Evaluation of the UAV-Based Multispectral Imagery and Its Application for Crop Intra-Field Nitrogen Monitoring and Yield Prediction in Ontario

Unmanned Aerial Vehicle (UAV) has the capability of acquiring high spatial and temporal resolution images. This new technology fills the data gap between satellite and ground survey in agriculture. In addition, UAV-based crop monitoring and methods are new challenge of remote sensing application in agriculture. First, in my thesis the potential of UAV-based imagery was investigated to monitor spatial and temporal variation of crop status in comparison with RapidEye. The correlation between red-edge indices and LAI and biomass are higher for UAV-based imagery than that of RapidEye. Secondly, the nitrogen weight and yield in wheat was predicted using the UAV-based imagery. The intra-field nitrogen prediction model performs well at wheat early growth stage. Additionally, the best data collection time for yield prediction is at the end of booting stage. The results demonstrate the UAV-based data could be an alternative effective and affordable approach for farmers on intra-field management

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Citation: Peralta, N.R.; Assefa, Y.; Du, J.; Barden, C.J.; Ciampitti, I.A. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield. Remote Sens. 2016, 8, 848.This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value)

Application of RADARSAT-2 Polarimetric Data for Land Use and Land Cover Classification and Crop monitoring in Southwestern Ontario

Timely and accurate information of land surfaces is desirable for land change detection and crop condition monitoring. Optical data have been widely used in Land Use and Land Cover (LU/LC) mapping and crop condition monitoring. However, due to unfavorable weather conditions, high quality optical images are not always available. Synthetic Aperture Radar (SAR) sensors, such as RADARSAT-2, are able to transmit microwaves through cloud cover and light rain, and thus offer an alternative data source. This study investigates the potential of multi-temporal polarimetric RADARSAT-2 data for LU/LC classification and crop monitoring in the urban rural fringe areas of London, Ontario. Nine LU/LC classes were identified with a high overall accuracy of 91.0%. Also, high correlations have been found within the corn and soybean fields between some polarimetric parameters and Normalized Difference Vegetation Index (NDVI). The results demonstrate the capability of RADARSAT-2 in LU/LC classification and crop condition monitoring

Assessing Agave sisalana biomass from leaf to plantation level using field measurements and multispectral satellite imagery

Biomassa, eli kasviaineksen määrä, on tärkeä muuttuja viljelykasvien kasvun seurannassa sekä arvioitaessa hiilen kiertoa. Kenttätöissä biomassaa voidaan arvioida kasveja vahingoittamatta hyödyntämällä allometrisia malleja. Suuremmassa mittakaavassa biomassaa voidaan kartoittaa kaukokartoitusmenetelmillä. Tässä tutkimuksessa arvioitiin Agave sisalanan eli sisalin lehtien kuivaa biomassaa. Sisal on trooppisilla ja subtrooppisilla alueilla viljeltävä monivuotinen kasvi, jonka lehdistä tuotetaan kuitua ja biopolttoainetta. Lehtibiomassan arvioimiseksi luotiin ensin allometrinen malli, minkä jälkeen biomassa mallinnettiin 8851 hehtaarin plantaasille Kaakkois-Keniassa käyttämällä Sentinel-2 multispektraalista satellittikuva-aineistoa. Allometrista mallia varten kerättiin 38:n lehden otos. Kasvin korkeuden ja lehden suurimman ympärysmitan avulla muodostettiin tilavuusarvio, jonka yhteyttä biomassaan mallinnettiin lineaarisella regressiolla. Muuttujien välille löytyi vahva log-log lineaarinen yhteys ja ristiinvalidointi osoitti, että mallin ennusteet ovat tarkkoja (R2 = 0.96, RMSE = 7.69g). Mallin avulla ennustettiin lehtibiomassa 58:lle koealalle, jotka muodostivat otoksen biomassan mallinnukseen Sentinel-2 kuvalla. Mallinnuksessa käytettiin yleistettyjä additiivisia malleja, joiden avulla tutkittiin lukuisten spektraalisten kasvillisuusindeksien yhteyttä biomassaan. Parhaaksi osoittautuivat indeksit, jotka laskettiin hyödyntämällä vihreää ja lähi-infrapunakanavaa, sekä ns. ”red-edge”-kanavia (D2 = 74%, RMSE = 4.96 Mg/ha). Keskeisin mallin selitysastetta heikentävä tekijä vaikutti olevan suuresti vaihteleva aluskasvillisuuden määrä. Hyödyntämällä parhaaksi todettua kasvillisuusindeksiä lehtibiomassa mallinnettiin koko plantaasin peltoalalle. Biomassa vaihteli 0 ja 45.1 Mg/ha välillä, keskiarvon ollessa 9.9 Mg/ha. Tämän tutkimuksen tuloksena syntyi allometrinen malli, jota voidaan käyttää sisalin lehtibiomassan arviointiin. Jatkotutkimuksissa tulisi ottaa huomioon myös kasvin muut osat, kuten varsi ja juuret. Biomassan mallinnus multispektraalisilla kasvillisuusindekseillä osoitti menetelmän toimivuuden sisalin biomassan kartoituksessa, mutta vaihtelevan aluskasvillisuuden todettiin heikentävän mallin suorituskykyä. Aluskasvillisuuden vaikutusta ja täydentäviä aineistolähteitä tulisi tutkia tulevaisuudessa. Plantaasin lehtibiomassan, ja näin ollen maanpäälle sitoutuneen hiilen määrä, on saman suuruinen, kuin alueen luonnollisella pensassavannilla. Sisal-plantaasin hiilen kierron kokonaisvaltainen ymmärtäminen vaatii kuitenkin lisätietoa kasvien ja maaperän hiilivuosta sekä maaperän hiilensitomisesta.Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation

Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data. Model performance was evaluated based on cross-validation bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless, model performance increased with red edge indices and texture attributes, which shows that they play an important role in semi-arid regions with sparse vegetation

A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments

Assessment of vegetational indices applied to sugarcane monitoring using Rapideye images.

Rapideye sensor have been operating since 2009 and was one of the first multispectral (5 bands) sensors to operate as part of a satellite constellation (5 satellites), able to provide a massive stock of images with singular synoptic (~5 days) and spatial capabilities (5m). However, so far the Rapideye unique characteristic is still underused, precisely, its Red Edge band (690-730) and possible applications to agricultural mapping considering crop biophysical compounds. The Red Edge band is spectrally located between the Red band, where chlorophyll presence causes strong absorption of light, and the Near Infrared (NIR) band, which has a strong reflection associated with the leaf cell structure. In rder to explore the spectral characteristics of the Rapideye images we evaluated the sensor performance at the sugarcane crop over five different crop anagement (i.e. irrigated, non-irrigated, meiosi, conventional management and Coopercitrus management), by comparison between two band ratios: NDVI (Normalized Difference Vegetation Index), that uses Red and NIR bands; and NDRE (Normalized Difference Red Edge Index), which uses the Red and Red Edge bands. Through the use of NDRE index was possible to map iscreet and important spatial variations of chlorophyll content in the intra experimental fields

Satellite and Fluorescence Remote Sensing for Rice Nitrogen Status Diagnosis in Northeast China

Nitrogen (N), as the most important element of crop growth and development, plays a decisive role in ensuring yield. However, the problems of over-application of N fertilizers have been repeatedly reported in China, which resulted in low N use efficiency and high risk of environmental pollution. The requirements of developing technologies for real-time and site-specific diagnosis of crop N status are the foundation to realize the precision N management, and also benefit to the improvement of the N use efficiency. Remote sensing technology provides a promising non-intrusive solution to monitor rice N status and to realize the precision N management over large areas. This research focuses on proposing N nutrition diagnosis methods and developing N fertilizer management strategies for paddy rice of cold regions in Northeast China. The main contents and results are presented as follows: (1)This study developed a new critical N (Nc) dilution curve for paddy rice of cold regions in Northeast China. The curve could be described by the equation Nc=27.7W^(-0.34) if W≥1 t/ha for dry matter (DM) or Nc=27.7g/kg DM if W<1 t/ha, where W is the aboveground biomass. Results indicated that the new Nc dilution curve was suitable for diagnosing short-season Japonica rice N status in Northeast China. The validation result indicated that it worked well to diagnose plant N status of the 11-leaf variety rice. (2)This study investigated the potential of the satellite remote sensing data for diagnosing rice N status and guiding the topdressing N application at the stem elongation stage in Northeast China. 50 vegetation indices (VIs) were computed based on the FORMOSAT-2 satellite data, and they were correlated with the field-based agronomic variables, i.e., aboveground biomass (AGB), leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), chlorophyll meter readings, and N nutrition index (NNI, defined as the ratio of actual PNC and critical PNC according to the new Nc dilution curves). The results presented that 45% of variation in the NNI was obtained by using a direct estimation method based on the best VI according to the FORMOSAT-2 satellite data, while 52% of the variation in the NNI was yielded by an indirect estimation method, which firstly used the VIs to estimate AGB and PNU, respectively, then estimated NNI according to these two variables. Moreover, based on the critical N uptake curve, a N recommendation algorithm was proposed. The algorithm was based on the difference between the estimated PNU and the critical PNU to adjust the topdressing N application rate. The results demonstrated that FORMOSAT-2 images have the potential to estimate rice N status and guide panicle N fertilizer applications in Northeast China. (3)This study also evaluated the potential improvements of the newest satellite sensors with the red edge band for diagnosing rice N status in Northeast China. The canopy-scale hyperspectral data were upscaled to simulate the wavebands of RapidEye, WorldView-2, and FORMOSAT-2, respectively. The VI analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to evaluate the N status indicators. The results indicated that the VIs based on the RE band from RapidEye and WorldView-2 data could explain more variability for N indicators than the VIs from FORMOSAT-2 data having no RE band. Moreover, the SMLR and PLSR results revealed that both the near-infrared and red edge band were important for N status estimation. (4)The proximal fluorescence sensor Multiplex_3 was used to evaluate the potential of fluorescence spectrum for estimating the N status of the cold regional paddy rice at different growth stages. The Multiplex indices and their normalized N sufficient indices (NSI) were used to estimate the five N status indicators, i.e., AGB, leaf N concentration (LNC), PNC, PNU, and NNI. The results indicated that there were strong relationships between the fluorescence indices (i.e., BRR_FRF, FLAV, NBI_G, and NBI_R) and (i.e., LNC, PNC, NNI), with the coefficient of determination between 0.40 and 0.78. In particular, NNI was well estimated by these fluorescence indices. Moreover, the NSI data improved the accuracy of the N diagnosis. These results of this study were useful for N nutrition diagnosis and variable fertilization of the cold regional paddy rice, which were significant for the ecological environment protection and the national food security

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small