Pre-harvest state level wheat acreage estimation using IRS-IA LISS-I data in Punjab (India)

Mahey, R. K.; Singh, R. P.; Sidhu, S. S.; Narang, R. S.; Dadhwal, V. K.; Parihar, J. S.; Sharma, A. K.

doi:10.1080/01431169308904398

Cited by 17 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the seasonal maximum NDVI and EVI were selected as this enabled timely forecasting of crop production around a month and half prior to harvest. These results are in line with previous studies that found similar results with either crop production or yield (Becker-Reshef et al 2010;Doraiswamy and Cook 1995;Mahey et al 1993;Tucker 1980). In terms of comparison amongst the employed indices, it is apparent from statistical evaluation of the results that NDVI could have an advantage relative to the EVI and NPP for forecasting crop production in the region using the leave-one-year-out approach.…”

Section: Discussionsupporting

confidence: 93%

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq

Qader

Dash

Atkinson

2018

Science of The Total Environment

View full text Add to dashboard Cite

Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R=0.70 compared to the date of MODIS EVI (Avg R=0.68) and a NPP (Avg R=0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from -20 to 20%, -45 to 28% and -48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach.

show abstract

Section: Discussionsupporting

confidence: 93%

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq

Qader

Dash

Atkinson

2018

Science of The Total Environment

View full text Add to dashboard Cite

show abstract

“…The same general patterns are found in the North Dakota data and our small plot experiment thus suggesting that NDYI is relatively better than NDVI for modeling canola seed yield. NDVI has been shown to work best for estimating wheat yield either at maximum green leaf biomass (Tucker, et al, 1980) or maximum green canopy cover (Mahey et al, 1993). Similar to how NDVI performs during maximum greenness, NDYI performs best during maximum yellow flower cover.…”

Section: Discussionmentioning

confidence: 99%

Spectral considerations for modeling yield of canola

Sulik

Long

2016

Remote Sensing of Environment

111

View full text Add to dashboard Cite

Prominent yellow flowers that are present in a Brassica oilseed crop such as canola require careful consideration when selecting a spectral index for yield estimation. This study evaluated spectral indices for multispectral sensors that correlate with the seed yield of Brassica oilseed crops. A small-plot experiment was conducted near Pendleton, Oregon in which spring canola was grown under varying water regimes and nitrogen treatments to create a wide range in oilseed yield. Plot measurements consisted of canopy reflectance at flowering using a hand-held spectroradiometer and seed yield at physiological maturity. Spectroradiometric measurements were converted to MODIS band equivalent reflectance. Selected indices were computed from spectra obtained with the radiometer and correlated with seed yield. A normalized difference yellowness index (NDYI), computed from the green and blue wavebands, overcame limitations of the normalized difference vegetation index (NDVI) during flowering and best modeled variability in relative yield potential. NDYI was more linear and correlated with county-wide oilseed yield data and MODIS satellite data from North Dakota (r 2 ≤ 0.72) than NDVI (r 2 ≤ 0.66). NDYI only requires wavebands in the visible region of the spectrum and can be applied to any satellite or aerial sensor that has blue and green channels. These findings highlight the benefit of using a spectral index that is sensitive to reproductive growth of vegetation instead of vegetative growth for crops with spectrally prominent reproductive canopy elements. Our results indicate that NDYI is a better indicator of yield potential than NDVI during mid-season development stages, especially peak flowering.

show abstract

“…Pioneering work carried out in this field, such as by Fischer (1975), found that wheat yields could be forecasted as a function of the leaf area at the onset of the reproductive stage, which corresponds to the timing of maximum crop green leaf area. In the case of wheat, studies have found a strong correlation between the peak of the Normalized Different Vegetation Index (NDVI, Rouse, 1974), which corresponds closely to the reproductive stage, and final wheat yields (Groten, 1993;Mahey et al, 1993;Rasmussen, 1992;Smith, Adams, Stephens, & Hick, 1995;Tucker, Holben, Elgin, & McMurtrey, 1980). Nevertheless, one of the challenges in crop forecasting over large areas, such as at the state or national scale using remote sensing data, is the variability in climatic zones, which can result in different timing of crop development.…”

Section: Introductionmentioning

confidence: 96%

Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information

Franch

Vermote

Becker‐Reshef

et al. 2015

Remote Sensing of Environment

162

View full text Add to dashboard Cite

Pre-harvest state level wheat acreage estimation using IRS-IA LISS-I data in Punjab (India)

Cited by 17 publications

References 4 publications

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq

Spectral considerations for modeling yield of canola

Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information

Contact Info

Product

Resources

About