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Remote Sensing of Crop Lands and Crop Production

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 29095

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Special Issue Editors

Dr. Roger Lawes

E-Mail Website
Guest Editor

Centre for Environment and Life Sciences, CSIRO, Floreat, WA 6014, Australia
Interests: agricultural crops and pastures; farming systems; remote sensing; crop modelling; applied statistics

Dr. Aniruddha Ghosh

E-Mail Website
Guest Editor

Climate Action Research, Alliance of Bioversity International and CIAT, Nairobi, Kenya
Interests: remote sensing for agriculture production and monitoring; agriculture and climate change; crop modeling; finance and investment in agriculture; conservation; spatial data science

Special Issue Information

Dear Colleagues,

Since the development of the normalised difference vegetation index, scientists have realised that, globally, crops could be monitored with this surrogate indicator of plant health. Platforms such as Landsat and MODIS provide reliable, but occasionally fragmented, indicators of production. National crops are monitored to assist countries with managing the global food supply, and these efforts have evolved into products such as the GEOGLAM crop monitor.

In recent years, new satellites have become available, with higher resolutions and faster repeat times. The list of available satellites is growing, and SAR platforms can augment optical platforms. Constellations of micro-satellites now allow information to be collected at a high frequency with a very fine resolution. New platforms such as Google Earth Engine enable the sensing data to be utilised cheaply and efficiently so as to monitor crop production.

Yet, despite these advances, the remote sensing of crops must still overcome many challenges. Small-holder farms and fields can be difficult to monitor. Field boundaries need to be generated en-masse in order to enable researchers to switch to object-based classifications. The need for training data to both build and test models challenges every remote sensing scientist. Practicing agriculturalists also need to monitor crops in order to determine how to optimally treat and manage a field, and they require a remote sensing output to be delivered in a timely fashion on mobile devices. Thus, as the capability to monitor crops improves, the demand for new sensing products from industry increases. Factors such as timeliness, speed, computational efficiency, and coping with sparse data can all confound the efforts to sense crops. This Special Issue of Remote Sensing seeks to showcase the latest research in this important area, that will ultimately help us as a species to monitor and manage the global food supply. I look forward to receiving your contributions.

Dr. Roger Lawes
Dr. Aniruddha Ghosh
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Crop modelling
Agricultural landscapes
Remote sensing
Food security
Crop forecasting
Biotic and abiotic stress

Published Papers (10 papers)

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Research

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18 pages, 1517 KiB

Open AccessArticle

SC-CAN: Spectral Convolution and Channel Attention Network for Wheat Stress Classification

by Wijayanti Nurul Khotimah, Farid Boussaid, Ferdous Sohel, Lian Xu, David Edwards, Xiu Jin and Mohammed Bennamoun

Remote Sens. 2022, 14(17), 4288; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174288 - 30 Aug 2022

Cited by 2 | Viewed by 1656

Abstract

Biotic and abiotic plant stress (e.g., frost, fungi, diseases) can significantly impact crop production. It is thus essential to detect such stress at an early stage before visual symptoms and damage become apparent. To this end, this paper proposes a novel deep learning method, called Spectral Convolution and Channel Attention Network (SC-CAN), which exploits the difference in spectral responses of healthy and stressed crops. The proposed SC-CAN method comprises two main modules: (i) a spectral convolution module, which consists of dilated causal convolutional layers stacked in a residual manner to capture the spectral features; (ii) a channel attention module, which consists of a global pooling layer and fully connected layers that compute inter-relationship between feature map channels before scaling them based on their importance level (attention score). Unlike standard convolution, which focuses on learning local features, the dilated convolution layers can learn both local and global features. These layers also have long receptive fields, making them suitable for capturing long dependency patterns in hyperspectral data. However, because not all feature maps produced by the dilated convolutional layers are important, we propose a channel attention module that weights the feature maps according to their importance level. We used SC-CAN to classify salt stress (i.e., abiotic stress) on four datasets (Chinese Spring (CS), Aegilops columnaris (co(CS)), Ae. speltoides auchery (sp(CS)), and Kharchia datasets) and Fusarium head blight disease (i.e., biotic stress) on Fusarium dataset. Reported experimental results show that the proposed method outperforms existing state-of-the-art techniques with an overall accuracy of 83.08%, 88.90%, 82.44%, 82.10%, and 82.78% on CS, co(CS), sp(CS), Kharchia, and Fusarium datasets, respectively. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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19 pages, 6230 KiB

Open AccessArticle

Identification of Cultivated Land Quality Grade Using Fused Multi-Source Data and Multi-Temporal Crop Remote Sensing Information

by Yinshuai Li, Chunyan Chang, Zhuoran Wang, Tao Li, Jianwei Li and Gengxing Zhao

Remote Sens. 2022, 14(9), 2109; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092109 - 27 Apr 2022

Cited by 9 | Viewed by 2103

Abstract

To explore the fast, accurate, and efficient remote sensing identification method of cultivated land quality, this study took Shandong Province as the study area, and used measured data to carry out the soil quality evaluation based on conventional GIS. On this basis, MODIS sequence images were used as remote sensing data sources, and multi-source data such as topography, meteorology, and statistical yearbook were fused. Then, according to the Pressure-State-Response framework, we constructed three kinds of characteristic indicators through distinguishing crop rotation types and fusing remote sensing data. Finally, the soil quality grade was identified by the random forest method, and the accuracy analysis was carried out. The results showed that the NDVI peak values of double-season crops are in mid-April and mid-August, and one-season crops are in mid-August. Through evaluation, soil quality was divided into three categories, with six grades. Through principal component analysis, each soil status indicator contains two to three principal components, and each principal component contains five to eight temporal crop remote sensing information. After distinguishing crop rotation types and fusing remote sensing images, the identification accuracy of soil quality is significantly improved. The overall accuracy is 79.18%, 86.12%, and 93.65%, and the Kappa coefficient is 0.66, 0.77, and 0.90, respectively. This research developed an automatic identification method for cultivated land quality grade, and it proved that distinguishing crop rotation types and fusing multi-temporal crop remote sensing information are effective ways to improve identification accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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17 pages, 624 KiB

Open AccessArticle

Field Data Collection Methods Strongly Affect Satellite-Based Crop Yield Estimation

by Kate Tiedeman, Jordan Chamberlin, Frédéric Kosmowski, Hailemariam Ayalew, Tesfaye Sida and Robert J. Hijmans

Remote Sens. 2022, 14(9), 1995; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14091995 - 21 Apr 2022

Cited by 5 | Viewed by 2831

Abstract

Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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21 pages, 3814 KiB

Open AccessArticle

Winter Wheat Yield Estimation Based on Optimal Weighted Vegetation Index and BHT-ARIMA Model

by Qiuzhuo Deng, Mengxuan Wu, Haiyang Zhang, Yuntian Cui, Minzan Li and Yao Zhang

Remote Sens. 2022, 14(9), 1994; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14091994 - 21 Apr 2022

Cited by 4 | Viewed by 1733

Abstract

This study aims to use remote sensing (RS) time-series data to explore the intrinsic relationship between crop growth and yield formation at different fertility stages and construct a high-precision winter wheat yield estimation model applicable to short time-series RS data. Sentinel-2 images were acquired in this study at six key phenological stages (rejuvenation stage, rising stage, jointing stage, heading stage, filling stage, filling-maturity stage) of winter wheat growth, and various vegetation indexes (VIs) at different fertility stages were calculated. Based on the characteristics of yield data continuity, the RReliefF algorithm was introduced to filter the optimal vegetation index combinations suitable for the yield estimation of winter wheat for all fertility stages. The Absolutely Objective Improved Analytic Hierarchy Process (AOIAHP) was innovatively proposed to determine the proportional contribution of crop growth to yield formation in six different phenological stages. The selected VIs consisting of MTCI(RE2), EVI, REP, MTCI(RE1), RECI(RE1), NDVI(RE1), NDVI(RE3), NDVI(RE2), NDVI, and MSAVI were then fused with the weights of different fertility periods to obtain time-series weighted data. For the characteristics of short time length and a small number of sequences of RS time-series data in yield estimation, this study applied the multiplexed delayed embedding transformation (MDT) technique to realize the data augmentation of the original short time series. Tucker decomposition was performed on the block Hankel tensor (BHT) obtained after MDT enhancement, and the core tensor was extracted while preserving the intrinsic connection of the time-series data. Finally, the resulting multidimensional core tensor was trained with the Autoregressive Integrated Moving Average (ARIMA) model to obtain the BHT-ARIMA model for wheat yield estimation. Compared to the performance of the BHT-ARIMA model with unweighted time-series data as input, the weighted time-series input significantly improves yield estimation accuracy. The coefficients of determination (R²) were improved from 0.325 to 0.583. The root mean square error (RMSE) decreased from 492.990 to 323.637 kg/ha, the mean absolute error (MAE) dropped from 350.625 to 255.954, and the mean absolute percentage error (MAPE) decreased from 4.332% to 3.186%. Besides, BHT-ARMA and BHT-CNN models were also used to compare with BHT-ARIMA. The results indicated that the BHT-ARIMA model still had the best yield prediction accuracy. The proposed method of this study will provide fast and accurate guidance for crop yield estimation and will be of great value for the processing and application of time-series RS data. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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19 pages, 8961 KiB

Open AccessArticle

Modelling Within-Season Variation in Light Use Efficiency Enhances Productivity Estimates for Cropland

by Michael J. Wellington, Petra Kuhnert, Luigi J. Renzullo and Roger Lawes

Remote Sens. 2022, 14(6), 1495; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061495 - 20 Mar 2022

Cited by 9 | Viewed by 2446

Abstract

Gross Primary Productivity (GPP) for cropland is often estimated using a fixed value for maximum light use efficiency (LUE_max) which is reduced to light use efficiency (LUE) by environmental stress scalars. This may not reflect variation in LUE within a crop season, and environmental stress scalars developed for ecosystem scale modelling may not apply linearly to croplands. We predicted LUE on several vegetation indices, crop type, and agroclimatic predictors using supervised random forest regression with training data from flux towers. Using a fixed LUE_max and environmental stress scalars produced an overestimation of GPP with a root mean square error (RMSE) of 6.26 gC/m²/day, while using predicted LUE from random forest regression produced RMSEs of 0.099 and 0.404 gC/m²/day for models with and without crop type as a predictor, respectively. Prediction uncertainty was greater for the model without crop type. These results show that LUE varies between crop type, is dynamic within a crop season, and LUE models that reflect this are able to produce much more accurate estimates of GPP over cropland than using fixed LUE_max with stress scalars. Therefore, we suggest a paradigm shift from setting the LUE variable in cropland productivity models based on environmental stress to focusing more on the variation of LUE within a crop season. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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18 pages, 12872 KiB

Open AccessArticle

Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

by Dan Li, Yuxin Miao, Sanjay K. Gupta, Carl J. Rosen, Fei Yuan, Chongyang Wang, Li Wang and Yanbo Huang

Remote Sens. 2021, 13(16), 3322; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163322 - 22 Aug 2021

Cited by 28 | Viewed by 5047

Abstract

Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R² = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R² = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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20 pages, 2055 KiB

Open AccessArticle

Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning

by Fiona H. Evans and Jianxiu Shen

Remote Sens. 2021, 13(13), 2435; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132435 - 22 Jun 2021

Cited by 11 | Viewed by 3096

Abstract

Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to identify the timing of phenological events from sequences of Landsat NDVI and derive phenological and seasonal climate metrics. Using data from a 17,000 ha study area, we investigated the relationships between the metrics and yield over 17 years from 2003 to 2019. We compared six statistical and machine learning models for estimating yield: multiple linear regression, mixed effects models, generalised additive models, random forests, support vector regression using radial basis functions and deep learning neural networks. We used a 50-50 train-test split on paddock-years where 50% of paddock-year combinations were randomly selected and used to train each model and the remaining 50% of paddock-years were used to assess the model accuracy. Using only phenological metrics, accuracy was highest using a linear mixed model with a random effect that allowed the relationship between integrated NDVI and yield to vary by year

(R^{2}

= 0.67, MAE = 0.25 t ha⁻¹, RMSE = 0.33 t ha⁻¹, NRMSE = 0.25). We quantified the improvements in accuracy when seasonal climate metrics were also used as predictors. We identified two optimal models using the combined phenological and seasonal climate metrics: support vector regression and deep learning models (

R^{2}

= 0.68, MAE = 0.25 t ha⁻¹, RMSE = 0.32 t ha⁻¹, NRMSE = 0.25). While the linear mixed model using only phenological metrics performed similarly to the nonlinear models that are also seasonal climate metrics, the nonlinear models can be more easily generalised to estimate yield in years for which training data are unavailable. We conclude that long-term hindcasts of wheat yield in fields, at 30 m spatial resolution, can be produced using remotely sensed phenology from Landsat NDVI, climate data and machine learning. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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18 pages, 64715 KiB

Open AccessArticle

Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI

by Fiona H. Evans and Jianxiu Shen

Remote Sens. 2021, 13(11), 2128; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112128 - 28 May 2021

Cited by 1 | Viewed by 2304

Abstract

Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. With a 40-year record of 30 m resolution images and 16-day revisits, the Landsat satellite series is ideal for producing long-term records of remotely sensed phenology to build understanding of how climate affects crop growth. However, the time-series of Landsat images exhibits gaps caused by cloud cover, which is common in wet periods when crops reach maximum growth. We propose a novel spatial–temporal approach to gap-filling that avoids data fusion. Crop growth curve estimation is used to perform temporal smoothing and incorporation of spatial weights allows spatial smoothing. We tested our approach using Landsat NDVI data acquired for an 8000 ha study area in Western Australia using a train/test approach where 157 available Landsat-7 images between 2013 and 2019 were used to train the model, and 95 at least 80% cloud-free Landsat-8 images from the same period were used to test its performance. We found that compared to nonspatial estimation, use of spatial weights in growth curve estimation improved correlation between observed and predicted NDVI by 75%, MAE by 31% and RMSE by 75%. For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. We conclude that spatially weighted estimation of crop growth curves can be used to fill spatial and temporal gaps in Landsat NDVI for the purpose of within-field monitoring. Our approach is also applicable to other data sources and vegetation indices. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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Other

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12 pages, 3533 KiB

Open AccessTechnical Note

Comparing Methods to Extract Crop Height and Estimate Crop Coefficient from UAV Imagery Using Structure from Motion

by Nitzan Malachy, Imri Zadak and Offer Rozenstein

Remote Sens. 2022, 14(4), 810; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040810 - 09 Feb 2022

Cited by 12 | Viewed by 2577

Abstract

Although it is common to consider crop height in agricultural management, variation in plant height within the field is seldom addressed because it is challenging to assess from discrete field measurements. However, creating spatial crop height models (CHMs) using structure from motion (SfM) applied to unmanned aerial vehicle (UAV) imagery can easily be done. Therefore, looking into intra- and inter-season height variability has the potential to provide regular information for precision management. This study aimed to test different approaches to deriving crop height from CHM and subsequently estimate the crop coefficient (K_c). CHMs were created for three crops (tomato, potato, and cotton) during five growing seasons, in addition to manual height measurements. The K_c time-series were derived from eddy-covariance measurements in commercial fields and estimated from multispectral UAV imagery in small plots, based on known relationships between K_c and spectral vegetation indices. A comparison of four methods (Mean, Sample, Median, and Peak) was performed to derive single height values from CHMs. Linear regression was performed between crop height estimations from CHMs against manual height measurements and K_c. Height was best predicted using the Mean and the Sample methods for all three crops (R² = 0.94, 0.84, 0.74 and RMSE = 0.056, 0.071, 0.051 for cotton, potato, and tomato, respectively), as was the prediction of K_c (R² = 0.98, 0.84, 0.8 and RMSE = 0.026, 0.049, 0.023 for cotton, potato, and tomato, respectively). The Median and Peak methods had far less success in predicting both, and the Peak method was shown to be sensitive to the size of the area analyzed. This study shows that CHMs can help growers identify spatial heterogeneity in crop height and estimate the crop coefficient for precision irrigation applications. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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23 pages, 62855 KiB

Open AccessTechnical Note

Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots

by Michal Polák, Jakub Miřijovský, Alba E. Hernándiz, Zdeněk Špíšek, Radoslav Koprna and Jan F. Humplík

Remote Sens. 2021, 13(16), 3169; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163169 - 10 Aug 2021

Cited by 2 | Viewed by 2683

Abstract

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m

^{2}

) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a

0.78

0.79

correlation with LiDAR measurement with manual validation (RMSE =

0.19

) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)

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