Evaluation of Near-Surface Air Temperature From Reanalysis Over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

Santamaría-Artigas, Andrés; Franch, B.; Guillevic, Pierre; Roger, Jean-Claude; Vermote, Éric; Skakun, Sergii

doi:10.1109/jstars.2019.2902479

Cited by 10 publications

(11 citation statements)

References 38 publications

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“…As a consequence, sensor systems with a low spatial resolution but a high temporal resolution such as AVHRR, MODIS, and SPOT are used predominantly. The most popular crop to be forecast is wheat [84][85][86][87][88][89][90][91][92][93][94][95][96][97][98], followed by corn [93,94,[99][100][101][102][103][104][105][106], sugarcane [107][108][109][110], and rice [111,112]. Furthermore, forecasts have been made for wine [113] and potato [114] yields, as well as multiple crop types within a single study [91,93,94,99,100,115].…”

Section: Research Topicsmentioning

confidence: 99%

“…Crop yield at the end of the growing season is in most cases regressed against spectral indices measured at a specific point in mid-season or temporal metrics of these indices computed over parts of the growing season. The most frequently used index by far is the Normalized Difference Vegetation Index (NDVI) [84,[86][87][88][89][90][91][92][93][95][96][97][98]100,101,[106][107][108][109]113,114], followed by the Enhanced Vegetation Index (EVI) [84,95,105]. Furthermore, derived parameters like LAI [85,91,104] and FPAR [108,110] are used as explanatory variables.…”

Section: Research Topicsmentioning

confidence: 99%

“…Their study areas, however, are indicated as dots on the map. Note that studies with multiple distinct and distant study areas appear as multiple dots on the map, e.g., the studies of Becker-Reshef et al [86] and Santamaria-Artigas et al [92] in the Ukraine and U.S. Most studies were conducted in China (24), the U.S. (23), followed by India (16), Brazil 7, Iran 7, and France (7, including French overseas territories).…”

Section: Spatial Scope and Author Affiliationmentioning

confidence: 99%

“…The lead time of hydrological forecasts, for example, is determined by the river flow velocity and can therefore be modeled as a regression with a time lag of a few days between upper river measurements and downstream water level or discharge estimations [147][148][149][150][151]154]. In crop yield forecasting, time lags are determined between the harvest date and, for example, vegetation index measurements that in the most cases are taken one to three months in advance [84,[86][87][88][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105]107,109,111,112,114,115]. Near-real-time forecasts with lead times of only a few hours have been conducted by Sun et al [138], who modeled the spread of fire using a cellular automaton, as well as Licciardi et al [173] and Urbich et al [174] forecasting solar irradiance.…”

Section: Temporal Scopementioning

confidence: 99%

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Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

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Section: Research Topicsmentioning

confidence: 99%

Section: Research Topicsmentioning

confidence: 99%

Section: Spatial Scope and Author Affiliationmentioning

confidence: 99%

Section: Temporal Scopementioning

confidence: 99%

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Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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“…We used air temperature derived from NASA's Modern-Era Retrospective analysis for Research and Applications (MERRA2) reanalysis data set [42] to compute the growing degree days (GDDs) for winter wheat. Santamaria-Artigas et al (2019) [43] showed that MERRA2 is one of the best data sources for computing GDD for winter wheat in terms of error (<2K compared to in situ measurements), spatial resolution, and low delay in data availability. The data are provided on a regular grid that has 576 points in the longitudinal direction and 361 points in the latitudinal direction, corresponding to a resolution of 0.625 • × 0.5 • .…”

Section: Meteorological Datamentioning

confidence: 99%

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

et al. 2019

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A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3-5 days) at moderate spatial resolution (10-30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA's Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016-2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R 2 = 0.73 on cross-validation.is that methods for generating products from coarse spatial resolution sensors can be ported to moderate (Landsat 8/OLI, Sentinel-2/MSI) or high (Planet/PlanetScope) spatial resolution sensors. However, the practice shows that such a transition is not always straightforward due to larger data gaps because of clouds and uneven coverage, sensor characteristics, and increased spatial resolution (at least at the order of 100, when going from 250 to 30 m).Consider, for example, a crop yield assessment/forecasting application [4]. The hypothesis is that satellite-based features, such as vegetation indices (VIs) or biophysical parameters derived at a single date or accumulated over some time period, can be correlated to crop yields [5,6]. Since the reference data on crop yields are mainly available at the regional scale, the corresponding empirical models are built by averaging satellite-based features over those regions and correlating these derived variables with crop yields [7][8][9]. It is assumed that there is a homogeneity within the region in terms of crops grown and agricultural practices applied and, therefore, the averaging should be performed for satellite data acquired at the same (or approximately the same) stages of crop growth, meaning that the data are normalized. This is usually the case for coarse spatial resolution remote sensing sensors, which enable a higher likelihood of obtaining a high temporal frequency of cloud-free data over the Earth's surface [10,11]. This is also evidenced by multiple successful applications of coarse spatial resolution satellite data to crop yield assessment and forecasting [5,[7][8][9][12][13][14][15].However, this is not the case for moderate spatial resolution sat...

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Mathematical Models and Informational Technologies of Crop Yield Forecasting in Cloud Environment

Shumilo

Drozd

Kussul

et al. 2022

Lecture Notes in Networks and Systems

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Evaluation of Near-Surface Air Temperature From Reanalysis Over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

Cited by 10 publications

References 38 publications

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

Mathematical Models and Informational Technologies of Crop Yield Forecasting in Cloud Environment

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