Kevin Dalsted

Clouds Influence Precision and Accuracy of Ground‐Based Spectroradiometers

Abstract The objectives of this study were to determine the precision and accuracy, under field conditions, of two commonly used ground‐based spectroradiometers and to propose guidance on how to minimize system errors. Sunlight irradiance and reflected radiance were measured on calibration tarps (3.6% and 52% reflectance) on 6 days using a CropScan MSR 16 handheld multispectral radiometer and a Fieldspec model FR hyperspectral radiometer during 2002. Radiance and irradiance were corrected for temperature and sun angle and converted to percent reflectance. Analysis showed that variances of the reflectance values for both radiometers increased with cloud cover. These results were attributed to several factors. First, cloud cover produced atmospheric conditions that made irradiance highly variable. Under these conditions, if reflected light is calculated by dividing radiance from target by radiance from a known standard, which is only periodically measured, then the calculated reflectance value may contain errors. Second, the reduction of diffuse irradiance by increasing cloud cover may introduce errors into reflectance calibration. Third, the relationship between incident irradiance, reflection of surface, and sensor efficiency may not be linear, and therefore, calculated reflectance can be variable when incident irradiance is variable. Results from this study showed that 1) the field measurements must be conducted under similar conditions at a similar time, 2) both sensors must be calibrated before and after measurements with reference panel, with ample time for device warm up, 3) measured reflectance should be corrected with reflectance from a reference panel, and 4) for the FieldSpec, reflectance measurements can be improved by simultaneously measuring radiance from the target and a known standard.

Assessing the Value of Using a Remote Sensing-Based Evapotranspiration Map in Site-Specific Management

ABSTRACT In the glaciated regions of the northern Great Plains, water - either too much or too little - influences soil development, carbon storage, and plant productivity. Integrating site-specific water variability information directly into management is difficult. Simulation models that employ remotely sensed data can generate hard to measure values such as evapotranspiration (ET). This information can be used to identify management zones. The objective of this study was to determine if the METRIC (Mapping Evapotranspiration at High Resolution and with Internalized Calibration) model, which uses weather station and remote sensing data can be used as a tool in site-specific management. This study was conducted on a 65 ha corn (Zea mays L.) field located in east central South Dakota. The METRIC model used Landsat 7 data collected on August 4, 2001 to calculate ET values with spatial resolution of 30 m. ET values were correlated with corn yield (r = 0.85**), apparent electrical conductivity (ECa; r = 0.71**), soil organic carbon (SOC; r = 0.32*), and pH (r = 0.28*). In the footslope positions, high ET values were associated with high corn yields, SOC, EC a , and pH values, while in the summit/shoulder areas low ET values were associated with low yields, SOC, ECa, and pH values. The strong relationship between ET and productivity was attributed to landscape processes that influenced plant available water, which in turn influenced productivity. Cluster analysis of the ET and EC data showed that these data bases complimented each other. Remote sensing-based ET data was most successful in identifying areas where water stress reduced corn yields, while ECa was most successful in identifying high yielding management zones. Findings from this study suggest that remote sensing-based ET estimates can be used to improve management zone delineation.

Detecting weed-free and weed-infested areas of a soybean field using near-infrared spectral data

Abstract Weed distribution maps can be developed from remotely sensed reflectance data if collected at appropriate times during the growing season. The research objectives were to determine if and when reflectance could be used to distinguish between weed-free and weed-infested (mixed species) areas in soybean and to determine the most useful wavebands to separate crop, weed, and soil reflectance differences. Treatments included no vegetation (bare soil), weed-free soybean, and weed-infested soybean and, in 1 yr, 80% corn residue cover. Reflectance was measured at several sampling times from May through September in 2001 and 2002 using a handheld multispectral radiometer equipped with band-limited optical interference filters (460 to 1,650 nm). The spatial resolution was 0.8 m2. The reflectance in the visible spectral range (460 to 700 nm) generally was similar among treatments. In the near-infrared (NIR) range (> 700 to 1,650 nm), differences among treatments were observed from soybean growth stage V-3 (about 4 wk after planting) until mid-July to early August depending on crop vigor and canopy closure (76-cm row spacing in 2001 and 19-cm row spacing in 2002). Reflectance rankings in the NIR range when treatments could be differentiated were consistent between years and, from lowest to highest reflectance, were soil < weed-free < weed-infested areas. Increased reflectance from weed-infested areas was most likely due to increased biomass and canopy cover. Residue masked differences between weed-free and weed-infested areas during the early stages of growth due to high reflectance from the residue and reduced weed numbers in these areas. These results suggest that NIR spectral reflectance collected before canopy closure can be used to distinguish weed-infested from weed-free areas. Nomenclature: Soybean, Glycine max (L.) Merr.

Evaluating Modified Atmospheric Correction Methods for Landsat Imagery : Image-Based and Model-Based Calibration Methods

Abstract To increase the accuracy of remotely sensed data for agricultural forecasting, pixel values must be corrected for atmospheric effects and converted to spectral reflectance. The objective of this research was to compare two atmospheric correction methods of Landsat imagery under a range of atmospheric conditions. Ground‐based dark‐object subtraction (GDOS) is an image‐based calibration method that used in situ ground data that the dark‐object subtraction (DOS) method did not use, whereas atmospheric calibration (AC) is a model‐based calibration method that required a standard atmospheric profile refined with the use of in situ atmospheric data. GDOS and AC methods improved the reflectance values and had relationships with measured bands, which were approximately 1 to 1 in all bands. However, the GDOS generally had lower root‐mean‐square errors (RMSE) than AC. Data from this study suggest that at the present time the GDOS method may be more accurate than the AC method.

Precision farming management via information dissemination

In the Northern Great Plains of the United States, growing seasons are short (80-120 days) but extremely productive. Farms and ranches are large (>1000 acres), so many of precision agricultures early adopters reside in the region. Management optimization depends on decisions taken based on past as well as in-season information. Spatial data is an ideal tool to answer both the long term and the short-term needs.

Applications of remote sensing to precision agriculture with dual economic and environmental benefits

In the U.S. Northern Great Plains, growing seasons are short but extremely productive. Farms and ranches are large, so many of precision agricultures early adopters reside in the region. Crop yield maps at seasons end reveal sizable variations across fields. Farm management relying upon uniform chemical applications is ineffective and wasteful. We provided information about crop and range status in near- real-time, so that in-season decisions could be made to optimize final yields and minimize environmental degradation. We created learning communities, in which information is shared among scientists, farmers, ranchers, and data providers. The new information for agricultural producers was satellite and aerial imagery. Value-added information was derived from ETM+, AVHRR, IKONOS, and MIDOS sensors. The emphasis was on reducing the time between acquisition of data by a satellite and delivery of value-added products to farmers and ranchers. To distribute large spatial data sets in short times to rural users we relied upon satellite transmission (Direct PC). Results include: (1) management zone delineation, (2) variable-rate fertilizer applications, (3) weed detection, (4) irrigation efficiency determination, (5) detection of insect infestation, (6) specification of crop damage due to inadvertent chemical application, and (7) determination of livestock carrying capabilities on rangelands.

Providing Precision Crop and Range Protection in the US Northern Great Plains

Faculty, students, and staff from eight universities in the U.S. Northern Great Plains formed the Upper Midwest Aerospace Consortium (UMAC ) to lead a regional transition to sustainability . One major focus was on agriculture, an important part of the region’s economy and social structure. By forming a learning community in concert with farmers and ranchers, UMAC has made information an asset as valuable as land, labor, and capital. One primary source of information combined with traditional sources is remotely sensed imagery. UMAC has created an end-to-end operation, starting with data acquisition by airborne and orbiting sensors customized to acquire data needed to meet producer demands, proceeding to development of value-added products, and finally making them readily accessible on the WWW to non-expert users whom we also train. A specific example of the operation in action illustrates the economic and environmental benefits that result.