Aerosols play a critical role in radiative transfer within the atmosphere and in climate change. As part of the validation of atmospheric correction of remote sensing data affected by the atmosphere, it is critical to utilize appropriate aerosol models as aerosols are a main source of error. Here, we define the aerosol model by recalculating the aerosol microphysical properties based on the optical thickness at 440 nm and the Ångström coefficient obtained from numerous AERONET sites. The associated uncertainties are up to 23%, except for the imaginary part of the refractive index (about 38%). Uncertainties of the retrieved aerosol microphysical properties were incorporated in the framework for validating surface reflectance derived from space-borne Earth observation sensors. It yields an overall uncertainty of approximately of 1 to 3% of the retrieved surface reflectance in the MODIS red spectral band, well below the specification used for atmospheric correction.

We present the validation of surface reflectance from Sentinel-2 (S2) produced by LaSRC (Land Surface Reflectance Code) using an automated camera system (CAMSIS). The system is composed of four cameras (470, 550, 650, and 850nm wavelengths), and a motorized reference (50% reflectance) calibration target. CAMSIS is installed 120m above ground on a TV tower (WLEF) near Park Falls, Wisconsin, and captures data every 15 minutes. Surface reflectance and NDVI computed from CAMSIS calibrated data show good agreement when compared to observations from Sentinel-2. These results show good performance of LaSRC atmospheric correction.

The long-term data record (LTDR) has the goal of developing a quality and consistent Advanced Very High Resolution Radiometer (AVHRR) surface reflectance and albedo products dating back to 1982 at 0.05° spatial resolution. Distinguishing between cloud and snow is of critical importance when analyzing global albedo trends, for they influence the Earth’s energy balance. However, this task is specially challenging when working with AVHRR given its limited spectral bands. Therefore, the current version of the LTDR does not distinguish between snow and clouds. To this end, we propose the Moderate Resolution Imaging Spectroradiometer (MODIS)-based AVHRR Class Separation Algorithm (MACSSA), whose goal is to identify clear land and snow pixels using AVHRR data. We make use of a combination of optical and thermal information from satellite and reanalysis data, along with monthly climatology information. These are used as inputs for two different support vector machine (SVM) models, which are then applied to AVHRR data to retrieve the MACSSA predicted tags. These are compared first against reference tags retrieved from the MYD10C1 product over pixels with less than 2-min overpass time difference between MODIS Aqua and NOAA16–19, distributed all around the world, and second against the Climate Change Initiative Cloud (Cloud_cci AVHRR) project. We found the product to be highly accurate in identifying clear land pixels, with a probability of detection of clear pixels (PODclear) of 97%. The discrimination of snow and clouds shows a PODsnow of 89%, which is encouraging given the spectral limitations of the AVHRR sensor.

In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI) and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied to MODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.

In this work, we focus on exploring very high spatial resolution (1-3 m) satellite imagery for capturing crop yield variability at field scale. In-field yields of soybean and corn were collected in Iowa, USA, and were correlated with multi-spectral satellite data acquired by WorldView-3 (at 1.25 m) and PlanetScope (Dove-Classic) (at 3 m). Results show that the most important spectral bands explaining corn and soybean yield variability are green/yellow, red edge and NIR. High temporal frequency of Planet data allowed identification of best suitable date for yield assessment: PlanetScope's spectral bands at 3 m explained 10% to 75% of in-field corn and soybean yield variability.

The Long Term Data Record (LTDR) has the goal of developing a quality and consistent AVHRR surface reflectance product using the MODIS instrument as a reference. When a surface reflectance time series is acquired from satellites with variable observation geometry, the directional variation generates an apparent noise which can be corrected by modeling the bidirectional reflectance distribution function (BRDF). The well-established MODIS-based VJB method estimates a target's BRDF shape and corrects for directional effects, maintaining the high temporal resolution of the measurement, but its viability and optimization for AVHRR data hasn't been fully explored. In this study we explore different approaches to find the most robust way of applying the VJB correction in AVHRR data, considering that high noise in the AVHRR's red band (B1) makes the VJB method unstable. Our results show that 1-the VJB method can be modified to improve the stability of the correction parameters in AVHRR by ~5% and that 2- the time series noise using MODIS correction parameters is comparable to that using AVHRR derived parameters.

In this work, we evaluate the performance of the AVHRR surface reflectance LTDR V4 using Landsat-5 Thematic Mapper (TM5) Collection-1 surface reflectance data over 440 globally distributed sites, which give a representative set of land surface types and climate conditions. Surface reflectance anisotropy effects were normalized using the VJB method, and spectral response differences were accounted for with spectral band adjustment factors (SBAFs) computed as a function of the Normalized Difference Vegetation Index (NDVI) from a set of over 100,000 hyperspectral spectra from Hyperion. The performance of the AVHRR record is reported in terms of the accuracy, precision, and uncertainty, as compared to the specifications of the reference product. Results show that the AVHRR record performance is close to the 0.071ρ+0.0071 specification defined in the original TM5 product evaluation.

This study focuses on winter wheat yield assessment from NASA's Harmonized Landsat Sentinel-2 (HLS) product and meteorological observations through phenological fitting. Vegetation indices (VIs), namely difference vegetation index (DVI), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2), extracted from satellite optical data, are fitted per pixel against accumulated growing degree days (AGDD) using a quadratic function. Accumulated VIs are correlated against winter wheat yields. Results show a better performance from DVI compared to NDVI and EVI2.

ACIX is international collaborative initiatives to inter-compare a set of atmospheric correction (AC) algorithms for high-spatial resolution optical sensors. The exercises focus on Sentinel-2 and Landsat-8 data over a set of test areas. The inter-comparison contributes to a better understanding of strengths and weaknesses of different algorithms and ultimately to contribute to a reduction of a major error source for atmospheric correction and surface parameter retrievals.

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. In this study we present a new model based on the extrapolation of the pure wheat signal (100% of wheat within the pixel) from MODIS data at 1 km resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national and state level yield of winter wheat in the United States from 2001 to 2016.

The Harmonized Landsat-8 and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a seamless, harmonized surface reflectance record from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat-8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, geographic co-registration and common gridding, bidirectional reflectance distribution function normalization and bandpass adjustment. As of version 1.3, the HLS v1.3 data set covers 9.12 million km2 and spans from first Landsat-8 data (2013) to present. HLS products provide near-daily surface reflectance information with a common geometric framework, and are suitable for a variety of agricultural and vegetation monitoring tasks, including analysis of crop type, condition, and phenology.

Cross-calibration between sensors is necessary to bring measurements to a common radiometric scale; it allows a more complete monitoring of land surface processes and enhances data continuity and harmonization. However, differences in the Relative Spectral Response (RSR) of sensors generate uncertainties in the process [1]. For this reason, compensating for these differences is of great importance and can be achieved by using a spectral band adjustment factor (SBAF), which establishes a relationship between two spectrally adjusted bands. Nonetheless, this relationship has been shown to depend greatly on the surface type [2] and therefore needs to be corrected. In this work, we compute the SBAF between the historical Landsat and Sentinel 2 sensors by using the RSRs of different passive optical sensors in the Green, Red and NIR bands and the surface reflectance spectral libraries (ASTER, AVIRIS, IGCP) with a wide variety of classes. We produce a quadratic fit of the SBAF vs the surface's NDVI (ρnir- ρred)/(ρnir+ ρred) and propose an exponential correction equation dependent on the NDVI value for both bands. A comparison between Landsat 8 and Sentinel 2 images using the HLS product shows that this method improves the red band and NDVI accuracy by 46.4% and 63.9% respectively when the difference between the Relative Spectral Responses (RSR) is significant, but is inaccurate for the green band, where the atmospheric correction is likely to introduce same order errors.

In this work we analyzed the impact of using surface air temperature data from different reanalysis products on the forecast made by a winter wheat yield model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal [3], [4]. Four surface air temperature datasets generated by the NCEP2, MERRA2, JRA55, and ERA-Interim reanalysis projects were compared to NCEP1 data over the United States and Ukraine. For this, the bias was analyzed both spatially on a per-pixel basis, and temporally over the whole country. In both cases, the highest agreement with NCEP1 was found for NCEP2 and MERRA2. The per-pixel spatial analysis revealed that the largest differences (BIAS of up to 7.5°C) were found in pixels on mountainous areas of complex terrain. The temporal analysis of the spatially-averaged values showed a strong seasonality of the BIAS on both countries for all datasets, with a range of differences that varied from less than 0.1°C to 1°C during the summer months, to between 1 °C and ~4°C during the winter months. Analysis of the model's forecasts revealed differences consistent to those found in the temperature analysis, which shows the sensitivity of the forecast model to different surface air temperature input datasets.

With availability of images acquired by NASA/USGS Landsat 8 and European Copernicus Sentinel-2 remote sensing satellites, it becomes possible to provide a global coverage of Earth's surface every 3-5 days. Such high temporal resolution is a prerequisite for developing next generation products at moderate spatial resolution (10-30 m). This is especially important for applications, involving agricultural monitoring. This paper explores a combined use of Landsat 8 and Sentinel-2 data to winter wheat yield assessment at regional scale. We take advantage of the NASA's Harmonized Landsat and Sentinel-2 (HLS) product, which provides a seamless unified product from different sensors aboard both satellites. Multiple features are evaluated through correlation with winter wheat yield values with normalized difference vegetation index (NDVI) serving as a benchmark. We show that, when using Landsat 8 and Sentinel-2 data together, the error of winter wheat yield estimates can be reduced up to 1.8 times, compared to using a single satellite.

This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derive an explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS VIIRS, LANDSAT and Sentinel 2 data's and its validation using the AERONET data [1].

The land surface reflectance is a fundamental climate data record at the basis of the derivation of other climate data records (Albedo, LAI/Fpar, Vegetation indices) and has been recognized as a key parameter in the understanding of the land-surface-climate processes. In this presentation, we present the validation of the Land surface reflectance used for MODIS, VIIRS, Landsat 8 and Sentinel 2 data. This methodology uses the 6SV Code and data from the AERONET network. The overall accuracy clearly reaches the satellite specifications. To understand how to improve the validation, we developed an exhaustive error budget. Results show an impact of the absorption of aerosol and of the fine mode volume concentration.

This study aims at addressing misregistration issues between Landsat-8/OLI and Sentinel-2A/MSI at 30 m resolution using a phase correlation approach and multiple transformation functions. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30 m resolution between Landsat-8 and Sentinel-2A images were observed. The Random Forest regression used for constructing the mapping function showed best results, yielding an average RMSE error of 0.07 pixels at 30 m resolution for multiple tiles and multiple conditions.

This study aims at analyzing sub-pixel misregistration between multi-spectral images acquired by the Multi-Spectral Instrument (MSI) aboard Sentinel-2A remote sensing satellite, and exploring its potential for moving target and cloud detection. By virtue of its hardware design, MSI's detectors exhibit a parallax angle that leads to sub-pixel shifts that are corrected with special pre-processing routines. However, these routines do not correct shifts for moving and/or high altitude objects. In this letter, we apply a phase correlation approach to detect sub-pixel shifts between B2 (blue), B3 (green) and B4 (red) Sentinel-2A/MSI images. We show that shifts of more than 1.1 pixels can be observed for moving targets, such as airplanes and clouds, and can be used for cloud detection. We demonstrate that the proposed approach can detect clouds that are not identified in the built-in cloud mask provided within the Sentinel-2A Level-1C (L1C) product.

The land surface reflectance is a fundamental climate data record at the basis of the derivation of other climate data records (Albedo, LAI/Fpar, Vegetation indices) and has been recognized as a key parameter in the understanding of the land-surface-climate processes. In this presentation, we present the validation of the Land surface reflectance used for MODIS and VIIRS data. This methodology uses the 6SV Code and data from the AERONET network. The overall accuracy clearly reaches the MODIS and VIIRS specifications. To understand how to improve the validation, we developed an exhaustive error budget. Results show an impact of the absorption of aerosol and of the fine mode volume concentration. At the end, we discuss about the interest of the indirect and direct method for validation.

Wheat is one of the most important cereal crops in the world. Timely and accurate forecast of wheat yield and production at global scale is vital in implementing food security policy. Becker-Reshef et al. (2010) developed a generalized empirical model for forecasting winter wheat production using remote sensing data and official statistics. This model was implemented using static wheat maps. In this paper, we analyze the impact of incorporating yearly wheat masks into the forecasting model. We propose a new approach of producing in season winter wheat maps exploiting satellite data and official statistics on crop area only. Validation on independent data showed that the proposed approach reached 6% to 23% of omission error and 10% to 16% of commission error when mapping winter wheat 2-3 months before harvest. In general, we found a limited impact of using yearly winter wheat masks over a static mask for the study regions.

This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derive an explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS and LANDSAT data and its validation using the AERONET data [1].

A methodology for water vapor retrieval over ocean, using a differential absorption technique from near-IR channels, is presented. Over ocean, surface reflectance is close to zero and aerosol scattering is used to estimate water vapor contents, from an accurate radiative transfer code to account for optical thickness and scale height of aerosols. This method has been applied to POLDER data and comparisons with ECMWF (European Centre for Medium-Range Weather Forecasts) data are presented over the Straits of Gibraltar.