Assessment of structural and functional characteristics of cropping systems using advanced terrestrial, air-, and spaceborne remote sensing applications - S. Dayananda, T. Möckel, M. Wachendorf C01 Methodology To capture a maximum of crop characteristics C01 applies following sensors: + Hyperspectral camera + UAV-RGB camera +3D Terr. laser scanner (I-C01) + WV3- & LISS-4 satellites Recorded crop reference data: + Crop fresh & dry matter yield (whole crops and parts) + Canopy height + Crude protein & ADF contents + Leaf area index and chlorophyll content Both sensor and reference measurements are conducted: + At 5 dates during growth of each treatment (3 crop species, 3 N levels) + For irrigated (rainy and dry season) and rainfed (rainy season only) systems (Fig. 1) Results Point clouds based on RGB drone imagery can be used to estimate biomass in vegetable crops along the whole growing period Hyperspectral full-frame imagery data is a valuable tool to estimate functional characteristics of crop species Combination of point cloud information with hyperspectral provides reliable estimates of biomass from different crops (Fig.2). Collaborations German projects: A01, A02, B01, B02, C02 Indian project: I-C01 “Integrating air- and space borne spectroscopy and laser scanning to assess structural and functional characteristics of crops and field margin vegetation. Fig. 2: Observed and predicted fresh biomass for tomato (top), eggplant (centre) and cabbage (bottom) based on random forest models using point cloud information only (left), hyperspectral information only (middle) and both datasets combined (right). Values on both axis are log-transformed. Fig. 1: Rainfed (left) and irrigated (right) experimental site at UASB from October 2017 Introduction Modern sensing techniques can be used to better understand the dynamics of crop production in the process of urbanisation and gain insight into the effects of growing cities on the structure, management intensity, and crop diversity of cropping systems. Phase I conclusions Modern remote sensing applications allow the assessment of structural (i.e. crop height) and functional characteristics (i.e. biomass) of various agricultural crops under the soil and climate conditions of Bengaluru. R²=0.98 R²=0.81 R²=0.90 R²=0.95 R²=0.97 R²=0.97 R²=0.89 R²=0.93 R²=0.92

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Page 1: Assessment of structural and functional characteristics of ... · Assessment of structural and functional characteristics of cropping systems using advanced terrestrial, air-, and

Assessment of structural and functional characteristics of cropping systems using advanced terrestrial, air-, and spaceborne remote sensing applications - S. Dayananda, T. Möckel, M. Wachendorf C01MethodologyTo capture a maximum of crop characteristics C01 applies following sensors:+ Hyperspectral camera+ UAV-RGB camera+ 3D Terr. laser scanner (I-C01)+ WV3- & LISS-4 satellitesRecorded crop reference data:+ Crop fresh & dry matter yield (whole crops and parts)+ Canopy height+ Crude protein & ADF contents+ Leaf area index and chlorophyll contentBoth sensor and reference measurements are conducted:+ At 5 dates during growth of each treatment (3 cropspecies, 3 N levels)+ For irrigated (rainy and dry season) and rainfed (rainyseason only) systems (Fig. 1) Results Point clouds based on RGB drone imagery can be usedto estimate biomass in vegetable crops along the wholegrowing period Hyperspectral full-frame imagery data is a valuable toolto estimate functional characteristics of crop species Combination of point cloud information withhyperspectral provides reliable estimates of biomassfrom different crops (Fig.2).Collaborations German projects: A01, A02, B01, B02, C02 Indian project: I-C01 “Integrating air- and space borne spectroscopy and laser scanning to assessstructural and functional characteristics of cropsand field margin vegetation.Fig. 2: Observed and predicted fresh biomass for tomato (top),eggplant (centre) and cabbage (bottom) based on randomforest models using point cloud information only (left),hyperspectral information only (middle) and both datasetscombined (right). Values on both axis are log-transformed.Fig. 1: Rainfed (left) and irrigated (right) experimental site at UASB from October 2017

IntroductionModern sensing techniques can be used to better understand the dynamics of crop production inthe process of urbanisation and gain insight into the effects of growing cities on thestructure, management intensity, and crop diversity ofcropping systems. Phase I conclusionsModern remote sensing applications allow the assessmentof structural (i.e. crop height) and functional characteristics(i.e. biomass) of various agricultural crops under the soil andclimate conditions of Bengaluru.

R²=0.98 R²=0.81 R²=0.90R²=0.95 R²=0.97 R²=0.97R²=0.89 R²=0.93 R²=0.92