Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the camera-UAV system may provide important information for site-specific agriculture.

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r2=0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r2=0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops

Winter cover crops are recognized as an important agricultural conservation practice for reducing nitrogen (N) losses to groundwater following the summer growing season. Accordingly, cost-share programs have been established to promote winter cover crops for water quality on farms throughout the Chesapeake Bay watershed. However, current estimates of cover crop nutrient uptake are largely calculated from plot-scale studies extrapolated to watershed-scale based solely on enrollment acreage. Remote sensing provides a tool for rapid estimation of cover crop biomass production on working farms throughout the landscape. This project combined cost-share program enrollment data with satellite imagery and on-farm sampling to evaluate cover crop N uptake on 136 fields within the Choptank River watershed, on Maryland's eastern shore. The Normalized Difference Vegetation Index was a successful predictor of aboveground biomass for fields with >210 kg ha⁻¹ (>187 lb ac⁻¹) of vegetation (corresponding to 4.2 kg ha⁻¹ [3.7 lb ac⁻¹] of plant N), below which the background reflectance of soils and crop residues obstructed the cover crop signal. Cover crops planted in the two weeks prior to the regional average first frost date (October 15) exhibited average fall aboveground N uptake rates of 18, 13, and 5 kg ha⁻¹ (16, 12, 4 lb ac⁻¹) for rye, barley, and wheat, respectively, corresponding to 1,260, 725, and 311 kg ha⁻¹ (1,124, 647, 277 lb ac⁻¹) of aboveground biomass, with associated cost-share implementation costs of $5.49, $7.60, and $19.77 kg⁻¹ N ($2.50, $3.46, and $8.99 lb⁻¹ N). Cover crops planted after October 15 exhibited significantly reduced biomass and nutrient uptake, with associated program costs of $15.44 to $20.59 kg⁻¹ N ($7.02 to $9.36 lb⁻¹ N). Agronomic factors influencing cover crop performance included species, planting date, planting method, and previous crop. Field sampling locations with >1,000 kg ha⁻¹ (>890 lb ac⁻¹) of springtime cover crop biomass exhibited greatly reduced soil nitrate (<3 mg kg⁻¹ [<3 ppm]) in comparison to fields with low cover crop biomass (up to 14 mg kg⁻¹ soil nitrate), indicating a target biomass threshold for maximum water quality impact. Additional sampling years will be necessary to account for cover crop response to climate variability. Combining remote sensing with farm program data can provide important information to scientists and regulators working to improve conservation programs. Results can be used to more effectively utilize scarce conservation resources and increase water quality protection.

Crop residues serve many important functions in agricultural conservation including preserving soil moisture, building soil organic carbon, and preventing erosion. Percent crop residue cover on a field surface reflects the outcome of tillage intensity and crop management practices. Previous studies using proximal hyperspectral remote sensing have demonstrated accurate measurement of percent residue cover using residue indices that characterize cellulose and lignin absorption features found between 2100 nm and 2300 nm in the shortwave infrared (SWIR) region of the electromagnetic spectrum. The 2014 launch of the WorldView-3 (WV3) satellite has now provided a space-borne platform for the collection of narrow band SWIR reflectance imagery capable of measuring these cellulose and lignin absorption features. In this study, WorldView-3 SWIR imagery (14 May 2015) was acquired over farmland on the Eastern Shore of Chesapeake Bay (Maryland, USA), was converted to surface reflectance, and eight different SWIR reflectance indices were calculated. On-farm photographic sampling was used to measure percent residue cover at a total of 174 locations in 10 agricultural fields, ranging from plow-till to continuous no-till management, and these in situ measurements were used to develop percent residue cover prediction models from the SWIR indices using both polynomial and linear least squares regressions. Analysis was limited to agricultural fields with minimal green vegetation (Normalized Difference Vegetation Index < 0.3) due to expected interference of vegetation with the SWIR indices. In the resulting residue prediction models, spectrally narrow residue indices including the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Lignin Cellulose Absorption Index (LCA) were determined to be more accurate than spectrally broad Landsat-compatible indices such as the Normalized Difference Tillage Index (NDTI), as determined by respective R² values of 0.94, 0.92, and 0.84 and respective residual mean squared errors (RMSE) of 7.15, 8.40, and 12.00. Additionally, SINDRI and LCA were more resistant to interference from low levels of green vegetation. The model with the highest correlation (2nd order polynomial SINDRI, R² = 0.94) was used to convert the SWIR imagery into a map of crop residue cover for non-vegetated agricultural fields throughout the imagery extent, describing the distribution of tillage intensity within the farm landscape. WorldView-3 satellite imagery provides spectrally narrow SWIR reflectance measurements that show utility for a robust mapping of crop residue cover.