Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs

Guan, Senlin; Fukami, Koichiro; Matsunaka, Hitoshi; Okami, Midori; Tanaka, Ryo; Nakano, Hiroshi; Sakai, Tetsufumi; Nakano, Keiko; Ohdan, Hideki; Takahashi, Kimiyasu

doi:10.3390/rs11020112

Cited by 130 publications

(94 citation statements)

References 41 publications

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“…Operation efficiency is a primary factor to utilize UAV remote sensing for agricultural applications. The operating efficiency depends on the flight operation plan, such as forward and side overlaps, time interval for capture and flight altitude (Guan et al, ). In a previous study, Guan et al () evaluated the average operating efficiency from 223 automatic flights using multispectral DJI Phantom 4 or DJI Phantom 4 Pro cameras at 30‐, 50‐ and 100‐m flight altitudes.…”

Section: Discussionmentioning

confidence: 99%

Counting of Pennisetum alopecuroides at heading stage in a grazed pasture using images from an unmanned aerial vehicle

et al. 2020

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This study focused on the fact that Pennisetum alopecuroides ears are black that represents a black dots in image from sky and developed a simple algorithm to count the number of P. alopecuroides plants from unmanned aerial vehicle (UAV) images. Ortho-mosaicked red-green-blue (RGB) images from different flight altitudes (28, 56 and 82 m) were obtained in the heading growth stage. To remove small noisy pixels, various sized median filters (3 × 3-21 × 21) were tested. While the relationship between field and image counts was positive for most images obtained from 56 and 82 m altitudes, images in the 28-m flight altitude (the highest spatial resolution) with small and moderate-sized median filters (3 × 3-9 × 9) were overestimated. Moreover, higher resolution images from lower altitudes of UAV images decrease the operating efficiency (areas covered per 10 min). Overall, the best accuracy (80.3%) and lowest mean absolute error (MAE = 12.93) were obtained for the 56-m flight altitude

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Section: Discussionmentioning

confidence: 99%

Counting of Pennisetum alopecuroides at heading stage in a grazed pasture using images from an unmanned aerial vehicle

et al. 2020

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“…High-resolution digital surface models (DSMs) generated from sUAS data allow accounting for topo-edaphic conditions in forage production modeling and related analysis. A few pilot studies have explored the applications of sUAS technology in monitoring agricultural production for soybean [37], rice and wheat [38,39], barley [40,41], and mango [42]. However, there is currently limited research concerning the efficacy of sUASs for rangeland forage production quantification and modeling.…”

Section: Introductionmentioning

confidence: 99%

Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

Han

Dahlgren

Larsen³

et al. 2019

Remote Sensing

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Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass ( R 2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.

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“…The study was based in Leeton, NSW, Australia. A list of all the experiments used in this work is given in Table 34.606S]). The soil types of these sites according to the definitions in [42] are red-brown earth at YAI, and self-mulching clay at LFS.…”

Section: Methodsmentioning

confidence: 99%

“…Some studies have used hand-held or proximal sensors [31]. Mounting sensors on unmanned aerial vehicles (UAVs) allows collecting within-field variability data in much less time than using proximal sensors [21,33,34]. Some proximal sensing systems use active sensors, where the incident radiation is generated by the device.…”

Section: Introductionmentioning

confidence: 99%

Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

et al. 2019

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Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R 2 = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R 2 < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R 2 of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.

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Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs

Cited by 130 publications

References 41 publications

Counting of Pennisetum alopecuroides at heading stage in a grazed pasture using images from an unmanned aerial vehicle

Counting of Pennisetum alopecuroides at heading stage in a grazed pasture using images from an unmanned aerial vehicle

Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

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