Michael L. Whiting

Evaluated Crop Evapotranspiration over a Region of Irrigated Orchards with the Improved ACASA–WRF Model

AbstractAmong the uncertain consequences of climate change on agriculture are changes in timing and quantity of precipitation together with predicted higher temperatures and changes in length of growing season. The understanding of how these uncertainties will affect water use in semiarid irrigated agricultural regions depends on accurate simulations of the terrestrial water cycle and, especially, evapotranspiration. The authors test the hypothesis that the vertical canopy structure, coupled with horizontal variation in this vertical structure, which is associated with ecosystem type, has a strong impact on landscape evapotranspiration. The practical result of this hypothesis, if true, is validation that coupling the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) and the Weather Research and Forecasting (WRF) models provides a method for increased accuracy of regional evapotranspiration estimates.ACASA–WRF was used to simulate regional evapotranspiration from irrigated almond orchards over an entire gr...

Remotely sensed estimates of crop water demand

In water limited environments, the density and water content of plant canopies are highly correlated to available soil moisture. Specific absorption bands for liquid water are identifiable and the variation in their depths can be related to canopy water content using high spectral resolution (hyperspectral) imagery. The spectral absorption feature centered at approximately 980 nm has been widely utilized for estimating equivalent water thickness, a measure of the volume of canopy water if it is equally distributed over the area of the pixel. Although it is affected by canopy structure, it is highly correlated with plant water content, and is independent of reflectance changes due to photosynthetic pigments. This study relates the depth of the 980 nm water band absorption, measured by the continuum removal (CR) technique, to crop water stress, and compares these results to other vegetation and plant stress indicators, NDVI and NDWI.

Remote detection of water stress in orchard canopies using MODIS/ASTER airborne simulator (MASTER) data

Vegetation canopy water content (CWC) is an important parameter for monitoring natural and agricultural ecosystems. Previous studies focused on the observation of annual or monthly variations in CWC but lacked temporal details to study vegetation physiological activities within a diurnal cycle. This study provides an evaluation of detecting vegetation diurnal water stress using airborne data acquired with the MASTER instrument. Concurrent with the morning and afternoon acquisitions of MASTER data, an extensive field campaign was conducted over almond and pistachio orchards in southern San Joaquin Valley of California to collect CWC measurements. Statistical analysis of the field measurements indicated a significant decrease of CWC from morning to afternoon. Field measured CWC was linearly correlated to the normalized difference infrared index (NDII) calculated with atmospherically corrected MASTER reflectance data using either FLAASH or empirical line (EL). Our regression analysis demonstrated that both atmospheric corrections led to a root mean square error (RMSE) of approximately 0.035 kg/m2 for the estimation of CWC (R2=0.42 for FLAASH images and R2=0.45 for EL images). Remote detection of the subtle decline in CWC awaits an improved prediction of CWC. Diurnal CWC maps revealed the spatial patterns of vegetation water status in response to variations in irrigation treatment.

Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images

Quick and low cost delineation of site-specific management zones (SSMZ) would improve applications of precision agriculture. In this study, a new method for delineating SSMZ using object-oriented segmentation of airborne imagery was demonstrated. Three remote sensing domains—spectral, spatial, and temporal- are exploited to improve the SSMZ relationship to yield. Common vegetation indices (VI), and first and second derivatives (

Using Imaging Spectroscopy to study soil properties.

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Temporal and Spatial Relationships between Within-Field Yield Variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery

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Remote Sensing Based Assessment of Biophysical Indicators for Land Degradation and Desertification

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