy

Yellow rust is a worldwide disease that poses a serious threat to the safety of wheat production. Numerous studies on near-surface hyperspectral remote sensing at the leaf scale have achieved good results for disease monitoring. The next step is to monitor the disease at the field scale, which is of great significance for disease control. In our study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain hyperspectral images at the field scale. Vegetation indices (VIs) and texture features (TFs) extracted from the UAV-based hyperspectral images and their combination were used to establish partial least-squares regression (PLSR)-based disease monitoring models in different infection periods. In addition, we resampled the original images with 1.2 cm spatial resolution to images with different spatial resolutions (3 cm, 5 cm, 7 cm, 10 cm, 15 cm, and 20 cm) to evaluate the effect of spatial resolution on disease monitoring accuracy. The findings showed that the VI-based model had the highest monitoring accuracy (R2 = 0.75) in the mid-infection period. The TF-based model could be used to monitor yellow rust at the field scale and obtained the highest R2 in the mid- and late-infection periods (0.65 and 0.82, respectively). The VI-TF-based models had the highest accuracy in each infection period and outperformed the VI-based or TF-based models. The spatial resolution had a negligible influence on the VI-based monitoring accuracy, but significantly influenced the TF-based monitoring accuracy. Furthermore, the optimal spatial resolution for monitoring yellow rust using the VI-TF-based model in each infection period was 10 cm. The findings provide a reference for accurate disease monitoring using UAV hyperspectral images.

scholarly journals Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

2021 ◽  
Vol 13 (17) ◽  
pp. 3345
Author(s):  
Fabio Castaldi
Keyword(s):  
Time Series ◽  
Soil Properties ◽  
Spatial Resolution ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Visible Region ◽  
Bare Soil ◽  
Landsat 8 ◽  
Economic Perspectives ◽  
Sentinel 2

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO3 prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO3 content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra.

scholarly journals COMPARISON OF UNCALIBRATED RGBVI WITH SPECTROMETER-BASED NDVI DERIVED FROM UAV SENSING SYSTEMS ON FIELD SCALE

2016 ◽  
Vol XLI-B8 ◽  
pp. 837-843 ◽  
Author(s):  
G. Bareth ◽  
A. Bolten ◽  
M. L. Gnyp ◽  
S. Reusch ◽  
J. Jasper
Keyword(s):  
Winter Wheat ◽  
Data Acquisition ◽  
Precision Agriculture ◽  
Growth Parameters ◽  
Crop Protection ◽  
Crop Management ◽  
Hyperspectral Data ◽  
Field Scale ◽  
Sensing Systems ◽  
Low Weight

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers’ field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

scholarly journals A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity

2020 ◽  
Vol 12 (15) ◽  
pp. 2399 ◽  
Author(s):  
Red Willow Coleman ◽  
Natasha Stavros ◽  
Vineet Yadav ◽  
Nicholas Parazoo
Keyword(s):  
Los Angeles ◽  
Land Cover ◽  
Spatial Resolution ◽  
Urban Areas ◽  
High Spatial Resolution ◽  
Water Cycle ◽  
Bare Soil ◽  
Urban Vegetation ◽  
Optical Imagery ◽  
Sentinel 2

High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities.

Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy

2020 ◽  
Author(s):  
Calogero Schillaci ◽  
Edoardo Tomasoni ◽  
Marco Acutis ◽  
Alessia Perego
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Northern Italy ◽  
Yield Data ◽  
Batch Mode ◽  
Area Index ◽  
Sentinel 2

&lt;p&gt;To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with &lt;25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R&lt;sup&gt;2&lt;/sup&gt;=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.&lt;/p&gt;

scholarly journals COMPARISON OF UNCALIBRATED RGBVI WITH SPECTROMETER-BASED NDVI DERIVED FROM UAV SENSING SYSTEMS ON FIELD SCALE

2016 ◽  
Vol XLI-B8 ◽  
pp. 837-843 ◽  
Author(s):  
G. Bareth ◽  
A. Bolten ◽  
M. L. Gnyp ◽  
S. Reusch ◽  
J. Jasper
Keyword(s):  
Winter Wheat ◽  
Data Acquisition ◽  
Precision Agriculture ◽  
Growth Parameters ◽  
Crop Protection ◽  
Crop Management ◽  
Hyperspectral Data ◽  
Field Scale ◽  
Sensing Systems ◽  
Low Weight

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (&lt; 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (&lt; 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers’ field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

Capability of maize water use efficiency estimation at field scale using Sentinel-2 data

2020 ◽  
Author(s):  
Zonghan Ma ◽  
Bingfang Wu ◽  
Nana Yan ◽  
Weiwei Zhu
Keyword(s):  
Water Management ◽  
High Resolution ◽  
Water Use Efficiency ◽  
Water Use ◽  
Water Consumption ◽  
Precision Agriculture ◽  
Gross Primary Production ◽  
Field Scale ◽  
Use Efficiency ◽  
Sentinel 2

&lt;p&gt;Water use efficiency (WUE) is defined as the ratio between gross primary production (GPP) and evapotranspiration (ET) at ecosystem scale, which can help understand the mechanism between water consumption and crop production in guiding field water management. Water consumption control is important in precision agriculture development. Mapping WUE at field scale using remote sensing data could provide crop water use status at high resolution and acquire the WUE spatial distribution. In this study we proposed a method to estimate field-scale maize WUE with Sentienl-2 data. The GPP of maize is estimated by a light use efficiency model with RS observed albedo, sunshine radiation, fraction of photosynthetically active radiation (fpar) fitted using in site observation. Maize ET is modelled using FAO-PM model with crop coefficient simulated using vegetation indexes acquired from Sentinel-2 bands. We compared the GPP, ET and final WUE estimation with eddy covariance (EC) observations in a maize field of North China Plain where water scarcity is a main limit factor of crop development. Comparation results show a high correlation between in site observation and modelled results. Combining the phenology development of maize, the temporal characteristics of maize WUE change is associated with phenology. WUE was low after sowing, then increased during Elongation stage. Maize WUE peaked at Heading and Grouting period and decreased in Maturation stage. Our WUE estimation method with high resolution could guide adopting various irrigation strategies based on different WUE conditions at field scale. This research could help shed light on the future WUE development under climate change background and improve our knowledge of precise water management.&lt;/p&gt;

scholarly journals Spectral Data Collection by Dual Field-Of-View System Under Changing Atmospheric Conditions—A Case Study of Estimating Early Season Soybean Populations

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 457 ◽  
Author(s):  
Ittai Herrmann ◽  
Steven K. Vosberg ◽  
Philip A. Townsend ◽  
Shawn P. Conley
Keyword(s):  
Data Collection ◽  
Precision Agriculture ◽  
Hyperspectral Data ◽  
Field Of View ◽  
Great Promise ◽  
Soybean Plant ◽  
Least Squares Regression ◽  
Atmospheric Conditions ◽  
Commercial Applications

There is an increasing interest in using hyperspectral data for phenotyping and crop management while overcoming the challenge of changing atmospheric conditions. The Piccolo dual field-of-view system collects up- and downwelling radiation nearly simultaneously with one spectrometer. Such systems offer great promise for crop monitoring under highly variable atmospheric conditions. Here, the system’s utility from a tractor-mounted boom was demonstrated for a case study of estimating soybean plant populations in early vegetative stages. The Piccolo system is described and its performance under changing sky conditions are assessed for two replicates of the same experiment. Plant population assessment was estimated by partial least squares regression (PLSR) resulting in stable estimations by models calibrated and validated under sunny and cloudy or cloudy and sunny conditions, respectively. We conclude that the Piccolo system is effective for data collection under variable atmospheric conditions, and we show its feasibility of operation for precision agriculture research and potential commercial applications.

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