Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information for many applications. Traditional approaches to ...crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5–2months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001–2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of >90% can be achieved in mapping winter crops 1.5–2months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R2>0.85.
•An automatic method for early season large-area winter crop mapping is proposed.•The method is based on MODIS NDVI, GDD and Gaussian Mixture Model.•The method can map winter crops 1.5–2months before harvest with accuracies >90%.•Mapping results correspond to official statistics with R2>0.85.
This study aims at validating Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) products derived from MODIS surface reflectance (MOD09CMG) at coarse ...resolution (0.05°) over crops. These Essential Climate Variables (ECVs) are estimated by using the inversion of the PROSAIL radiative transfer (BV-NNET tool) applied on MODIS BRDF (Bidirectional Reflectance Distribution Function) corrected surface reflectances and non-corrected. ECV estimates and the corresponding MCD15A3 Collection 5 and GEOLAND-2 (GEOv1) products are compared with ECV reference maps derived from BV-NNET applied on 105 high spatial resolution images (Formosat-2, 8m) which were acquired from 2006 to 2010 in Southwest France. These latter are compared with local scale in situ measurements. The validation shows an uncertainty of 0.35 and 0.07 for LAI and FAPAR, respectively.
The comparison shows that the ECV estimates from the three products properly capture the crops phenology in agreement with reference maps. Results indicate that MCD15A3 uncertainties (0.23 and 0.07 for LAI and FAPAR, respectively) are similar to previous intercomparison studies. GEOv1 shows a systemic positive bias for both LAI and FAPAR. The best agreement with the reference maps is found for MODIS BV-NNET products with r2 higher than 0.9 and relative uncertainties lower than 17%. The use of BRDF-corrected surface reflectances as input of BV-NNET tool improves the uncertainty of LAI estimates (0.11, compared to 0.17 when directional surface reflectances are used as input) but not the uncertainty of FAPAR estimates. The deviation between FAPAR products which mostly affects low winter FAPAR, is related to the discrepancy of the soil directional assumption in PROSAIL model and BRDF correction method. The temporal stability of the daily MODIS BV-NNET products is better than the 4-day composite MCD15A3 products. Finally, BV-NNET tool applied at finer resolutions demonstrates that the increase of the resolution results in a decrease of the LAI and FAPAR uncertainties and a conservation of the biases.
•We validate cropland LAI/FAPAR MOD15, GEOv1 and from BV-NNET applied on MODIS data.•We use 40 crop measurements and 105 Formosat-2 data to assess temporal consistency.•Formosat-2 and MODIS reflectances agree due to good atmospheric/BRDF corrections.•LAI/FAPAR estimates uncertainties fit in the GCOS requirements.•All products show significant temporal agreements (r2>0.6) except for winter FAPAR.
Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the >40 years of inland water observations made possible by optical remote ...sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to ship-borne, underway in situ validation measurements. The land surface product shows the best agreement (4% Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and generates 36% higher reflectance values in the visible bands compared to aquatic methods (ACOLITE and SeaDAS) with larger differences between land and aquatic products observed in Sentinel-2 (0.01 sr−1) compared to Landsat-8 (0.001 sr−1). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59% and 35% respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 5–31% depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance.
•Landsat-8 and Sentinel-2 images are compared over three large rivers.•Over 13,000 underway, ship-borne measurements were collected for validation.•Land surface reflectance product agreed within 4% of field data in turbid waters.•Choice of correction biases satellite-retrieved Chl-a and turbidity by 3–59%.
Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 ...launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality.
Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and follow-up with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat.
•Landsat program approaching 50 years of continuous global data collection.•Landsat-8 successfully on-orbit; Landsat-9 under development; Landsat-10 being scoped.•Open data has accelerated science and application developments.•Value of calibrated data shown for science, applications, and towards virtual constellations.•Time series analysis of Landsat offering new insights on earth system and human activity.
The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980's to the present. Over the years, several efforts have been made ...on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR Surface Reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geo-location, improvement of the cloud masking and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream Leaf Area Index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by 1 and 2 are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980's, the results have errors equivalent to those derived from MODIS.
Crop yield monitoring is an important component in agricultural assessment. Multispectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer ...(AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 µm), red-edge (0.726 µm), and near-infrared (NIR - 0.865 µm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R^2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R^2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.
This paper presents a simple and efficient image processing method for estimating the number of coconut trees in the Tonga region using very high spatial resolution data (30 cm) in the blue, green, ...red and near infrared spectral bands acquired by the WorldView-3 sensor. The method is based on the detection of tree shadows and the further analysis to reject false detection using geometrical properties of the derived segments. The algorithm is evaluated by comparing coconut tree counts derived by an expert through photo-interpretation over 57 randomly distributed (4% sampling rate) segments of 200 m × 200 m over the Vaini region of the Tongatapu island. The number of detected trees agreed within 5% versus validation data. The proposed method was also evaluated over the whole Tonga archipelago by comparing satellite-derived estimates to the 2015 agricultural census data—the total tree counts for both Tonga and Tongatapu agreed within 3%.
Surface reflectance is one of the key products from VIIRS and as with MODIS, is used in developing several higher-order land products. The VIIRS Surface Reflectance (SR) Intermediate Product (IP) is ...based on the heritage MODIS Collection 5 product (Vermote, El Saleous, & Justice, 2002). The quality and character of surface reflectance depend on the accuracy of the VIIRS Cloud Mask (VCM), the aerosol algorithms and the adequate calibration of the sensor. The focus of this paper is the early evaluation of the VIIRS SR product in the context of the maturity of the operational processing system, the Interface Data Processing System (IDPS). After a brief introduction, the paper presents the calibration performance and the role of the surface reflectance in calibration monitoring. The analysis of the performance of the cloud mask with a focus on vegetation monitoring (no snow conditions) shows typical problems over bright surfaces and high elevation sites. Also discussed is the performance of the aerosol input used in the atmospheric correction and in particular the artifacts generated by the use of the Navy Aerosol Analysis and Prediction System. Early quantitative results of the performance of the SR product over the AERONET sites show that with the few adjustments recommended, the accuracy is within the threshold specifications. The analysis of the adequacy of the SR product (Land PEATE adjusted version) in applications of societal benefits is then presented. We conclude with a set of recommendations to ensure consistency and continuity of the JPSS mission with the MODIS Land Climate Data Record.
•We provide an early assessment of the VIIRS Surface Reflectance (SR) and Cloud Mask.•Both products are fundamental in the derivation of the downstream land products.•The VIIRS instrument is found to perform well.•Quantitative performances of VIIRS SR are presented and compared to MODIS SR.•Some application of VIIRS SR to agricultural monitoring is presented.
This study investigates misregistration issues between Landsat-8/ Operational Land Imager and Sentinel-2A/ Multi-Spectral Instrument at 30 m resolution, and between multi-temporal Sentinel-2A images ...at 10 m resolution using a phase-correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. 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, and 1.2 pixels and 2.8 pixels at 10 m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07 ± 0.02 pixels at 30 m resolution, and 0.09 ± 0.05 and 0.15 ± 0.06 pixels at 10 m resolution for the same and adjacent Sentinel-2A orbits, respectively, for multiple tiles and multiple conditions. A simpler 1st order polynomial function (affine transformation) yielded RMSE of 0.08 ± 0.02 pixels at 30 m resolution and 0.12 ± 0.06 (same Sentinel-2A orbits) and 0.20 ± 0.09 (adjacent orbits) pixels at 10 m resolution.
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