Remote Sensing Technologies in Agricultural Crop and Soil Monitoring

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 24584

Special Issue Editors

1. Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China
2. Department of Civil Engineering, Monash University, Clayton, Australia
Interests: remote sensing; radar; water resource; intelligent agriculture; artificial intelligence
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Guest Editor
Department of Civil Engineering, Monash University, Clayton, Australia
Interests: environmental sensing; earth system modelling; data assimilation; passive microwave; radar
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Forschungszentrum Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Wilhelm-Johnen-Straße, 52428 Jülich, Germany
Interests: remote sensing for hydrology; passive microwave remote sensing for surface soil moisture estimation; hydrological modeling; data assimilation; radiometer-radar data fusion; Unmanned Aerial Systems (UAS)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing provides accurate and timely information for agriculture management, including crop health, crop and soil water status, and evapotranspiration. The use of such information is increasingly important for achieving further advances in precision farming, water resource management and agricultural adaptation strategies to tackle climate change. The recent developments in remote sensing sensors, platforms and processing tools are enabling enhanced monitoring of crop and soil conditions, with unprecedented details in spatial and temporal resolutions, larger penetration depths, and the capability to image in three dimensions. The use of these new features together with cloud-based artificial intelligence is expected to allow state-of-the-art progress in agriculture, meeting the world’s growing demand for food production.

This Special Issue focuses on the use of state-of-the-art remote sensing technologies in agricultural crop and soil monitoring. Accordingly, it will include interdisciplinary studies embracing agriculture with disciplines of remote sensing, modelling, artificial intelligence, cloud computing, and electrical engineering. Research articles are expected to cover a broad range of crop and soil status, e.g., soil moisture, texture, and tillage status, as well as crop heath, biomass, density, and evapotranspiration. All types of articles, including original research, opinions, data notes, and reviews, are welcome.

Dr. Liujun Zhu
Prof. Dr. Jeffrey Walker
Dr. Carsten Montzka
Guest Editors

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Keywords

  • remote sensing
  • soil moisture
  • soil texture
  • soil salinity
  • field tillage status
  • crop heath
  • crop biomass
  • evapotranspiration
  • food security
  • artificial intelligence

Published Papers (14 papers)

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Research

17 pages, 5624 KiB  
Article
Estimation of Irrigation Water Use by Using Irrigation Signals from SMAP Soil Moisture Data
by Liming Zhu, Huifeng Wu, Min Li, Chaoyin Dou and A-Xing Zhu
Agriculture 2023, 13(9), 1709; https://doi.org/10.3390/agriculture13091709 - 29 Aug 2023
Cited by 1 | Viewed by 1278
Abstract
Accurate irrigation water-use data are essential to agricultural water resources management and optimal allocation. The obscuration presented by ground cover in farmland and the subjectivity of irrigation-related decision-making processes mean that effectively identifying regional irrigation water use remains a critical problem to be [...] Read more.
Accurate irrigation water-use data are essential to agricultural water resources management and optimal allocation. The obscuration presented by ground cover in farmland and the subjectivity of irrigation-related decision-making processes mean that effectively identifying regional irrigation water use remains a critical problem to be solved. In view of the advantages of satellite microwave remote sensing in monitoring soil moisture, previous studies have proposed a method for estimating irrigation water use using the satellite microwave remote sensing of soil moisture. However, the method is affected by false irrigation signals from soil moisture increases caused by non-irrigation factors, causing irrigation water use to be overestimated. Therefore, the purpose of this study is to improve the estimation of irrigation water use in drylands by using irrigation signals from SMAP soil moisture data. In this paper, the irrigation water use in Henan Province is estimated by using the irrigation signals from SMAP (soil moisture active and passive) soil moisture data. Firstly, a method for recognizing irrigation signals in soil moisture data obtained by microwave satellite remote sensing was used. Then, an estimation model of the amount of irrigation water (SM2Rainfall model) was built on each data pixel of the satellite microwave remote sensing of soil moisture. Finally, the amount of irrigation water utilized in Henan Province was estimated by combining the irrigation signals and irrigation water-use estimation model, and the results were evaluated. According to the findings, this study improved the estimation accuracy of irrigation water use by using the irrigation signals in Henan Province. The result of this study is of great importance to accurately obtain irrigation water use in the region. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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23 pages, 7312 KiB  
Article
Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis
by Artur Gafurov, Svetlana Mukharamova, Anatoly Saveliev and Oleg Yermolaev
Agriculture 2023, 13(9), 1672; https://doi.org/10.3390/agriculture13091672 - 24 Aug 2023
Cited by 1 | Viewed by 1421
Abstract
This study addresses the challenge of accurate crop detection using satellite data, focusing on the application of Long Short-Term Memory (LSTM) networks. The research employs a “spatial generalization” approach, where a model trained on one geographic area is applied to another area with [...] Read more.
This study addresses the challenge of accurate crop detection using satellite data, focusing on the application of Long Short-Term Memory (LSTM) networks. The research employs a “spatial generalization” approach, where a model trained on one geographic area is applied to another area with similar vegetation conditions during the growing season. LSTM networks, which are capable of learning long-term temporal dependencies, are used to overcome the limitations of traditional machine learning techniques. The results indicate that LSTM networks, although more computationally expensive, provide a more accurate solution for crop recognition compared with other methods such as Multilayer Perceptron (MLP) and Random Forest algorithms. The accuracy of LSTM networks was found to be 93.7%, which is significantly higher than the other methods. Furthermore, the study showed a high correlation between the real and model areas of arable land occupied by different crops in the municipalities of the study area. The main conclusion of this research is that LSTM networks, combined with a spatial generalization approach, hold great promise for future agricultural applications, providing a more efficient and accurate tool for crop recognition, even in the face of limited training data and complex environmental variables. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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24 pages, 17408 KiB  
Article
Unmanned Aerial System-Based Wheat Biomass Estimation Using Multispectral, Structural and Meteorological Data
by Jianyong Zhang, Yanling Zhao, Zhenqi Hu and Wu Xiao
Agriculture 2023, 13(8), 1621; https://doi.org/10.3390/agriculture13081621 - 17 Aug 2023
Cited by 2 | Viewed by 1072
Abstract
Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaining structural information about crops and identifying spatial heterogeneity. [...] Read more.
Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaining structural information about crops and identifying spatial heterogeneity. However, methods of data fusion of different factors still need to be explored in order to enhance the accuracy of their estimates. Therefore, the objective of this study was to investigate the combined metrics of different variables (spectral, structural and meteorological factors) for AGB estimation of wheat using UAS multispectral data. UAS images were captured on two selected growing dates at a typical reclaimed cropland in the North China Plain. The spectral response was determined using the highly correlated vegetation index (VI). A structural metric, the canopy height model (CHM), was produced using UAS-based multispectral images. The measure of growing degree days (GDD) was selected as a meteorological proxy. Subsequently, a structurally–meteorologically weighted canopy spectral response metric (SM-CSRM) was derived by the pixel-level fusion of CHM, GDD and VI. Both correlation coefficient analysis and simple function fitting were implemented to explore the highest correlation between the measured AGB and each proposed metric. The optimal regression model was built for AGB prediction using leave-one-out cross-validation. The results showed that the proposed SM-CSRM generally improved the correlation between wheat AGB and various VIs and can be used for estimating the wheat AGB. Specifically, the combination of MERIS terrestrial chlorophyll index (MTCI), vegetation-masked CHM (mCHM) and normalized GDD (nGDD) achieved an optimal accuracy (R2 = 0.8069, RMSE = 0.1667 kg/m2, nRMSE = 19.62%) through the polynomial regression method. This improved the nRMSE by 3.44% compared to the predictor using MTCI × mCHM. Moreover, the pixel-level fusion method slightly enhanced the nRMSE by ~0.3% for predicted accuracy compared to the feature-level fusion method. In conclusion, this paper demonstrated that an SM-CSRM using pixel-level fusion with canopy spectral, structural and meteorological factors can obtain a good level of accuracy for wheat biomass prediction. This finding could benefit the assessment of reclaimed cropland or the monitoring of crop growth and field management in precision agriculture. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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0 pages, 15213 KiB  
Article
Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India
by Amit Kumar Srivastava, Suranjana Bhaswati Borah, Payel Ghosh Dastidar, Archita Sharma, Debabrat Gogoi, Priyanuz Goswami, Giti Deka, Suryakanta Khandai, Rupam Borgohain, Sudhanshu Singh and Ashok Bhattacharyya
Agriculture 2023, 13(8), 1509; https://doi.org/10.3390/agriculture13081509 - 27 Jul 2023
Cited by 1 | Viewed by 2115
Abstract
Rice-fallow areas have significant potential to sustainably increase agricultural intensification to address growing global food demands while simultaneously increasing farmers’ income by harnessing the residual soil moisture in rainfed ecologies. Assam is the largest rice-growing belt in northeast India during kharif; however, [...] Read more.
Rice-fallow areas have significant potential to sustainably increase agricultural intensification to address growing global food demands while simultaneously increasing farmers’ income by harnessing the residual soil moisture in rainfed ecologies. Assam is the largest rice-growing belt in northeast India during kharif; however, for the next rabi season, an average of 58% of the rice areas remain uncultivated and are described as rice-fallow (Kharif, rabi and zaid are the crop seasons in the study area. The kharif season refers to the monsoon/rainy season and corresponds to the major crop season in the region extending from June to October. The rabi season refers to the winter season extending from November to April, and the zaid season is the summer crop season from April to June). Unutilized rice-fallow areas with optimum soil moisture for a second crop were identified over three consecutive years using multiple satellite data (optical and radar) for the state of Assam and an average accuracy of 92.6%. The reasons governing the existence of rice-fallow areas were analyzed, and an average of 0.88 million ha of suitable rice-fallow areas, based on soil moisture availability, were identified. Targeted interventions were carried out in selected locations to demonstrate the potential of sustainable cropping intensification. Maize, with best management practices, and a yield between 5.5 and 6 t/ha, was demonstrated as a successful second crop during the rabi season in selected areas with optimum residual soil moisture after the kharif paddy harvest. This study highlights the significance of geospatial technology to effectively identify and target suitable rice-fallow areas for cropping intensification and to enhance productivity and profitability. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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10 pages, 3392 KiB  
Article
Proximal Soil Moisture Sensing for Real-Time Water Delivery Control: Exploratory Study over a Potato Farm
by Xiaoling Wu, Jeffrey P. Walker and Vanessa Wong
Agriculture 2023, 13(7), 1297; https://doi.org/10.3390/agriculture13071297 - 25 Jun 2023
Cited by 1 | Viewed by 1549
Abstract
New sensing technologies are at the cusp of providing state-of-the-art infrastructure to precisely monitor crop water requirements spatially so as to optimize irrigation scheduling and agricultural productivity. This project aimed to develop a new smart irrigation system that uses an L-band radiometer in [...] Read more.
New sensing technologies are at the cusp of providing state-of-the-art infrastructure to precisely monitor crop water requirements spatially so as to optimize irrigation scheduling and agricultural productivity. This project aimed to develop a new smart irrigation system that uses an L-band radiometer in conjunction with an irrigation boom, allowing for a precision water delivery system using derived high-resolution soil moisture information. A potato farm was selected due to its sensitivity to water and an existing irrigation system where the radiometer could be mounted. A field experiment was conducted to capture the soil moisture variation across the farm using the radiometer. A greenhouse trial was also conducted to mimic the actual growth of potatoes by controlling the soil moisture and exploring the impact on their growth. It was found that 0.3 cm3/cm3 was the optimal moisture level in terms of productivity. Moreover, it was demonstrated that on-farm soil moisture maps could be generated with an RMSE of 0.044 cm3/cm3. It is anticipated that through such technology, a real-time watering map will be generated, which will then be passed to the irrigation software to adjust the rate of each nozzle to meet the requirements without under- or over-watering. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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14 pages, 7060 KiB  
Article
Maize On-Farm Stressed Area Identification Using Airborne RGB Images Derived Leaf Area Index and Canopy Height
by Rahul Raj, Jeffrey P. Walker and Adinarayana Jagarlapudi
Agriculture 2023, 13(7), 1292; https://doi.org/10.3390/agriculture13071292 - 24 Jun 2023
Cited by 1 | Viewed by 1240
Abstract
The biophysical properties of a crop are a good indicator of potential crop stress conditions. However, these visible properties cannot indicate areas exhibiting non-visible stress, e.g., early water or nutrient stress. In this research, maize crop biophysical properties including canopy height and Leaf [...] Read more.
The biophysical properties of a crop are a good indicator of potential crop stress conditions. However, these visible properties cannot indicate areas exhibiting non-visible stress, e.g., early water or nutrient stress. In this research, maize crop biophysical properties including canopy height and Leaf Area Index (LAI), estimated using drone-based RGB images, were used to identify stressed areas in the farm. First, the APSIM process-based model was used to simulate temporal variation in LAI and canopy height under optimal management conditions, and thus used as a reference for estimating healthy crop parameters. The simulated LAI and canopy height were then compared with the ground-truth information to generate synthetic data for training a linear and a random forest model to identify stressed and healthy areas in the farm using drone-based data products. A Healthiness Index was developed using linear as well as random forest models for indicating the health of the crop, with a maximum correlation coefficient of 0.67 obtained between Healthiness Index during the dough stage of the crop and crop yield. Although these methods are effective in identifying stressed and non-stressed areas, they currently do not offer direct insights into the underlying causes of stress. However, this presents an opportunity for further research and improvement of the approach. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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23 pages, 44399 KiB  
Article
Intelligent Extraction of Terracing Using the ASPP ArrU-Net Deep Learning Model for Soil and Water Conservation on the Loess Plateau
by Yinan Wang, Xiangbing Kong, Kai Guo, Chunjing Zhao and Jintao Zhao
Agriculture 2023, 13(7), 1283; https://doi.org/10.3390/agriculture13071283 - 22 Jun 2023
Cited by 1 | Viewed by 921
Abstract
The prevention and control of soil erosion through soil and water conservation measures is crucial. It is imperative to accurately and quickly extract information on these measures in order to understand how their configuration affects the runoff and sediment yield process. In this [...] Read more.
The prevention and control of soil erosion through soil and water conservation measures is crucial. It is imperative to accurately and quickly extract information on these measures in order to understand how their configuration affects the runoff and sediment yield process. In this investigation, intelligent interpretation algorithms and deep learning semantic segmentation models pertinent to remote sensing imagery were examined and scrutinized. Our objective was to enhance interpretation accuracy and automation by employing an advanced deep learning-based semantic segmentation model for the astute interpretation of high-resolution remote sensing images. Subsequently, an intelligent interpretation algorithm model tailored was developed for terracing measures in high-resolution remote sensing imagery. Focusing on Fenxi County in Shanxi Province as the experimental target, in this research we conducted a comparative analysis between our proposed model and alternative models. The outcomes demonstrated that our refined algorithm model exhibited superior precision. Additionally, in this research we assessed the model’s generalization capability by utilizing Wafangdian City in Liaoning Province as another experimental target and performed a comparative analysis with human interpretation. The findings revealed that our model possesses enhanced generalization ability and can substantially augment interpretation efficiency. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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12 pages, 9540 KiB  
Article
Estimation of Soil Cations Based on Visible and Near-Infrared Spectroscopy and Machine Learning
by Yiping Peng, Ting Wang, Shujuan Xie, Zhenhua Liu, Chenjie Lin, Yueming Hu, Jianfang Wang and Xiaoyun Mao
Agriculture 2023, 13(6), 1237; https://doi.org/10.3390/agriculture13061237 - 13 Jun 2023
Cited by 1 | Viewed by 998
Abstract
Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate and efficient acquisition of information on soil cation content is of great importance for the monitoring of soil quality and pollution prevention. At present, few scholars focus [...] Read more.
Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate and efficient acquisition of information on soil cation content is of great importance for the monitoring of soil quality and pollution prevention. At present, few scholars focus on soil exchangeable cations using remote sensing technology. This study proposes a new method for estimating soil cation content using hyperspectral data. In particular, we introduce Boruta and successive projection (SPA) algorithms to screen feature variables, and we use Guangdong Province, China, as the study area. The backpropagation neural network (BPNN), genetic algorithm–based back propagation neural network (GABP) and random forest (RF) algorithms with 10-fold cross-validation are implemented to determine the most accurate model for soil cation (Ca2+, K+, Mg2+, and Na+) content estimations. The model and hyperspectral images are combined to perform the spatial mapping of soil Mg2+ and to obtain the spatial distribution information of images. The results show that Boruta was the optimal algorithm for determining the characteristic bands of soil Ca2+ and Na+, and SPA was the optimal algorithm for determining the characteristic bands of soil K+ and Mg2+. The most accurate estimation models for soil Ca2+, K+, Mg2+, and Na+ contents were Boruta-RF, SPA-GABP, SPA-RF and Boruta-RF, respectively. The estimation effect of soil Mg2+ (R2 = 0.90, ratio of performance to interquartile range (RPIQ) = 3.84) was significantly better than the other three elements (Ca2+: R2 = 0.83, RPIQ = 2.47; K+: R2 = 0.83, RPIQ = 2.58; Na+: R2 = 0.85, RPIQ = 2.63). Moreover, the SPA-RF method combined with HJ-1A HSI images was selected for the spatial mapping of soil Mg2+ content with an R2 of 0.71 and RPIQ of 2.05. This indicates the ability of the SPA-RF method to retrieve soil Mg2+ content at the regional scale. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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14 pages, 3211 KiB  
Article
Estimation of Productivity and Above-Ground Biomass for Corn (Zea mays) via Vegetation Indices in Madeira Island
by Fabrício Lopes Macedo, Humberto Nóbrega, José G. R. de Freitas, Carla Ragonezi, Lino Pinto, Joana Rosa and Miguel A. A. Pinheiro de Carvalho
Agriculture 2023, 13(6), 1115; https://doi.org/10.3390/agriculture13061115 - 24 May 2023
Cited by 3 | Viewed by 3442
Abstract
The advancement of technology associated with the field, especially the use of unmanned aerial vehicles (UAV) coupled with multispectral cameras, allows us to monitor the condition of crops in real time and contribute to the field of machine learning. The objective of this [...] Read more.
The advancement of technology associated with the field, especially the use of unmanned aerial vehicles (UAV) coupled with multispectral cameras, allows us to monitor the condition of crops in real time and contribute to the field of machine learning. The objective of this study was to estimate both productivity and above-ground biomass (AGB) for the corn crop by applying different vegetation indices (VIs) via high-resolution aerial imagery. Among the indices tested, strong correlations were obtained between productivity and the normalized difference vegetation index (NDVI) with a significance level of p < 0.05 (0.719), as well as for the normalized difference red edge (NDRE), or green normalized difference vegetation index (GNDVI) with crop productivity (p < 0.01), respectively 0.809 and 0.859. The AGB results align with those obtained previously; GNDVI and NDRE showed high correlations, but now with a significance level of p < 0.05 (0.758 and 0.695). Both GNDVI and NDRE indices showed coefficients of determination for productivity and AGB estimation with 0.738 and 0.654, and 0.701 and 0.632, respectively. The use of the GNDVI and NDRE indices shows excellent results for estimating productivity as well as AGB for the corn crop, both at the spatial and numerical levels. The possibility of predicting crop productivity is an essential tool for producers, since it allows them to make timely decisions to correct any deficit present in their agricultural plots, and further contributes to AI integration for drone digital optimization. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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19 pages, 4887 KiB  
Article
Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data
by Dexi Zhan, Yongqi Mu, Wenxu Duan, Mingzhu Ye, Yingqiang Song, Zhenqi Song, Kaizhong Yao, Dengkuo Sun and Ziqi Ding
Agriculture 2023, 13(5), 1088; https://doi.org/10.3390/agriculture13051088 - 19 May 2023
Viewed by 1414
Abstract
Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the [...] Read more.
Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R2 = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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13 pages, 3567 KiB  
Article
Assessing the Within-Field Heterogeneity Using Rapid-Eye NDVI Time Series Data
by Jasper Mohr, Andreas Tewes, Hella Ahrends and Thomas Gaiser
Agriculture 2023, 13(5), 1029; https://doi.org/10.3390/agriculture13051029 - 09 May 2023
Cited by 1 | Viewed by 1547
Abstract
(1) Background: The relation between the sub-field heterogeneity of soil properties and high-resolution satellite time series data might help to explain spatiotemporal patterns of crop growth, but detailed field studies are seldom. (2) Methods: Normalized Difference Vegetation Index (NDVI) data derived from satellite [...] Read more.
(1) Background: The relation between the sub-field heterogeneity of soil properties and high-resolution satellite time series data might help to explain spatiotemporal patterns of crop growth, but detailed field studies are seldom. (2) Methods: Normalized Difference Vegetation Index (NDVI) data derived from satellite time series images were used to identify changes in the spatial distribution of winter triticale (×Triticosecale), winter rye (Secale cereale) and winter barley (Hordeum vulgare) growth (2015 to 2020) for a field in north-eastern Germany. NDVI patterns (quartiles) that remained persistent over time were identified and it was tested if spatially heterogeneous soil characteristics such as water holding capacity and altitude could explain them. (3) Results: A statistically significant relationship between elevation and soil classes with NDVI values was found in most cases. The lowest NDVI quartiles, considered as representing the poorest growth conditions, were generally found in the depressions with the lowest water holding capacity. These areas showed temporally stable spatial patterns, especially during the pre-harvest period. Over the six-year period, up to 80% of the grid cells with the lowest NDVI values were spatially consistent over time. Differences in the climatic water balance were rather low but could contribute to explaining spatial patterns, such as the lower clustering of values in the wettest year. (4) Conclusions: High-resolution satellite NDVI time series are a valuable information source for precise land management in order to optimize crop management with respect to yield and ecosystem services. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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15 pages, 2067 KiB  
Article
A New Method for Estimating Irrigation Water Use via Soil Moisture
by Liming Zhu, Zhangze Gu, Guizhi Tian and Jiahao Zhang
Agriculture 2023, 13(4), 757; https://doi.org/10.3390/agriculture13040757 - 24 Mar 2023
Cited by 2 | Viewed by 1647
Abstract
The ability to obtain an accurate measure of irrigation water use is urgently needed in order to provide further scientific guidance for irrigation practices. This investigation took soil moisture and precipitation as the study objects and quantitatively analyzed their relationship by establishing four [...] Read more.
The ability to obtain an accurate measure of irrigation water use is urgently needed in order to provide further scientific guidance for irrigation practices. This investigation took soil moisture and precipitation as the study objects and quantitatively analyzed their relationship by establishing four models: a linear model, a logarithmic model, a soil water balance model, and a similarity model. The results from building models on every site clearly revealed the relationship between soil moisture and precipitation and confirmed the feasibility of estimating irrigation water use when soil moisture data are known. Four models combined with soil moisture data were used to estimate irrigation water use. First, the 16 sites which monitor soil moisture conditions in Hebi City were identified as study objects, from which everyday meteorological data (temperature, precipitation, atmospheric pressure, wind speed, sunshine duration) and soil moisture data from 2015 to 2020 (totaling six years) were collected. Second, the eligible data from the first four years in the date range were used to create four kinds of models (linear model, logarithmic model, soil water balance model, and similarity model) to estimate the amount of water input to the soil surface based on soil moisture. Third, the eligible data from the last two years in the established date range were used to verify the established models on every site and then judge the accuracy of the models. For example, for site 53990, the RMSE of the linear model, logarithmic model, soil water balance model, and similarity model was 10,547, 10,302, 8619, and 7524, respectively. The results demonstrate that the similarity model proposed in this study can express the quantitative relationship between soil moisture and precipitation more accurately than the other three models. Based on this conclusion, the eligible soil moisture data known in the specific site were ultimately used to estimate the irrigation water use in the field by the relationship expressed in the similarity model. Compared with the amount of irrigation water data recorded, the estimated irrigation water use yielded by the similarity model in this study was 18.11% smaller. In a future study, microwave satellite remote sensing of soil moisture data, such as SMAP and SMOS soil moisture data, will be used to evaluate the performance of estimated regional irrigation water use. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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17 pages, 12494 KiB  
Article
Assessment of NDVI Dynamics of Maize (Zea mays L.) and Its Relation to Grain Yield in a Polyfactorial Experiment Based on Remote Sensing
by András Tamás, Elza Kovács, Éva Horváth, Csaba Juhász, László Radócz, Tamás Rátonyi and Péter Ragán
Agriculture 2023, 13(3), 689; https://doi.org/10.3390/agriculture13030689 - 15 Mar 2023
Cited by 2 | Viewed by 2065
Abstract
Remote sensing is an efficient tool to detect vegetation heterogeneity and dynamics of crop development in real-time. In this study, the performance of three maize hybrids (Fornad FAO-420, Merida FAO-380, and Corasano FAO-490-510) was monitored as a function of nitrogen dose (0, 80 [...] Read more.
Remote sensing is an efficient tool to detect vegetation heterogeneity and dynamics of crop development in real-time. In this study, the performance of three maize hybrids (Fornad FAO-420, Merida FAO-380, and Corasano FAO-490-510) was monitored as a function of nitrogen dose (0, 80 and 160 kg N ha−1), soil tillage technologies (winter ploughing, strip-tillage, and ripping), and irrigation (rainfed and 3x25 mm) in a warm temperature dry region of East-Central Europe. Dynamics of the Normalized Difference Vegetation Index (NDVI) were followed in the vegetation period of 2021, a year of drought, by using sensors mounted on an unmanned aerial vehicle. N-fertilization resulted in significantly higher NDVI throughout the entire vegetation period (p < 0.001) in each experimental combination. A significant positive effect of irrigation was observed on the NDVI during the drought period (77–141 days after sowing). For both the tillage technologies and hybrids, NDVI was found to be significantly different between treatments, but showing different dynamics. Grain yield was in strong positive correlation with the NDVI between the late vegetative and the early generative stages (r = 0.80–0.84). The findings suggest that the NDVI dynamics is an adequate indicator for evaluating the impact of different treatments on plant development and yield prediction. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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16 pages, 6371 KiB  
Article
Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning
by Fan Ding, Changchun Li, Weiguang Zhai, Shuaipeng Fei, Qian Cheng and Zhen Chen
Agriculture 2022, 12(11), 1752; https://doi.org/10.3390/agriculture12111752 - 23 Oct 2022
Cited by 8 | Viewed by 2246
Abstract
Nitrogen (N) is an important factor limiting crop productivity, and accurate estimation of the N content in winter wheat can effectively monitor the crop growth status. The objective of this study was to evaluate the ability of the unmanned aerial vehicle (UAV) platform [...] Read more.
Nitrogen (N) is an important factor limiting crop productivity, and accurate estimation of the N content in winter wheat can effectively monitor the crop growth status. The objective of this study was to evaluate the ability of the unmanned aerial vehicle (UAV) platform with multiple sensors to estimate the N content of winter wheat using machine learning algorithms; to collect multispectral (MS), red-green-blue (RGB), and thermal infrared (TIR) images to construct a multi-source data fusion dataset; to predict the N content in winter wheat using random forest regression (RFR), support vector machine regression (SVR), and partial least squares regression (PLSR). The results showed that the mean absolute error (MAE) and relative root-mean-square error (rRMSE) of all models showed an overall decreasing trend with an increasing number of input features from different data sources. The accuracy varied among the three algorithms used, with RFR achieving the highest prediction accuracy with an MAE of 1.616 mg/g and rRMSE of 12.333%. For models built with single sensor data, MS images achieved a higher accuracy than RGB and TIR images. This study showed that the multi-source data fusion technique can enhance the prediction of N content in winter wheat and provide assistance for decision-making in practical production. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Agricultural Crop and Soil Monitoring)
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