Soil background effects on reflectance-based crop coefficients for corn

Bausch, Walter C.

doi:10.1016/0034-4257(93)90096-g

Cited by 174 publications

(107 citation statements)

References 19 publications

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“…The regression coefficient obtained when comparing Kcb NDVI with Kcb SIMDualKc is much better (b = 0.98; Table 5), which may be related with the tendency of NDVI reaching a maximum value at about the same time as Kcb, i.e., at a LAI around 3 [14,59]. Contrarily, SAVI tends to continue increasing for LAI above 3 [21,25,60]. Thus, according to Allen et al [21], the NDVI supports better than SAVI the estimation of Kc and Kcb in crops reaching high LAI values as for the full cover crops considered in the current study.…”

Section: Estimation Of Actual Kcb From Reflectance-based Vegetation Imentioning

confidence: 88%

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

et al. 2015

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Abstract:A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (Kcb VI) considering a density coefficient (Kd) and a crop coefficient for bare soil. Kd is computed using the fraction of ground cover by vegetation (fc VI), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (Kc VI) is also proposed by integrating the Kcb VI with the soil evaporation coefficient (Ke) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both fc VI and Kcb VI compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual Kcb VI and Kc VI with actual Kcb and Kc obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management. OPEN ACCESSRemote Sens. 2015, 7 2374

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Section: Estimation Of Actual Kcb From Reflectance-based Vegetation Imentioning

confidence: 88%

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

et al. 2015

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“…Such study might explore the use of alternative vegetation indices that are less influenced by soil background such as the Soil Adjusted Vegetation Index and related transforms [34,35]. Among other things however, potential use of such indices in an operational context should be evaluated for cloud shadow sensitivity, where NDVI retrievals are fairly robust.…”

Section: Discussionmentioning

confidence: 99%

Satellite NDVI Assisted Monitoring of Vegetable Crop Evapotranspiration in California’s San Joaquin Valley

2012

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Abstract:Reflective bands of Landsat-5 Thematic Mapper satellite imagery were used to facilitate the estimation of basal crop evapotranspiration (ETcb), or potential crop water use, in San Joaquin Valley fields during 2008. A ground-based digital camera measured green fractional cover (Fc) of 49 commercial fields planted to 18 different crop types (row crops, grains, orchard, vineyard) of varying maturity over 11 Landsat overpass dates. Landsat L1T terrain-corrected images were transformed to surface reflectance and converted to normalized difference vegetation index (NDVI). A strong linear relationship between NDVI and Fc was observed (r 2 = 0.96, RMSE = 0.062). The resulting regression equation was used to estimate Fc for crop cycles of broccoli, bellpepper, head lettuce, and garlic on nominal 7-9 day intervals for several study fields. Prior relationships developed by weighing lysimeter were used to transform Fc to fraction of reference evapotranspiration, also known as basal crop coefficient (Kcb). Measurements of grass reference evapotranspiration from the California Irrigation Management Information System were then used to calculate ETcb for each overpass date. Temporal profiles of Fc, Kcb, and ETcb were thus developed for the study fields, along with estimates of seasonal water use. Daily ETcb retrieval uncertainty resulting from error in satellite-based Fc estimation was <0.5 mm/d, with seasonal uncertainty of 6-10%. Results were compared with FAO-56 irrigation guidelines and prior lysimeter observations for reference. OPEN ACCESSRemote Sens. 2012, 4 440

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“…In order to minimize the presence of soil background, other vegetation indices (VI) have been used to compute Kcb [23]. This provides a particularly useful tool for satellite images where soil brightness and colour can vary.…”

Section: Crop Coefficient From Ndvi and Canopy Biophysics Parametersmentioning

confidence: 99%

Remote Sensing Based Crop Coefficients for Water Management in Agriculture

Rocha¹,

Perdigão²,

Melo³

et al. 2012

Sustainable Development - Authoritative and Leading Edge Content for Environmental Management

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Soil background effects on reflectance-based crop coefficients for corn

Cited by 174 publications

References 19 publications

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

Satellite NDVI Assisted Monitoring of Vegetable Crop Evapotranspiration in California’s San Joaquin Valley

Remote Sensing Based Crop Coefficients for Water Management in Agriculture

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