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 study we present a methodology to develop a global winter wheat crop calendar based on the existing crop calendar products from FAO and GEOGLAM Crop Monitor in the framework of the WorldCereal project. It is based on integrating both datasets by building on the accuracy from Crop Monitor and the spatial resolution from the Food and Agriculture Organization of the United Nations (FAO). Additionally, given the global extent of WorldCereal and the gaps that both products present at global scale, we simulated the crop calendars in those areas not covered by any of the products. To do so, we integrated a Regression-Kriging model considering as training data the calendars derived from both products and based on the latitude, height and distance to the coast (DTC).

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.

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.

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].

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].

In this paper, we will evaluate the Vermote et al. method, hereafter referred to as VJB, in comparison to the MCD43 MODerate Resolution Imaging Spectroradiometer (MODIS) product, focusing on the white sky albedo parameter. We also present and study three different methods based on the VJB assumption, the 4param, 5param Rsqr, and 5param Vsqr. We use daily MODIS Climate Modeling Grid data both from Terra and Aqua platforms from 2002 to 2011 for all the pixels over Europe. We obtain an overall root-mean-square error of 5% when using the VJB method and 6.1%, 5.1%, and 5.3% for the 4param, 5param Rsqr, and 5param Vsqr methods, respectively. The main differences between the methods are located in areas where only few cloud-free snow-free samples were available that correspond mainly to mountainous areas during the winter. We finally conclude that the VJB method has an equivalent performance in deriving the white sky albedo results to the MODIS product with the advantage of daily temporal resolution. Additionally, we propose the 5param Rsqr method as an alternative to the VJB method due to its decreased data processing time.

It has been demonstrated that the spectrum of fluorescence emission is dependent on leaf temperature, thus there is a need for thermal information in order to interpret fluorescence signals. Temperature is also related to transpiration and stomata closure, which affects CO2 uptake and fluorescence. Therefore temperature measurements help to confirm the trends observed in fluorescence measurements. While fluorescence is immediately and uniquely related to photosynthesis, temperature provides additional information about plant status and instantaneous energy/water fluxes between plants and the atmosphere. The objective of this paper is to demonstrate the role of surface temperature in the context of FLuorescence EXplorer (FLEX) mission. To this end a database of land surface emissivity and temperature obtained from thermal radiometric measurements carried out in the framework of the Sen2FLEX (SENtinel-2 and FLuorescence Experiment) campaign has been used. These data were acquired in the agricultural site of Barrax (Spain) in June and July of 2005 simultaneously with airborne imagery acquired with Airborne Hyperspectral Scanner (AHS) and Compact Airborne Spectrographic Imager (CASI) sensors and data from the airborne fluorescence measuring instrument (AIRFLEX). As a result of these studies we have identified the optimal band configuration for the FLEX mission, that allows the estimation of land surface temperature with an accuracy lower than 1.5 K. To this end single-channel, split-window, and Temperature Emissivity Separation algorithms have been compared using a database of simulated brightness temperatures.