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The estimation of physicochemical crop parameters based on spectral indices depend strongly on planting year, cultivar, and growing period. Therefore, the efficient monitoring of crop growth and nitrogen (N) fertilizer treatment requires that we develop a generic spectral index that allows the rapid assessment of the plant nitrogen content (PNC) of crops and that is independent of year, cultivar, and growing period. Thus, to obtain the best indicator for estimating potato PNC, herein, we provide an in-depth comparative analysis of the use of hyperspectral single-band reflectance and two- and three-band spectral indices of arbitrary bands for estimating potato PNC over several years and for different cultivars and growth periods. Potato field trials under different N treatments were conducted over the years 2018 and 2019. An unmanned aerial vehicle hyperspectral remote sensing platform was used to acquire canopy reflectance data at several key potato growth periods, and six spectral transformation techniques and 12 arbitrary band combinations were constructed. From these, optimal single-, two-, and three-dimensional spectral indices were selected. Finally, each optimal spectral index was used to estimate potato PNC under different scenarios and the results were systematically evaluated based on a correlation analysis and univariate linear modeling. The results show that, although the spectral transformation technique strengthens the correlation between spectral information and potato PNC, the PNC estimation model constructed based on single-band reflectance is of limited accuracy and stability. In contrast, the optimal three-band spectral index TBI 5 (530,734,514) performs optimally, with coefficients of determination of 0.67 and 0.65, root mean square errors of 0.39 and 0.39, and normalized root mean square errors of 12.64% and 12.17% for the calibration and validation datasets, respectively. The results thus provide a reference for the rapid and efficient monitoring of PNC in large potato fields. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) is an important indicator for monitoring crop growth and plays a vital role in guiding agricultural management, so it must be determined rapidly and nondestructively. The present study investigated the extraction from UAV hyperspectral images of multiple variables, including canopy original spectra (COS), first-derivative spectra (FDS), vegetation indices (VIs), and crop height (CH) to estimate the potato AGB via the machine-learning methods of support vector machine (SVM), random forest (RF), and Gaussian process regression (GPR). High-density point clouds were combined with three-dimensional spatial information from ground control points by using structures from motion technology to generate a digital surface model (DSM) of the test field, following which CH was extracted based on the DSM. Feature bands in sensitive spectral regions of COS and FDS were automatically identified by using a Gaussian process regression-band analysis tool that analyzed the correlation of the COS and FDS with the AGB in each growth period. In addition, the 16 Vis were separately analyzed for correlation with the AGB of each growth period to identify highly correlated Vis and excluded highly autocorrelated variables. The three machine-learning methods were used to estimate the potato AGB at each growth period and their results were compared separately based on the COS, FDS, VIs, and combinations thereof with CH. The results showed that (i) the correlations of COS, FDS, and VIs with AGB all gradually improved when going from the tuber-formation stage to the tuber-growth stage and thereafter deteriorated. The VIs were most strongly correlated with the AGB, followed by FDS, and then by COS. (ii) The CH extracted from the DSM was consistent with the measured CH. (iii) For each growth stage, the accuracy of the AGB estimates produced by a given machine-learning method depended on the combination of model variables used (VIs, FDS, COS, and CH). (iv) For any given set of model variables, GPR produced the best AGB estimates in each growth period, followed by RF, and finally by SVM. (v) The most accurate AGB estimate was achieved in the tuber-growth stage and was produced by combining spectral information and CH and applying the GPR method. The results of this study thus reveal that UAV hyperspectral images can be used to extract CH and crop-canopy spectral information, which can be used with GPR to accurately estimate potato AGB and thereby accurately monitor crop growth. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is an important indicator to evaluate crop growth, which is closely related to yield and plays an important role in guiding fine agricultural management. Compared with traditional AGB measurements, unmanned aerial vehicle (UAV) hyperspectral remote sensing technology has the advantages of being non-destructive, highly mobile, and highly efficient in precision agriculture. Therefore, this study uses a hyperspectral sensor carried by a UAV to obtain hyperspectral images of potatoes in stages of tuber formation, tuber growth, starch storage, and maturity. Linear regression, partial least squares regression (PLSR), and random forest (RF) based on vegetation indices (Vis), green-edge parameters (GEPs), and combinations thereof are used to evaluate the accuracy of potato AGB estimates in the four growth stages. The results show that (i) the selected VIs and optimal GEPs correlate significantly with AGB. Overall, VIs correlate more strongly with AGB than do GEPs. (ii) AGB estimates made by linear regression based on the optimal VIs, optimal GEPs, and combinations thereof gradually improve in going from the tuber-formation to the tuber-growth stage and then gradually worsen in going from the starch-storage to the maturity stage. Combining the optimal GEPs with the optimal VIs produces the best estimates, followed by using the optimal VIs alone, and using the optimal GEPs produces the worst estimates. (iii) Compared with the single-parameter model, which uses the PLSR and RF methods based on VIs, the combination of VIs with the optimal GEPs significantly improves the estimation accuracy, which gradually improves in going from the tuber-formation to the tuber-growth stage, and then gradually deteriorates in going from the starch-storage to the maturity stage. The combination of VIs with the optimal GEPs produces the most accurate estimates. (iv) The PLSR method is better than the RF method for estimating AGB in each growth period. Therefore, combining the optimal GEPs and VIs and using the PLSR method improves the accuracy of AGB estimates, thereby allowing for non-destructive dynamic monitoring of potato growth. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky–Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes. [ABSTRACT FROM AUTHOR]

• Deep features were extracted by combining SPA and LSTM. • The AGB estimation performance of spectral and deep features was compared. • The accuracy of the SPA-LSTM model across years and growth stages was evaluated. The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation indices (VIs) and locations at fixed wavelengths, can be not applicable to single growth stages. Therefore, the main objective of this study is to improve the applicability of the AGB estimation model to mitigate the impacts of phenological stages by mining depth features from hyperspectral data. The canopy hyperspectral and AGB data of potato tuber formation, tuber growth and starch accumulation stages were measured in 2017–2019. The acquired canopy hyperspectral reflectance was smoothed by Savitzky-Golay (SG) filter, and the green edge (GE), the red edge (RE), red edge chlorophyll index (CIred-edge), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and normalized difference vegetation index (NDVI) were extracted. The spectral changes were analyzed at different growth stages of each year. To obtain features in response to AGB changes, a new approach is proposed to analyze the hidden depth features between sensitive wavelengths by combining a successive projections algorithm (SPA) and a long-short-term memory network (LSTM). The AGB estimation models were constructed by partial least squares regression (PLSR) based on traditional VIs + GE + RE, full spectrum, sensitive wavelengths obtained by SPA, and depth features obtained by SPA-LSTM. The results showed that GE, RE and VIs alone explained only 24 %-36 % of the variation in AGB. The accuracy of estimating AGB at sensitive wavelengths obtained after SPA was higher (R2 = 0.70, RMSE = 406 kg/hm2, and NRMSE = 27.14 %) than that of conventional VIs + GE + RE (R2 = 0.51, RMSE = 536 kg/hm2, and NRMSE = 35.81 %) and full spectrum (R2 = 0.63, RMSE = 452 kg/hm2, and NRMSE = 30.19 %) in the three-year dataset. The depth features extracted by the SPA-LSTM method proposed in this study achieved the best AGB estimation (R2 = 0.82, RMSE = 318 kg/hm2, and NRMSE = 21.25 %). The applicability of the model was validated in different years and growth stages. The R2 of each year was in the range of 0.48–0.77, the RMSE was in the range of 290–367 kg/hm2, and the NRMSE was in the range of 16.69 %-25.81 %. The R2 of each growth stage within three year was in the range of 0.48–0.78, the RMSE was in the range of 184–386 kg/hm2, and the NRMSE was in the range of 14.91 %-29.45 %. The method proposed in this study mitigates the impacts of phenological stage and improves the robustness and accuracy of the AGB estimation model, which provides remote sensing technical support for realizing potato growth monitoring and yield assessment in the field. [ABSTRACT FROM AUTHOR]

• AGB was estimated by combining UAV RGB and hyperspectral images. • The ability of different wavelet basis functions to extract features was compared. • The high-frequency information and wavelet coefficients were used to estimate AGB. • The problem of inaccurate estimation of potato AGB using VIs was solved. Crops' above-ground biomass (AGB) is a crucial indicator that reflects crop health and predicts crop yield. However, using only optical vegetation indices (VIs) can produce inaccurate AGB estimates due to differences in crop varieties, growth stages, and measurement environments. Given the advantages of unmanned aerial vehicle (UAV) RGB and hyperspectral image fusion, this study evaluated the performance of multi-source remote sensing data for estimating potato AGB at multiple growth stages. In 2019, this study conducted potato trials with different varieties, fertilization levels, and planting densities at the Xiaotangshan Experiment Base (Beijing). UAV image and AGB data of potato three main stages were obtained from ground survey work. High-frequency information of the potato canopy was extracted from RGB images using discrete wavelet transform (DWT). VIs and wavelet energy coefficients were extracted from hyperspectral images using continuous wavelet transform (CWT). The linear relationships between potato AGB with VIs, high-frequency information, and wavelet coefficients were analyzed. Potato AGB estimation models were constructed based on single and multiple types of variables using multiple stepwise regression (MSR) and random forest (RF) models, respectively. This work showed the following results: (i) High-frequency information and wavelet coefficients were more sensitive to potato multi-growth stage AGB than VIs, and the latter were the most sensitive. (ii) Using VIs, high-frequency information, or wavelet coefficients separately to estimate the potato multi-growth stage AGB resulted in higher error and lower model accuracy. (iii) Combining VIs with either high-frequency information or wavelet coefficients improved the accuracy of AGB estimation, which was further improved by combining high-frequency information with wavelet coefficients. (iv) Combining VIs with both high-frequency information and wavelet coefficients provided the highest estimation accuracy using the MSR method. This combined AGB estimation model reduced the RMSE by 27%, 21%, and 16%, respectively, relative to VIs, high-frequency information, or wavelet coefficients alone. This result shows that the complementary advantages of multi-source UAV data can solve the challenge of insufficient AGB estimation by optical remote sensing. The work in this study provides remote sensing technology support to achieve potato crop growth monitoring and improve yield predictions. [ABSTRACT FROM AUTHOR]

• The ability of AGB estimation based on RGB-VIs and H-VIs was compared. • The potential of RGB-VIs, SFs and TFs to estimate AGB was evaluated. • The potato AGB estimation model was built based on multiple types of features. • UAV RGB images could be used to monitor potato crop growth. Rapid and non-destructive potato above ground biomass (AGB) monitoring is a crucial step in the development of smart agriculture because AGB is closely related to crop growth, yield, and quality. Compared to time-consuming and laborious field surveys, unmanned aerial vehicle (UAV) remote sensing provides a new direction for large-scale AGB monitoring. However, estimating AGB using an optical remote sensing technique usually does not work well because of spectral saturation, but multi-source remote sensing feature fusion (e.g., fusing spectral and structural features) can mitigate that problem. Due to potato crop canopy structure and AGB change greatly during growth, the potential of fusing optical, textural (TFs), and structural features (SFs) for calculating potato AGB at multiple growth stages was unknown. In addition, the ability of optical features, TFs, and SFs and their combinations to estimate potato AGB had not been examined. Vegetation indices (RGB-VIs), TFs, and SFs were extracted from ultra-high spatial resolution RGB images and compared their performances for estimating potato AGB with those of hyperspectral vegetation indices (H-VIs) obtained from UAV hyperspectral images. The results revealed that each type of feature had its own advantages and limitations for potato AGB estimation. Except for canopy volume (CV) in SFs, the best H-VI, RGB-VI, and TF for estimating AGB in both single growth stages and the entire growth period were inconsistent. When estimating AGB with only a single type of feature, the model accuracy in descending order was SFs, TFs, H-VIs, and RGB-VIs. The fusion of any two types of remote sensing features improved AGB estimation model accuracy. Among them, TFs combined with SFs provided the best estimation performance. The fusion of RGB-VIs, TFs, and SFs produced the best AGB estimates precision (R2 = 0.81, RMSE = 207 kg/hm2, NRMSE = 17.40%). Since AGB was effectively estimated under different treatments in the field, the model applicability was confirmed. Using different types of remote sensing features, the Gaussian process regression method produced better estimation results than the partial least squares regression method did. This study provides an economic and effective method for monitoring the potato growth in the field, and thus helps improve farmland production and guide fertilization management. [ABSTRACT FROM AUTHOR]

• Our method focuses on exploring the effect of texture indices to estimate potato PNC. • An texture index was developed for PNC estimation throughout the potato growth cycle. • O-TTI3 can further improve the estimation accuracy of potato PNC. Plant nitrogen content (PNC) is vital for evaluating crop nitrogen nutrient status and for net primary productivity. Therefore, rapid and accurate acquisition of crop PNC information can help reduce fertilizer and increase efficiency in modern agriculture. This study investigated whether an optimized texture index constructed from hyperspectral characteristic wavelength images acquired from a unmanned aerial vehicle (UAV) may be used to estimate PNC in potatoes over multiple growth stages. A potato field trial conducted in 2019 in Beijing, China, included different planting densities, nitrogen fertilizer gradients, and potato varieties with three replicates. A UAV remote sensing platform served to acquire hyperspectral images during three critical N demand periods for potatoes. In addition, we simultaneously conducted field sampling to obtain ground-truth PNC measurements. Following the classical form of vegetation indices, 12 texture indices were constructed based on hyperspectral texture features, and an arbitrary variable combination optimization algorithm was used to optimize these indices. Finally, the texture index, which had the highest correlation with potato PNC, was used as the best indicator for estimating the PNC status of potato at multiple growth stages and whether this indicator, in combination with spectral information, could further improve the accuracy of potato PNC estimation was subsequently explored. The results showed that (i) the optimal texture index TTI3 (R 494 -Cor, R 578 -Hom, R 514 -Sem) maintained a good linear relationship with PNC at the late stage of potato growth, and the accuracy and stability of the PNC estimation models constructed based on it was significantly better than that of a single texture metric. (ii) Compared with spectral information alone, the texture index combined with spectral features improved the accuracy of potato PNC estimation. More specifically, TTI3 (R 494 -Cor, R 578 -Hom, R 514 -Sem) combined with the three-band spectral index TBI 5 (530, 734, 514) achieved the best estimation accuracy with calibrated R2 , root mean square error (RMSE), and normalized RMSE of 0.77%, 0.28%, and 9.88%, respectively. The results of this study showed that the texture index constructed by combining multiple texture metrics enhanced the association between texture features and potato PNC over multiple growth stages, thus improving the monitoring accuracy of potato nitrogen nutrition status. This study can provide a reference for accurately managing crop nitrogen nutrition. [ABSTRACT FROM AUTHOR]

• The changes of potato textures and plant heights were analyzed. • The estimation models of potato above ground biomass were constructed. • The underestimation of above ground biomass when using VIs alone was solved. • The potato growth monitoring at the field scale was realized. Above-ground biomass (AGB) is a significant phenotypic index for evaluating photosynthesis capacity, healthy growth, and estimating crop yield. Accurately monitoring the AGB helps improve agricultural fertilization management and optimize planting patterns. Numerous studies have confirmed that canopy spectrum saturation causes optical vegetation indices (VIs) to underestimate the AGB of crops at multiple growth periods. To solve this problem, the present research used a remote sensing method to obtain RGB images of potato tuber formation-, tuber growth-, and starch accumulation- periods by a high-definition digital camera sensor on the unmanned aerial vehicle (UAV). From the ultrahigh spatial resolution RGB images, we then extracted RGB-VIs, textures (based on the gray level co-occurrence matrix, GLCM), and crop height (Hdsm) and analyzed the correlation between the three image features and the potato AGB for single and multiple growth periods. Finally, we estimated potato AGB at multiple growth periods based on (1) RGB-VIs, (2) RGB-VIs + GLCM-based textures, (3) RGB-VIs + Hdsm, and (4) RGB-VIs + GLCM-based textures + Hdsm by applying multiple stepwise regression (MSR) and extreme learning machine (ELM). The results showed that (i) unlike the texture features of wheat and maize that increased with growth period, the texture features and crop height of the potato canopy both increased first and then decreased with the growth period. (ii) The potato AGB was poorly estimated when using RGB-VIs, CLCM-based textures, or Hdsm individually; (iii) combining GLCM-based textures, Hdsm, and RGB-VIs solved the problem of underestimating the high AGB values of potato samples by the RGB-VIs model alone. Therefore, combining GLCM-based textures, Hdsm, and RGB-VIs obtained from UAV digital images could enhance the accuracy of potato AGB estimation under high coverage. [ABSTRACT FROM AUTHOR]

• The optimized vegetation indices (Opt-VIs) could resist spectral saturation. • Sensitive wavelengths in response to potato AGB were 734 and 742 nm. • Opt-VIs combined with texture features improved potato AGB estimation. Above-ground biomass (AGB) estimation is critical for monitoring crop growth and assessing yields. Unmanned aerial vehicle (UAV) optical remote sensing technology offers robust support for crop AGB estimation through vegetation indices (VIs). However, under conditions of high nitrogen or high AGB, most VIs lose their response to the presence of a dense plant canopy. To address the inaccuracy of estimating crop multistage AGB based on VIs formed in two bands, a UAV imaging hyperspectral experiment was conducted on potato test plots with different varieties, planting densities, and fertilizer application gradients. The ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) were initially modified using a lambda-by-lambda optimization algorithm, which yielded the optimized RVI (Opt-RVI) and NDVI (Opt-NDVI). Their performances were compared with published VIs. Then, the corresponding bands from optimized VIs were used to extract textural features and their estimation performances were evaluated. Finally, based on published VIs, Opt-VIs, textures of sensitive bands, and Opt-VIs combined with the textures, the potato multistage AGB was estimated by optimized random forest (RF) regression. The RF estimation model was simplified according to variable importance scores. Results showed the spectral regions sensitive to AGB were mainly located in the red-edge range. The wavelengths of Opt-RVI and Opt-NDVI that exhibited the strongest correlation with AGB were 734 and 742 nm. Except for the normalized red-edge index formed by two red-edge bands, the published VIs (R2 = 0.07–0.28) demonstrated saturation in the context of high AGB. The optimized Opt-RVI and Opt-NDVI suppressed this phenomenon, such that R2 values for both reached 0.44 for estimating AGB. The R2 values of textural features relative to AGB for both bands ranged from 0.06 and 0.31, and CON 734 , COR 734 , DIS 742 , ENT 742 , SEC 742 , and COR 742 exhibited the strongest correlations with AGB (R2 > 0.2). The performance of estimating potato AGB by a single indicator was sensitive texture features, Opt-VIs and published VIs from high to low and the estimation capacities of these models were limited. The combination of Opt-VIs and textural features of sensitive bands demonstrated the greatest estimation accuracy (R2 = 0.62 and RMSE = 293.08 kg/hm2). Based on variable importance scores, the accuracy of the simplified RF estimation model (four variables: Opt-RVI, CON 734 , ENT 742 , COR 742) slightly decreased (R2 = 0.59 and RMSE = 301.01 kg/hm2), but model complexity was reduced and computational efficiency was improved. The simplified model accuracy was mainly affected by potato variety, fertilizer gradient, and growth stage, but not by planting density. The results of this study can be used as a reference for potato growth monitoring in the field. [ABSTRACT FROM AUTHOR]

• The AGB estimation performance of ASD and UHD185 was compared. • HD was employed to improve spectral sensitivity to AGB. • The AGB spatial distribution map was created. Accurately estimating potato above-ground biomass (AGB), which is closely associated with the growth and yield of crops, carries significant importance for guiding field management practices. Hyperspectral techniques have emerged as a powerful and efficient tool for quickly and non-invasively acquiring information about AGB due to its capability to provide rich spectral data closely related to crop physiology and biochemistry. However, using spectral features obtained from hyperspectral data, such as spectral reflectance and vegetation indices (VIs), often leads to inaccurate estimations of crop AGB at multiple growth stages due to spectral saturation effects and dynamic changes in spectral responses. To enhance the robustness of AGB estimation models, this study proposed a harmonic decomposition (HD) method derived from Fourier series to extract energy features. The ground (referred to as ASD) and unmanned aerial vehicle hyperspectral (referred to as UHD185) remote sensing data from three growth stages of potatoes in 2018 (validation set) and 2019 (calibration set) were utilized in the study. Firstly, a comparison was made between the spectral reflectance of the potato canopy measured by the ASD and UHD185 sensors. Subsequently, the correlation between spectral reflectance, VIs, and harmonic components obtained from ASD and UHD185 sensors was analyzed in relation to AGB at both the individual and whole growth stage. Then, sensitive bands selected through CARS (competitive adaptive reweighted sampling), the entire spectral reflectance, VIs, and harmonic components, were utilized to construct AGB estimation models by partial least squares regression (PLSR). Finally, the optimal model performance was validated across different years, growth stages, and treatment conditions. The results showed there were differences in spectral reflectance acquired by ASD and UHD185 sensors across various wavelengths, but overall, there was a high level of consistency between the two. The correlation of spectral reflectance and VIs with potato AGB at individual growth stage was notably higher than that observed for entire growth stages. The accuracy of AGB estimation using VIs obtained from ASD (the R2, RMSE and NRMSE of validation sets were 0.52, 592 kg/hm2 and 26.91 %, respectively) and UHD185 (the R2, RMSE and NRMSE of validation sets were 0.46, 612 kg/hm2 and 27.82 %, respectively) sensors were low. Utilizing sensitive bands and full spectral reflectance separately improved the precision of models, although the enhancement was somewhat limited. The HD-PLSR models from ASD (the R2, RMSE and NRMSE of validation sets were 0.69, 477 kg/hm2 and 21.69 %, respectively) and UHD185 (the R2, RMSE and NRMSE of validation sets were 0.66, 481 kg/hm2 and 21.86 %, respectively) achieved the best AGB estimation results. Using the HD-PLSR model to estimate AGB for two years, the R2 values were 0.79 and 0.76 for ASD and UHD185, with RMSE values of 381 kg/hm2 and 386 kg/hm2 and NRMSE values of 22.35 % and 22.70 %, respectively. The capability of the HD-PLSR model was confirmed at various growth stages and treatments. This work offers valuable remote sensing technical support for implementing potato growth monitoring and yield assessment in the field. [ABSTRACT FROM AUTHOR]