Using Remote Sensing for Agricultural Statistics

Carfagna, Elisabetta; Gallego, Francisco Javier

doi:10.1111/j.1751-5823.2005.tb00155.x

Cited by 105 publications

(44 citation statements)

References 30 publications

(26 reference statements)

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“…The extraction accuracy of the winter wheat directly affected the accuracy of the Remote Sens. 2018, 10, 962 7 of 25 yield estimation [42]. For accurately extracting the winter wheat area in Hengshui City, the study used specific phenological characteristics of the winter wheat and Sentinel-2 time-series data with a high spatial resolution of 10 m.…”

Section: Winter Wheat Planting Area Datasetmentioning

confidence: 99%

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

et al. 2018

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Abstract:The rapid and accurate estimation of wheat production at a regional scale is crucial for national food security and sustainable agricultural development. This study developed a new gross primary productivity (GPP) estimation model (denoted as the [ACPM]), based on the effects of light, heat, soil moisture, and nitrogen content (N) on the light-use efficiency of winter wheat. The ACPM model used the quantic additivity of the environmental factors to improve the minimum form or multiple multiplication form in the previous model and thus characterized the joint effects of heat, soil moisture, and N on crop photosynthesis performance. The key parameters (i.e., light) were determined from the photosynthetically active radiation product of the Himawari-8 sensor and the fraction of photosynthetically active radiation product of Moderate Resolution Imaging Spectroradiometer (MODIS). The heat was determined from the land temperature products of MODIS. The soil moisture was obtained from the inversion using a visible and shortwave infrared drought index (VSDI), whereas the N stress of winter wheat was detected using the newly developed modified ratio vegetation index (MRVI), which could accurately obtain the spatiotemporal distribution of the leaf chlorophyll content of winter wheat. The ACPM and two other previous models (named the GPP1 and GPP2 models) were applied on the Himawari-8 and MODIS images in Hengshui City. The evaluation results, based on the ground measurement, indicated that the ACPM models exhibited the best estimate of dry aboveground biomass (DAM) and the wheat yield in Hengshui City, with errors of <10% and <12% for the DAM and yield, respectively. Considering the easy operation of the ACPM model and the accessibility of the corresponding satellite images, the Agriculture Crop Photosynthesis Model (ACPM) can be expected to provide information on the winter wheat shortfalls and surplus ahead of the availability of official statistical data.

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Section: Winter Wheat Planting Area Datasetmentioning

confidence: 99%

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

et al. 2018

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“…This resulted in the final wheat area forecast. This type of regression adjustment for area estimates from remote sensing data is a common technique [48] and is also used by the USDA National Agricultural Statistics Service (USDA-NASS) [49]. The adjusted area estimates were multiplied with the yield forecasts to arrive at the wheat production forecasts.…”

Section: Weighted VI Time Seriesmentioning

confidence: 99%

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

et al. 2014

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Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. 9654 reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three-or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.

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“…Remote sensing images with a spatial resolution ranging from 10m (such as SPOT5 HRG) to 30 m (such as Landsat TM or ETM+) are applied for cropland identification in most landscapes (Carfagna & Gallego, 2005). In North China Plain, fragmented field parcels are distributed widely which is still a limited factor for cropland estimation using MODIS.…”

Section: Materials and Pre-processingmentioning

confidence: 99%

A Stratified Temporal Spectral Mixture Analysis Model for Mapping Cropland Distribution through MODIS Time-Series Data

Zhang¹,

Wei²,

Sun³

et al. 2015

JAS

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The aim of study was to develop a stratified temporal spectral mixture analysis (STSMA) for cropland area estimation using MODIS time-series data to address the mixed pixel problem caused from coarse resolution. The proposed method used thematic map from MODIS classification as prior knowledge to determine the endmember set for each sub-region input into SMA model. The results indicated the STSMA method performing better in estimating the cropland land. RMSEs (from 0.25 to 010), R 2 s (from 0.65 to 0.89) and bias (0.02), used as three accuracy assessment parameters, and STSMA obtained higher overall accuracy in the entire study area, at individual pixel scale to 0.10 at 10×10 pixels scale, representing higher performance compared to the conventional spectral mixture analysis (SMA) method at each pixel scale. In single-season crop, dual-season crop, natural vegetation and non-vegetation dominated landscape, the similar success from STSMA is also achieved because the suitable endmember set was set for the proposed model to ensure the accuracy of cropland estimation to address the conventional SMA colinearity problem at some degree.

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Using Remote Sensing for Agricultural Statistics

Cited by 105 publications

References 30 publications

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

A Stratified Temporal Spectral Mixture Analysis Model for Mapping Cropland Distribution through MODIS Time-Series Data

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