Crop Yield Assessment from Remote Sensing

Doraiswamy, P. C.; Moulin, Sophie; Cook, Paul W.; Stern, Alan J.

doi:10.14358/pers.69.6.665

Cited by 248 publications

(127 citation statements)

References 24 publications

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“…Because agricultural productivity directly influences food prices, trade and greenhouse-gas emission policies, and human livelihood [1,2], accurate and timely yield estimation is of primary interest to the scientific community and governments [3]. Compared with traditional ground-based methods, such as visual examination and survey sampling, remote sensing that provides synoptic and repetitive observations of the land surface is well suited for agricultural mapping [4][5][6] and monitoring [7,8] large geographic areas.…”

Section: Introductionmentioning

confidence: 99%

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Xin

Gong

et al. 2013

Remote Sensing

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Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.

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Section: Introductionmentioning

confidence: 99%

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Xin

Gong

et al. 2013

Remote Sensing

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“…Routine methods have been developed to assess crop biomass and yield on a large spatial scale (Metternicht and Zinck, 2003;Smith et al, 1995, Doraiswamy et al, 2003. With the growing availability of satellite archives, these applications are increasing (Pettorelli et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater

Kobryn

Lantzke

Bell

et al. 2015

Journal of Environmental Management

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Kobryn, H.T., Lantzke, R., Bell, R. and Admiraal, R. (2015)Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater. AbstractThe installation of deep drains is an engineering approach to remediate land salinised by the influence of shallow groundwater. It is a costly treatment and its economic viability is, in part, dependent on the lateral extent to which the drain increases biological productivity by lowering water tables and soil salinity (referred to as the drains' zone of benefit). Such zones may be determined by assessing the biological productivity response of adjacent vegetation over time. We tested a multi-temporal satellite remote sensing method to analyse temporal and spatial changes in vegetation condition surrounding deep drainage sites at five locations in the Western Australian wheatbelt affected by dryland salinity-Morawa, Pithara, Beacon, Narembeen and Dumbleyung. Vegetation condition as a surrogate for biological productivity was assessed by Normalised Difference Vegetation Index (NDVI) during the peak growing season. Analysis was at the site scale within a 1000 m buffer zone from the drains. There was clear evidence of NDVI increasing with elevation, slope and distance from the drain. After accounting for elevation, slope and distance from the drain, there was a significant increase in NDVI across the five locations after installation of deep drains. Changes in NDVI after drainage were broadly consistent with measured changes at each site in groundwater levels after installation of the deep drains. However, this study assessed the lateral extent of benefit for biological productivity and gave a measure of the area of benefit along the entire length of the drain. The method demonstrated the utility of spring NDVI images for rapid and relatively simple assessment of the change in site condition after implementation of drainage, but approaches for further improvement of the procedure were identified.

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“…Two important data sets for determining agricultural yields from remote sensing are crop type and leaf area index (LAI) (Maas 1988;Doraiswamy et al 2003Doraiswamy et al , 2004. The United States Department of Agriculture National Agricultural Statistics Service (USDA-NASS) have been producing the cropland data layer (CDL) annually for crop type starting with North Dakota in 1998 (Johnson and Mueller 2010;Boryan et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…The United States Department of Agriculture National Agricultural Statistics Service (USDA-NASS) have been producing the cropland data layer (CDL) annually for crop type starting with North Dakota in 1998 (Johnson and Mueller 2010;Boryan et al 2011). In crop models, LAI may be used either as an input or as a validation measure (Maas 1988;Doraiswamy et al 2003Doraiswamy et al , 2004Doraiswamy et al , 2005.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different MODIS data product collections over an agricultural area

Stern

Doraiswamy

Hunt

2014

Remote Sensing Letters

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Standard data products from NASA's moderate resolution imaging spectroradiometer (MODIS) were available at launch (collection 3) and have undergone two revisions (collections 4 and 5) during the continuing Terra and Aqua missions. In 2000, a research project was conducted in large fields of corn and soybean to evaluate MODIS leaf area index (LAI) and land cover type (MOD15 and MOD12 data products, respectively) as input to crop yield models. Our objective was to compare collections 3, 4 and 5 with the ground data to determine data product improvement. Classification of land cover type for collections 3, 4 and 5 were similar to the USDA-NASS Cropland Data Layer. The collection 5 MOD15 LAI was considerably improved over earlier collections when the quality assurance flags indicated good LAI retrievals. Land surface reflectances (MOD09) of MODIS band 2 (near-infrared, 250-m) were used as inputs to an inversion of the scattering by arbitrarily inclined leaves (SAIL) model. Compared to the collection 5 MOD15 product, SAIL-derived LAI had approximately equal agreement with the field data and had less systematic bias in root mean square error (RMSE). MODIS data products were designed to address global scientific questions without reliance on ground data, whereas the SAIL model inversions required ground data for model inputs. The accuracy of MODIS observations was not limiting LAI accuracy for broadleaf crops; additional information from non-MODIS sources may be required for improved MOD15 LAI.

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Crop Yield Assessment from Remote Sensing

Cited by 248 publications

References 24 publications

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater

Comparison of different MODIS data product collections over an agricultural area

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