Unmanned aerial vehicle–based assessment of cover crop biomass and nitrogen uptake variability

Abstract

Cover crops have the potential to reduce nitrate (NO 3) losses while improving soil quality, yet achieving uniform cover crop establishment can be challenging in the US Midwest. Understanding the variability of cover crop biomass and nitrogen (N) uptake at the fieldscale is an important step in determining potential effects on the following cash crop and benefits to water quality, but efficient and nondestructive methods are lacking. The objective of this study was to evaluate a lightweight unmanned aerial vehicle (UAV) mounted with a multispectral sensor in estimating cover crop (grass species) biomass and N uptake prior to cover crop termination at four commercial fields in Illinois during the 2017 growing season. Two fields had triticale (× Triticosecale Wittmack) before corn (Zea mays L.) and two had cereal rye (Secale cereale L.) before soybean (Glycine max [L.] Merr.). Forty ground-truth biomass samples (1 m 2) were collected on the day of each UAV flight across each field. Linear relationships were established between cover crop biomass and N uptake and four vegetation indices (VIs; Normalized Difference Vegetation Index [NDVI], Green Ratio Vegetation Index [GRVI], Green Normalized Difference Vegetation Index [GNDVI], and Triangular Vegetation Index [TVI]). The four VIs performed similarly in estimating cover crop biomass and N uptake (R 2 range, 0.42 to 0.93; RMSE range, 9.4% to 27.2% of the range of biomass or N uptake). A high degree of within-field variability for NDVI was observed at all fields, with biomass and N uptake at soybean fields ranging from 0 to 1,790 kg ha-1 and 0 to 48.5 kg ha-1 , respectively, and at corn fields 0 to 840 kg ha-1 and 0 to 31.5 kg ha-1 , respectively. Because cover crop biomass is often estimated based on hand samples, we also simulated the effects of biomass sampling number (2, 5, 10, or 15) on the probability of reaching different accuracy levels for estimating field means for different field sizes. Under the growing conditions of this single study year and relatively modest biomass accumulation (<1,400 kg ha-1), results from this preliminary study provide evidence that UAVs are a viable technique to obtain relatively rapid, nondestructive estimates of biomass and N uptake of two grass cover crops at vegetative stage prior to termination at the field-scale in the US Midwest. This approach could help effectively utilize scarce conservation resources, but further work is needed to evaluate other cover crop species under a wider range of growth conditions.

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

“…The choice of parameter(s) derived from UAS imagery is likely the most important factor influencing the accuracy and predictive ability of AGB estimation. Some studies used spectral information [2,11,18,24,26,43,58,[60][61][62] and some structural information [1,8,22,23,28,34,48,50,55,[63][64][65]. Others used both [3][4][5][6]9,12,16,20,21,25,30,33,49,57,[66][67][68], while a few studies used spectral and structural metrics plus another data type [13,27,69] (Table A1).…”

“…We found seven studies [11,18,24,43,58,61,62] that used MS or hyperspectral (HS) data alone and seven studies that combined MS or HS data with structural data [5,6,16,20,21,57,67] to measure AGB (Table A1). All MS or HS data and their calculation formulas used by studies considered in this review are shown in Table 2.…”

“…Rather than using the DNs from single MS bands, though, most of the research papers used NIR data in vegetation indices combining two or more wavelengths of light. Yuan et al [11] tested four NIR-based VIs for their ability to estimate cover crop biomass and found that good to excellent model accuracy could be achieved with all four VIs, although R 2 values and which VI was most important varied among the four fields studied (R 2 = 0.60-0.92). Zheng et al [26] used RGB, red edge and NIR indices along with textural data to estimate AGB and found that the NIR data outperformed the other two VI types at all growth stages of rice (max R 2 = 0.86).…”

“…In agricultural systems, AGB is a key agro-ecological indicator [4,10] that can be used to monitor crop growth, light use efficiency, carbon stock and physiological condition [3,5,6,[11][12][13][14][15][16]; predict crop yield and ensure yield quality [2,3,5,[12][13][14][17][18][19][20][21][22][23]; inform precision agriculture practices [3,15,[21][22][23]; maximize efficiency of fertilization and watering [4,10,14,21]; detect growth differences among phenotypes or cultivars [10,23]; calculate nitrogen content and assess nutrient status of plants [24][25][26]; and optimize economic decision-making throughout the growing season [3,14,15,19,22,27].…”

“…This makes UAS missions both reliable and repeatable, an advantage when there is a desire to survey the same area more than once [2,11,16,25]. UASs are also relatively inexpensive compared to other high-resolution remote sensing platforms [1,5,6,12,30,[47][48][49][50], flexible in terms of operating conditions [5,22,43], compatible with a variety of sensors [1,5,6,15] and able to fly at low altitudes, thus reducing the impact of cloud contamination and atmospheric interference that often hinders satellite imagery [1,7,11,15,16,25]. These advantages have driven the rapid adoption of UASs as data collection tools in a variety of fields in recent years [45,51,52].…”