000			05063nab|a22004337a|4500
001			66431
003			MX-TxCIM
005			20240116160544.0
008			20231s2023||||mx |||p|op||||00||0|eng|d
022			_a1385-2256
022			_a1573-1618 (Online)
024	8		_ahttps://doi.org/10.1007/s11119-023-10062-4
040			_aMX-TxCIM
041			_aeng
100	0		_aShao-Hua Zhang _927767
245	1		_aAboveground wheat biomass estimation from a low-altitude UAV platform based on multimodal remote sensing data fusion with the introduction of terrain factors
260			_bSpringer, _c2024. _aNetherlands :
500			_aPeer review
520			_aAboveground biomass is an important indicator used to characterize the growth status of crops, as well as an important physical and chemical parameter in agroecosystems. Aboveground biomass is an important basis for formulating management measures such as fertilization and irrigation. We selected four irrigated wheat fields in a region near Kaifeng, Henan Province, for this study. The terrain in that region was undulating and had spatial differences. We used a low-altitude unmanned aerial vehicle (UAV) remote sensing platform equipped with a multispectral camera, thermal infrared camera, and RGB camera to simultaneously obtain different remote sensing parameters during the key growth stages of wheat. Based on the extracted spectral reflectivity, thermal infrared temperature, and digital elevation information, we calculated the spatial variability of remote sensing parameters and growth indices under different terrain characteristics. We also analyzed the correlations between vegetation indices, temperature parameters, structural topographic parameters and aboveground biomass. Three machine learning methods were used, including the multiple linear regression method (MLR), partial least squares regression method (PLSR) and random forest regression method (RFR). We compared the aboveground biomass (AGB) estimation capability of single-modal data versus multimodal data fusion frameworks. The results showed that slope was an important factor affecting crop growth and aboveground biomass. We therefore analyzed several remote sensing parameters for three different slope scales. We found significant differences among them for soil water content, water content of plants, and aboveground biomass at four growth stages. Based on the strength of their correlation with aboveground biomass, seven vegetation indices (NDVI, GNDVI, NDRE, MSR, OSAVI, SAVI, and MCARI), four canopy structure parameters (CH, VF, CVM, SLOPE) and two temperature parameters (NRCT, CTD) were selected as the final input variables for the model. There was some variability in the accuracy of the models at different growth stages. The average accuracy of the models was anthesis stage > booting stage > filling stage > jointing stage. For the single-modal data framework, the model constructed with the vegetation indices was better than the aboveground biomass model constructed using the temperature or structure parameters, and the highest accuracy was obtained with an RFR model based on vegetation indices at the anthesis stage (R2 = 0.713). For the double modal data fusion approach, the highest accuracy resulted at the anthesis stage, using the structural parameters combined with the vegetation indices of the RFR model (R2 = 0.842). Even higher accuracies were obtained using the multimodal data fusion approach with an RFR model based on vegetation indices, temperature parameters and structure parameters at the anthesis stage (R2 = 0.897). By introducing terrain factors and combining them with the RFR algorithm to effectively integrate multimodal data, the complementary and synergistic effects between different remote sensing information sources could be fully exerted. The accuracy and stability of the aboveground biomass estimation models were effectively improved, and a high-throughput phenotype acquisition method was explored, which provides a reference and basis for real-time monitoring of crop growth and decoding the correlation between genotype and phenotype.
546			_aText in English
591			_aLi He : Not in IRS staff list but CIMMYT Affiliation
591			_aJian‑Zhao Duan : Not in IRS staff list but CIMMYT Affiliation
591			_aWei Feng : Not in IRS staff list but CIMMYT Affiliation
650		7	_aUnmanned aerial vehicles _2AGROVOC _911401
650		7	_aWinter wheat _2AGROVOC _92104
650		7	_aRemote sensing _2AGROVOC _91986
650		7	_aBiomass _2AGROVOC _91897
650		7	_aModels _2AGROVOC _94859
700	0		_aLi He _931579
700	0		_aJian‑Zhao Duan _927769
700	0		_aShao-Long Zang _931580
700	0		_aTian-Cong Yang _931581
700	1		_aSchulthess, U. _8CSCU01 _92005 _gSustainable Agrifood Systems
700	0		_aTian Cai Guo _931582
700	0		_aChen-Yang Wang _931583
700	0		_aWei Feng _924683
773	0		_tPrecision Agriculture _dNetherlands : Springer, 2024 _x1385-2256 _gv. 25. p. 119–145 _wu99020
942			_cJA _n0 _2ddc
999			_c66431 _d66423