Monitoring barley and corn growth from remote sensing data at field scale

Calera, Alfonso; González-Piqueras, José; Meliá, J.

doi:10.1080/0143116031000115319

Cited by 58 publications

(37 citation statements)

References 12 publications

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“…Using these LUE models we can consider that, under non-water stress, NDVI on plateau stage can be seen as a good estimator of the dry matter accumulation rate, depending on crop and environmental variables [13].…”

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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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

View full text Add to dashboard Cite

“…The SAVI index is considered less sensitive to soil background reflectance variability than other VI, thus with higher potential for application in tree crops with discontinuous ground cover. NDVImax was set to 0.75-0.85 for the three crops, and the value 0.10 was set for NDVImin, both based on reference values for, respectively, an irrigated agricultural field with full cover and a bare soil [48][49][50]. When SAVI was considered, the SAVImin was set to 0.09 while the SAVImax was adjusted to 0.75 according to Mateos et al [7].…”

Section: Basal Crop Coefficients Derived From Reflectance Vegetation mentioning

confidence: 99%

“…Both NDVI and SAVI may be used, with the latter producing better results for incomplete cover crops, e.g., olive orchards. NDVImax and NDVImin were set according to references in the literature [48][49][50] (Table 4). For the super high density olive orchard, SAVImax and SAVImin were considered following values proposed by Mateos et al [7] (Table 4).…”

Section: Basal Crop Coefficients Derived From Reflectance Vegetation mentioning

confidence: 99%

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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“…NDVI connects intimately with biophysical parameters such as chlorophyll concentration, primary productivity, and green biomass for a variety of ecosystem types (Calera et al, 2004;Wang et al, 2004;Liang et al, 2005). We adopted NDVI as the indicator of freeze injury to monitor the damage by means of remote sensing.…”

Section: Freeze Injury Indicator Determinationmentioning

confidence: 99%

Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data

Shu

Huang

Guo

et al. 2015

J. Zhejiang Univ. Sci. B

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Abstract:The winter oilseed rape (Brassica napus L.) accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010-2011 growing season. The normalized difference vegetation index (NDVI) was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury: terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree (P<0.01, two-tailed). The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south-and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature (LST) of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.

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Monitoring barley and corn growth from remote sensing data at field scale

Cited by 58 publications

References 12 publications

Remote Sensing Based Crop Coefficients for Water Management in Agriculture

Remote Sensing Based Crop Coefficients for Water Management in Agriculture

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

Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data

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