A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

Abstract

a b s t r a c tAn exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250 m spatial resolution while the other products were 1000 m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250 m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250 m vs. 1000 m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250 m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008-2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000 m resolution NDVI showed somewhat poorer performance than the 250 m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.Published by Elsevier B.V.

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

“…The correlation between yield and GCI/NDWI/NIR is between 0.72-0.74 from mid-July to mid-August, attributed to their high sensitivity to canopy green leaf volume, which directly contributes to grain growth. The negative correlations between NIR/VIs and yield (positive for the visible and SWIR bands) in April and May are consistent with previous studies (Johnson 2016) and related to the increasing trend of yield from low (earlier planting dates) to high latitudes (later planting dates). Heat stress variables, especially daytime LST, maximum and mean VPD, and maximum daily air temperature, have strong negative correlations with yield in summer consistent with previous findings (Lobell et al 2013).…”

A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

“…These results, in terms of both correlations and timing, are in line with Mahey et al [53]. Freeman et al [54] found NDVI and wheat grain yield to be highly correlated, establishing the potential to predict yield with remotely sensed data as reported subsequently in several studies for a variety of crop types [55][56][57][58].…”

“…Although negative correlations between NDVI and crop yield are reported in the literature for potato late in the season [55] and for canola, after bolting and once the plants start transitioning to the reproductive stages [59], there are few similar findings for cereal crops when analyzing single or multi cultivars [60][61][62][63]. All these latter authors found a negative correlation under severe stress conditions, such as high temperature and drought, during grain filling.…”

Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

“…Knowing geographical distribution of given crops can help optimize available resources, when performing large scale ground observations (Song et al, 2017). For instance, early season crop masks are required to provide crop yield prediction and, consequently, crop production forecasting in the operational context which is important for food security (Becker-Reshef, Vermote, Lindeman, & Justice, 2010;Franch et al, 2015;Johnson, 2016;Kogan et al, 2013;López-Lozano et al, 2015;Shao, Campbell, Taff, & Zheng, 2015). Crop maps can be incorporated into the drought risk assessment models to quantify and map the risk at different scales (Skakun, Kussul, Shelestov, & Kussul, 2016a).…”