Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

Becker‐Reshef, Inbal; Franch, B.; Barker, B.; Murphy, Emilie; Santamaría-Artigas, Andrés; Humber, M. L.; Skakun, Sergii; Vermote, Éric

doi:10.3390/rs10101659

Cited by 19 publications

(12 citation statements)

References 59 publications

(90 reference statements)

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“…Wheat and maize are the main food crops in northern China and key components in food production [1, 2]. Satellite remote sensing technology can rapidly and accurately obtain information on crop planting areas, monitor planting status, and simultaneously map spatial distribution patterns, influencing factors, and planting probability while estimating crop output, water, fertilizer demand, and other information [3–6]. These data can provide important reference information for crop production planning management, making remote sensing monitoring of wheat and maize an important means to ensure food security by improving production management.…”

Section: Introductionmentioning

confidence: 99%

Area extraction and spatiotemporal characteristics of winter wheat–summer maize in Shandong Province using NDVI time series

et al. 2019

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The use of remote sensing to rapidly and accurately obtain information on the spatiotemporal distribution of large-scale wheat and maize acreage is of great significance for improving the level of food production management and ensuring food security. We constructed a MODIS-NDVI time series dataset, combined linear interpolation and the Harmonic Analysis of Time Series algorithm to smooth the time series data curve, and classified the data with random forest algorithms. The results show that winter wheat–summer maize planting areas were mainly distributed in the western plains, southern region, and north-eastern part of the middle mountainous regions while the eastern hilly regions were less distributed and scattered. The winter wheat–summer maize planting areas in the study area continued to grow from 2004–2016, with the most significant growth in the northern part of the western plains and Yellow River Delta. The spatial planting probability reflected the planting core area and showed an intensive planting pattern. During the study period, the peak value and time for the NDVI of the winter wheat were significantly different and showed an increasing trend, while these parameters for the summer maize were relatively stable with little change. Therefore, we mapped a spatial distribution of the winter wheat and summer maize, using the time series data pre-processing synthesis and phenology curve random forest classification methods. Through precision analysis, we obtained satisfactory results, which provided a straightforward and efficient method to monitor the winter wheat and summer maize.

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Section: Introductionmentioning

confidence: 99%

Area extraction and spatiotemporal characteristics of winter wheat–summer maize in Shandong Province using NDVI time series

et al. 2019

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“…This binary (crop/no-crop) mask has been developed for 2019, but there are plans to extend it to other years. Using crop masks from prior years is a pragmatic choice to monitor the same region in the current season (Becker-Reshef et al, 2018). We can use the crop location data to assess the accuracy of the crop mask for other years and to test its suitability to enable within-season crop mapping.…”

Section: Validation Of Cropland Masksmentioning

confidence: 99%

Location, biophysical and agronomic parameters for croplands in northern Ghana

Gómez-Dans

Lewis²,

Yin³

et al. 2022

Earth Syst. Sci. Data

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Abstract. Smallholder agriculture is the bedrock of the food production system in sub-Saharan Africa. Yields in Africa are significantly below potentially attainable yields for a number of reasons, and they are particularly vulnerable to climate change impacts. Monitoring of these highly heterogeneous landscapes is needed to respond to farmer needs, develop an appropriate policy and ensure food security, and Earth observation (EO) must be part of these efforts, but there is a lack of ground data for developing and testing EO methods in western Africa, and in this paper, we present data on (i) crop locations, (ii) biophysical parameters and (iii) crop yield, and biomass was collected in 2020 and 2021 in Ghana and is reported in this paper. In 2020, crop type was surveyed in more than 1800 fields in three different agroecological zones across Ghana (the Guinea Savannah, Transition and Deciduous zones). In 2021, a smaller number of fields were surveyed in the Guinea Savannah zone, and additionally, repeated measurements of leaf area index (LAI) and leaf chlorophyll concentration were made on a set of 56 maize fields. Yield and biomass were also sampled at harvesting. LAI in the sampled fields ranged from 0.1 to 5.24 m2 m−2, whereas leaf chlorophyll concentration varied between 6.1 and 60.3 µg cm−2. Yield varied between 190 and 4580 kg ha−1, with an important within-field variability (average per-field standard deviation 381 kg ha−1). The data are used in this paper to (i) evaluate the Digital Earth Africa 2019 cropland masks, where 61 % of sampled 2020/21 cropland is flagged as cropland by the data set, (ii) develop and test an LAI retrieval method from Earth observation Planet surface reflectance data (validation correlation coefficient R=0.49, root mean square error (RMSE) 0.44 m2 m−2), (iii) create a maize classification data set for Ghana for 2021 (overall accuracy within the region tested: 0.84), and (iv) explore the relationship between maximum LAI and crop yield using a linear model (correlation coefficient R=0.66 and R=0.53 for in situ and Planet-derived LAI, respectively). The data set, made available here within the context of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) initiative, is an important contribution to understanding crop evolution and distribution in smallholder farming systems and will be useful for researchers developing/validating methods to monitor these systems using Earth observation data. The data described in this paper are available from https://doi.org/10.5281/zenodo.6632083 (Gomez-Dans et al., 2022).

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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%

“…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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Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

Cited by 19 publications

References 59 publications

Area extraction and spatiotemporal characteristics of winter wheat–summer maize in Shandong Province using NDVI time series

Area extraction and spatiotemporal characteristics of winter wheat–summer maize in Shandong Province using NDVI time series

Location, biophysical and agronomic parameters for croplands in northern Ghana

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

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