Soil characteristics influence on the winter wheat yield regression modeling using NDVI vegetation index

The research results of the dependence of the average district winter wheat yield on satellite and ground meteorological information for the subjects of the North Caucasian and Volga UGMS are presented. The following satellite indices were used in the work: NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index) and LAI (Leaf Area Index). The method of interpolation of inverse weighted squares of distances for obtain a set of meteorological parameters for districts there were no weather stations was used. Districts for taking into account agroclimatic conditions were combined into groups using Shashko's Agroclimatic Regionalization method. The selection of parameters that have the greatest impact on the yield was carried out using the correlation-regression analysis method. The corresponding regression models were obtained for the researched regions of the Russian Federation. Verification of the obtained models on dependent and independent information showed a fairly good result. Keywords: NDVI, LAI, interpolation, Shashko's Agroclimatic Regionalization, average district yield, meteorological information Tab. 5. Fig. 7. Ref. 20.

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

Remote Sensing ◽

10.3390/rs10101659 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1659 ◽

Cited By ~ 4

Author(s):

Inbal Becker-Reshef ◽

Belen Franch ◽

Brian Barker ◽

Emilie Murphy ◽

Andres Santamaria-Artigas ◽

...

Keyword(s):

Time Series ◽

Winter Wheat ◽

Spatial Resolution ◽

Vegetation Index ◽

Time Series Data ◽

Crop Yields ◽

Wheat Yield ◽

Series Data ◽

Crop Type ◽

Winter Wheat Yield

Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data.

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Forecasting the winter wheat yield based on the vegetation index NDVI dynamic model

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/848/1/012191 ◽

2021 ◽

Vol 848 (1) ◽

pp. 012191

Author(s):

A G Bukhovets ◽

E A Semin ◽

M V Kucherenko ◽

S I Iablonovskaia

Keyword(s):

Winter Wheat ◽

Dynamic Model ◽

Vegetation Index ◽

Wheat Yield ◽

Winter Wheat Yield

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The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa ◽

10.21046/2070-7401-2016-13-23-99-112 ◽

2016 ◽

Vol 13 (4) ◽

pp. 99-112 ◽

Cited By ~ 1

Author(s):

F.V. Eroshenko ◽

◽

S.A. Bartalev ◽

I.G. Storchak ◽

D.E. Plotnikov ◽

...

Keyword(s):

Remote Sensing ◽

Winter Wheat ◽

Vegetation Index ◽

Wheat Yield ◽

Remote Sensing Data ◽

Yield Estimation ◽

Sensing Data ◽

Winter Wheat Yield ◽

Photosynthetic Potential

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Analysis of Influencing Factors on Winter Wheat Yield Estimations Based on a Multisource Remote Sensing Data Fusion

Applied Engineering in Agriculture ◽

10.13031/aea.14398 ◽

2021 ◽

Vol 37 (5) ◽

pp. 991-1003

Author(s):

Yan Li ◽

Yan Zhao Ren ◽

Wan Lin Gao ◽

Sha Tao ◽

Jing Dun Jia ◽

...

Keyword(s):

Remote Sensing ◽

Winter Wheat ◽

Spatial Resolution ◽

Vegetation Index ◽

Time Window ◽

Wheat Yield ◽

Remote Sensing Data ◽

Sensing Data ◽

Winter Wheat Yield ◽

Window Selection

HighlightsThe potential of fusing GF-1 WFV and MODIS data by the ESTARFM algorithm was demonstrated.A better time window selection method for estimating yields was provided.A better vegetation index suitable for yield estimation based on spatiotemporally fused data was identified.The effect of the spatial resolution of remote sensing data on yield estimations was visualized.Abstract. The accurate estimation of crop yields is very important for crop management and food security. Although many methods have been developed based on single remote sensing data sources, advances are still needed to exploit multisource remote sensing data with higher spatial and temporal resolution. More suitable time window selection methods and vegetation indexes, both of which are critical for yield estimations, have not been fully considered. In this article, the Chinese GaoFen-1 Wide Field View (GF-1 WFV) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data were fused by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to generate time-series data with a high spatial resolution. Then, two time window selection methods involving distinguishing or not distinguishing the growth stages during the monitoring period, and three vegetation indexes, the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2) and wide dynamic range vegetation index (WDRVI), were intercompared. Furthermore, the yield estimations obtained from two different spatial resolutions of fused data and MODIS data were analyzed. The results indicate that taking the growth stage as the time window unit division basis can allow a better estimation of winter wheat yield; and that WDRVI is more suitable for yield estimations than NDVI or EVI2. This study demonstrates that the spatial resolution has a great influence on yield estimations; further, this study identifies a better time window selection method and vegetation index for improving the accuracy of yield estimations based on a multisource remote sensing data fusion. Keywords: Remote sensing, Spatiotemporal data fusion, Winter wheat, Yield estimation.

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