Orthorectification and Pan-Sharpening of WorldView-2 Satellite Imagery to Produce High Resolution Coloured Ortho-Photos

In the last decade VHR (Very High Resolution) images from satellite, because of the reduced dimensions of pixel (less than 1 meter) and the availability in different acquisition bands (4 or more), have had major dffusion in many application fields of remote sensing. They can be used also to produce high resolution coloured ortho-photos, but adequate levels of positional accuracy as well as small pixel dimensions are necessary. The aim of this paper is to demonstrate that WorldView-2 (WV-2) images satisfy totally these requirements if firstly submitted to high accurate rectification and Pan-Sharpening processes. Using Rational Polynomial Functions (RPFs), original dataset can be better overlapped to cartographic maps at medium or great scale; multispectral images (cell size: 2 m) can be resampled to meet geometric resolution of pan one (cell size: 0.5 m), so detailed and attendible RGB composition results. Applications are carried out on one sample of WV-2 imagery concerning a scene within the Province of Caserta (Italy) that includes vegetated as well as urban areas. Finally RGB composition with pixel dimensions of 0.5 m, positional accuracy less than 1 meter and likely colors are achieved, confirming the possibility to use this type of images for coloured ortho-photos at scale 1:5.000 at least

Assessing Crop Water Requirement and Yield by Combining ERA5-Land Reanalysis Data with CM-SAF Satellite-Based Radiation Data and Sentinel-2 Satellite Imagery

The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest data sources as input for crop–water balance models, thereby improving the crop water requirements (CWR) and yield estimates from the field to the regional scale. Satellite imagery and numerical weather prediction outputs offer high resolution (in time and space) gridded data that can compensate for the paucity of crop parameter field measurements and ground weather observations, as required for assessments of CWR and yield. In this study, the AquaCrop model was used to assess CWR and yield of tomato on a farm in Southern Italy by assimilating Sentinel-2 (S2) canopy cover imagery and using CM-SAF satellite-based radiation data and ERA5-Land reanalysis as forcing weather data. The prediction accuracy was evaluated with field data collected during the irrigation season (April–July) of 2021. Satellite estimates of canopy cover differed from ground observations, with a RMSE of about 11%. CWR and yield predictions were compared with actual data regarding irrigation volumes and harvested yield. The results showed that S2 estimates of crop parameters represent added value, since their assimilation into crop growth models improved CWR and yield estimates. Reliable CWR and yield estimates can be achieved by combining the ERA5-Land and CM-SAF weather databases with S2 imagery for assimilation into the AquaCrop model

Retrieval of evapotranspiration from sentinel-2: Comparison of vegetation indices, semi-empirical models and SNAP biophysical processor approach

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this studyperformeda comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived fromFAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas

Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy

Study region: The study region is represented by seven irrigation districts distributed under different climate and topography conditions in Italy. Study focus: This study explores the reliability and consistency of the global ERA5 single levels and ERA5-Land reanalysis datasets in predicting the main agrometeorological estimates commonly used for crop water requirements calculation. In particular, the reanalysis data was compared, variable-by-variable (e.g., solar radiation, R-s; air temperature, T-air; relative humidity, RH; wind speed, u(10); reference evapotranspiration, ET0), with in situ agrometeorological obser-vations obtained from 66 automatic weather stations (2008-2020). In addition, the presence of a climate-dependency on their accuracy was assessed at the different irrigation districts. New hydrological insights for the region: A general good agreement was obtained between observed and reanalysis agrometeorological variables at both daily and seasonal scales. The best perfor-mance was obtained for T-air, followed by RH, R-s, and u(10) for both reanalysis datasets, especially under temperate climate conditions. These performances were translated into slightly higher accuracy of ET0 estimates by ERA5-Land product, confirming the potential of using reanalysis datasets as an alternative data source for retrieving the ET0 and overcoming the unavailability of observed agrometeorological data

Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery

Due to their level of spatial detail (pixel dimensions equal to or less than 1 m), very high-resolution satellite images (VHRSIs) need particular georeferencing and geometric corrections which require careful orthorectification. Although there are several dedicated algorithms, mainly commercial and free software for geographic information system (GIS) and remote sensing applications, the quality of the results may be inadequate in terms of the representation scale for which these images are intended. This paper compares the most common orthorectification algorithms in order to define the best approach for VHRSIs. Both empirical models (such as 2D polynomial functions, PFs; or 3D rational polynomial functions, RPFs) and rigorous physical and deterministic models (such as Toutin) are considered. Ground control points (GCPs) and check points (CPs)—whose positions in the image as, well as in the real world, are known—support algorithm applications. Tests were executed on a WorldView-2 (WV-2) panchromatic image of an area near the Gulf of Naples in Campania (Italy) to establish the best-performing algorithm. Combining 3D RPFs with 2D PFs produced the best results

Comparison of Different Methods to Rectify IKONOS Imagery without Use of Sensor Viewing Geometry

Satellite images can be rectified and adapted to map representation also without information about viewing geometry of the sensor. Polynomial Functions (PFs) or Rational Polynomial Functions (RPFs) can be applied for this purpose, both requiring Ground Control Points (GCPs), of which the positions in the image as well as in the real world must be known. Typically for PFs only planimetric (X, Y) positions of GCPs are used while for RPFs 3D coordinates (X, Y, Z) of them as well as a DEM (Digital Elevation Model) of the entire scene are required. Check Points (CPs) with the same characteristics of GCPs (but not coincident with them) are used to better verify the accuracy of the product. Not only topographic survey, but also maps or ortho-photos with adequate resolution supply the coordinates of GCPs as well as CPs. This paper is aimed to compare methods to rectify IKONOS images based on PFs or RPFs applications, considering the positional accuracy of the results as index for performance evaluation. Tests were executed on IKONOS panchromatic image of an area of the Cilento and Vallo di Diano National Park, in Campania Region (Italy): ortho-photos (scale 1:10,000) were used for GCPs and CPs planimetric position in the real world while for RPFs applications also DEM (cell size 20 m) was considered as source of 3D information. To compare the selected methods, differences (residuals) between the X, Y coordinates of GCPs (but also of the CPs) on the ortho-photos and corresponding values in the rectified image were calculated and evaluated. The positional accuracy of the resulting products in relation to the method as well as to the number of GCPs was analyzed; also the implications of the calculation of Rational Polynomial Coefficients (RPCs) in alternative to the use of the values supplied for them by the image provider were investigated

Comparison of Methods for IKONOS Images Pan-sharpening Using Synthetic Sensors

Abstract: Many methods are present in literature for pan-sharpening of satellite images: they permit to transfer geometric resolution of panchromatic data to multispectral ones, but the results of their application are different. To evaluate the quality of these products, visual analysis is carried out, above all on the RGB composition to detect colour distortion. To quantize the level of similarity of the pan-sharpened images with them that should be achieved with effective more effective sensors, several indices are available such as: RMSE, correlation coefficients, UIQI, RASE. The principal limit of these indices consists in the terms of comparison because they compare the pansharpened images with the original ones that are with lower resolution. To supply the unavailability of the effective dataset with the same pixel dimensions of the pan-sharpened files, synthetic sensors can be introduced with lower resolution than the original ones. The correspondent degraded images can be submitted to pan-sharpening process and the results can be considered performed if similar to the original multispectral dataset. In this study IKONOS synthetic sensors are introduced to compare different methods: transforming the digital numbers into the radiance of the earth surface, original images of Campania Region are degraded and then submitted to some pan-sharpening approaches. The following methods are considered: multiplicative, simple mean, IHS, Fast IHS, Brovey, Weighted Brovey, Gram Schmidt, Zhang. Each resulting dataset is compared with the original multispectral one to evaluate the performance of each method

Application of different pan-sharpening methods on WorldView-3 images

In the field of the remote sensing, the introduction of high resolution satellite sensors has required the development of several data fusion approaches. Two kinds of images are usually acquired: multispectral and panchromatic. The first group has a lower spatial resolution but accurate spectral information while the second presents a higher spatial resolution with a longer band acquisition range. Pan-sharpening permits to combine panchromatic and multispectral data to create new multispectral images with higher geometric resolution. In this paper nine different pan-sharpening methods are tested on WorldView-3 images: Brovey, Weighted Brovey, Gram Schmidt, IHS, Fast IHS, Multiplicative, Principal Component Analysis (PCA), Simple Mean and Zhang. With the aim to rank the techniques efficiency, visual inspections combined with quantitative evaluations are performed to test spectral qualities of the fused images. This is a difficult task because the quality of the fused image depends on the considered datasets: RMSE (Root Mean Square Error) and ERGAS (Relative Dimensionless Global Error) are the accuracy indices used for this scope