Analysis of Soil Variability with Repetitive Aerial Photography

Milfred, C. J.; Kiefer, R. W.

doi:10.2136/sssaj1976.03615995004000040028x

Cited by 15 publications

(5 citation statements)

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“…Soil water and organic matter content have been reported in the literature to be inversely related to both visible and NIR soil reflectance. Correlation of water content to visible and NIR reflectance of bare-soil fields was obtained when the data were taken a few days after rainfall (Milfred and Kiefer, 1976). Lobell and Asner (2002) and Weidong et al (2002) found a non-linear dependence of water content to soil reflectance at various NIR bands of the spectrum.…”

Section: Resultsmentioning

confidence: 99%

Ground-Sensor Soil Reflectance as Related to Soil Properties and Crop Response in a Cotton Field

et al. 2005

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Bare soil reflectance from airborne imagery or laboratory spectrometers has been used to infer soil properties such as soil texture, organic matter, water content, salinity and crop residue cover. However, the relation of soil properties to reflectance data often varies with soil type and conditions and surface reflectance may not be representative of the conditions in the root zone. The objectives of this study were to assess the soil reflectance data obtained by ground-based sensors and to model soil properties in the root zone as a function of surface soil reflectance and plant response. Ground-based sensors were used to simultaneously monitor soil and canopy reflectance in the visible and near-infrared (VNIR) along six rows and in two growth stages in a 7 ha cotton field. The reflectance data were compared to soil properties, leaf nutrients and biomass measured at 33 sampling positions along the rows. Brightness values of the blue and green bands of soil reflectance were better correlated to soil water content, particulate organic matter and extractable potassium and phosphorus, while those in the red and NIR bands were correlated to soil carbonate content, total nitrogen, electrical conductivity and foliar nutrients. The correlation of red soil reflectance with canopy reflectance was significant and indicated an indirect inverse relationship between soil fertility and plant stress. The integration of surface soil reflectance and plant response variables in a multiple regression model did not substantially improve the prediction of soil properties in the root zone. However, crop nutrient status explained a significant portion of the spatial variability of soil properties related to nitrification processes when soil reflectance did not. The implication of these findings to agricultural management is discussed.

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Section: Resultsmentioning

confidence: 99%

Ground-Sensor Soil Reflectance as Related to Soil Properties and Crop Response in a Cotton Field

et al. 2005

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“…The relationship between reflectance in different bands of the electromagnetic radiation spectrum and surface soil moisture has been studied extensively (e.g., Gao et al, ; Lobell & Asner, ). Several studies have demonstrated that reflectance in the solar spectrum domain decreases with soil moisture increase, and this property has been used to estimate soil moisture (Ahmad, Zhang, & Nichols, ; Ghulam, Qin, Teyip, & Li, ; Jensen, ; Milfred & Kiefer, ; Weidong et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Several studies have demonstrated that reflectance in the solar spectrum domain decreases with soil moisture increase, and this property has been used to estimate soil moisture (Ahmad, Zhang, & Nichols, 2011;Ghulam, Qin, Teyip, & Li, 2007;Jensen, 2007;Milfred & Kiefer, 1976;Weidong et al, 2002).…”

Section: Study Areamentioning

confidence: 99%

Surface moisture area during rainfall–run‐off events to understand the hydrological dynamics of a basin in a plain region

Ares

Holzman

Entraigas

et al. 2018

Hydrological Processes

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The understanding of the hydrology of plain basins may be improved by the combined analysis of rainfall–run‐off records and remote sensed surface moisture data. Our work evaluates the surface moisture area (SMA) produced during rainfall–run‐off events in a plain watershed of the Argentine Pampas Region, and studies which hydrological variables are related to the generated SMA. The study area is located in the upper and middle basins of the Del Azul stream, characterized by the presence of small gently hilly areas surrounded by flat landscapes. Data from 9 rainfall–run‐off events were analysed. MODIS surface reflectance data were processed to calculate SMA subsequent to the peak discharge (post‐SMA), and previous to the rainfall events (prev‐SMA), to consider the antecedent wetness. Rainfall–run‐off data included total precipitation depth (P), maximum intensity of rainfall over 6 hr (I6max), surface run‐off registered between the beginning of the event and the day previous to the analysed MODIS scene (R), peak flow (Qp), and flood intensity (IF). In contrast with other works, post‐SMA showed a negative relationship with the R. Three groups of cases were identified: (a) Events of low I6max, high prev‐SMA, and low R were associated with slow and weakly channelized flow over plain areas, leading to saturated overland flow (SOF), with large SMA; (b) events of high I6max, low prev‐SMA, and medium to high R were rapidly transported along the gentle slopes of the basin, related to Hortonian overland flow (HOF) and low post‐SMA; and (c) events of medium to high I6max and prev‐SMA with medium R were related to heterogeneous input‐antecedent‐run‐off conditions combined: Local spatial conditions may have produced HOF or SOF, leading to an averaged response with medium SMA. The interactions between the geomorphology of the basin, the characteristics of the events, and the antecedent conditions may explain the obtained results. This analysis is relevant for the general knowledge of the hydrology of large plains, whose functioning studies are still in their early stages.

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“…The approach is limited when vegetation is present and is often only sensitive to conditions at the surface (~5 to 20 cm depth); however, use of different bands and integrating the data with soil-water balance models have shown that microwave data can be useful in mapping soil moisture conditions. Soil moisture has been correlated to visible and NIR reflectance of bare-soil fields if the data are taken a few days after rainfall (Milfred and Kiefer, 1976). Similarly, thermal imagery has also been related to differences in surface soil moisture content (Davidoff and Selim, 1988).…”

Section: Moisturementioning

confidence: 99%

Remote- and Ground-Based Sensor Techniques to Map Soil Properties

Barnes¹,

Sudduth²,

Hummel³

et al. 2003

photogramm eng remote sensing

155

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Farm managers are becoming increasingly aware of the spatial variability in crop production with the growing availability of yield monitors. Often this variability can be related to differences in soil properties (e.g., texture, organic matter, salinity levels, and nutrient status) within the field. To develop management approaches to address this variability, high spatial resolution soil property maps are often needed. Some soil properties have been related directly to a soil spectral response, or inferred based on remotely sensed measurements of crop canopies, including soil texture, nitrogen level, organic matter content, and salinity status. While many studies have obtained promising results, several interfering factors can limit approaches solely based on spectral response, including tillage conditions and crop residue. A number of different ground-based sensors have been used to rapidly assess soil properties "on the go" (e.g., sensor mounted on a tractor and data mapped with coincident position information) and the data from these sensors compliment image-based data. On-the-go sensors have been developed to rapidly map soil organic matter content, electrical conductivity, nitrate content, and compaction. Model and statistical methods show promise to integrate these groundand image-based data sources to maximize the information from each source for soil property mapping.

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Analysis of Soil Variability with Repetitive Aerial Photography

Cited by 15 publications

References 0 publications

Ground-Sensor Soil Reflectance as Related to Soil Properties and Crop Response in a Cotton Field

Ground-Sensor Soil Reflectance as Related to Soil Properties and Crop Response in a Cotton Field

Surface moisture area during rainfall–run‐off events to understand the hydrological dynamics of a basin in a plain region

Remote- and Ground-Based Sensor Techniques to Map Soil Properties

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