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16 pages, 4039 KiB

Open AccessArticle

Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District

by Lei Jiang, Yuting Yang and Songhao Shang

Remote Sens. 2022, 14(9), 2035; https://doi.org/10.3390/rs14092035 - 23 Apr 2022

Cited by 6 | Viewed by 2681

Abstract

Quantitative assessment of crop water-use efficiency (WUE) is an important basis for high-efficiency use of agricultural water. Here we assess the WUE of maize in the Hetao Irrigation District, which is a representative irrigation district in the arid region of Northwest China. Specifically, [...] Read more.

Quantitative assessment of crop water-use efficiency (WUE) is an important basis for high-efficiency use of agricultural water. Here we assess the WUE of maize in the Hetao Irrigation District, which is a representative irrigation district in the arid region of Northwest China. Specifically, we firstly mapped the location of the maize field by using a remote sensing/phenological–based vegetation classifier and then quantified the maize water use and yield by using a dual-source remote-sensing evapotranspiration (ET) model and a crop water production function, respectively. Validation results show that the adopted phenological-based vegetation classifier performed well in mapping the spatial distributions and inter-annual variations of maize planting, with a kappa coefficient of 0.86. In addition, the ET model based on the hybrid dual-source scheme and trapezoid framework also obtained high accuracy in spatiotemporal ET mapping, with an RMSE of 0.52 mm/day at the site scale and 26.21 mm/year during the maize growing season (April–October) at the regional scale. Further, the adopted crop water production function showed high accuracy in estimating the maize yield, with a mean relative error of only 4.3%. Using the estimated ET, transpiration, and yield of maize, the mean maize WUE based on ET and transpiration in the study region were1.94 kg/m³ and 3.06 kg/m³, respectively. Our results demonstrate the usefulness and validity of remote sensing information in mapping regional crop WUE. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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16 pages, 5530 KiB

Open AccessArticle

Retrospective Predictions of Rice and Other Crop Production in Madagascar Using Soil Moisture and an NDVI-Based Calendar from 2010–2017

by Angela J. Rigden, Christopher Golden and Peter Huybers

Remote Sens. 2022, 14(5), 1223; https://doi.org/10.3390/rs14051223 - 2 Mar 2022

Cited by 8 | Viewed by 5777

Abstract

Malagasy subsistence farmers, who comprise 70% of the nearly 26 million people in Madagascar, often face food insecurity because of unreliable food production systems and adverse crop conditions. The 2020–2021 drought in Madagascar, in particular, is associated with an exceptional food crisis, yet [...] Read more.

Malagasy subsistence farmers, who comprise 70% of the nearly 26 million people in Madagascar, often face food insecurity because of unreliable food production systems and adverse crop conditions. The 2020–2021 drought in Madagascar, in particular, is associated with an exceptional food crisis, yet we are unaware of peer-reviewed studies that quantitatively link variations in weather and climate to agricultural outcomes for staple crops in Madagascar. In this study, we use historical data to empirically assess the relationship between soil moisture and food production. Specifically, we focus on major staple crops that form the foundation of Malagasy food systems and nutrition, including rice, which accounts for 46% of the average Malagasy caloric intake, as well as cassava, maize, and sweet potato. Available data associated with survey-based crop statistics constrain our analysis to 2010–2017 across four clusters of Malagasy districts. Strong correlations are observed between remotely sensed soil moisture and rice production, ranging between 0.67 to 0.95 depending on the cluster and choice of crop calendar. Predictions are shown to be statistically significant at the 90% confidence level using bootstrapping techniques, as well as through an out-of-sample prediction framework. Soil moisture also shows skill in predicting cassava, maize, and sweet potato production, but only when the months most vulnerable to water stress are isolated. Additional analyses using more survey data, as well as potentially more-refined crop maps and calendars, will be useful for validating and improving soil-moisture-based predictions of yield. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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Graphical abstract
Full article ">Figure 1
Satellite-based annual average (a) soil moisture and (b) vegetation activity (NDVI) from 2010 to 2017. Districts are outlined on each map. Full article ">Figure 2
Survey-based average crop production (in tonnes) for each district, including (a) rice, (b) cassava, (c) maize, and (d) sweet potato and the associated harvested area fractions for (e) rice, (f) cassava, (g) maize, and (h) sweet potato. The harvested area fraction represents the average fractional proportion of a 0.05-degree grid box that was harvested in a crop during the 1997–2003 era [<a href="#B37-remotesensing-14-01223" class="html-bibr">37</a>]. In (e–h), grid boxes with harvested area fractions less than 0.001 are colored gray. Full article ">Figure 3
The four calendars used to temporally average soil moisture anomalies for comparison with rice production anomalies (see <a href="#app1-remotesensing-14-01223" class="html-app">Figures S5–S7</a> for analogous plots of cassava, maize, and sweet potato). The top row represents the starting month of the temporal average for calendars (a) C1, (b) C2, (c) C3, and (d) C4. The bottom represents the ending month of the temporal average for calendars (e) C1, (f) C2, (g) C3, and (h) C4. C1 and C2 represent mean planting (start) and mean harvesting (end) dates, while C3 and C4 represent temporal periods based on NDVI. For C2 (b,f), we have mapped the rainy season calendar (see <a href="#app1-remotesensing-14-01223" class="html-app">Figure S8</a> for separate maps of the rainy and dry seasons and <a href="#app1-remotesensing-14-01223" class="html-app">Supplementary Data 1</a> for details on C2). The starting month for C3 (c) and C4 (d) are identical, as they represent the minimum in NDVI after the climatological seasonal cycle is smoothed with a 5-month moving window. For all calendars, the starting month can be in the year prior to harvest. Full article ">Figure 4
Districts are grouped into four climate regions based on the average soil moisture within the district from 2010 to 2017. We refer to these regions from low to high soil moisture as “arid”, “semi-arid”, “semi-wet”, and “wet”. Full article ">Figure 5
Observations and predictions of rice production anomalies (tonnes) in the (a) arid, (b) semi-arid, (c) semi-wet, and (d) wet climate-zones. Note that the y-axis of each subplot has a different scale. Anomalies in production are computed as a deviation from a least-squares fit linear trend (see <a href="#remotesensing-14-01223-t001" class="html-table">Table 1</a> for the average total production in each region). Predictions are made using the optimal growing season length (<math display="inline"><semantics> <msubsup> <mover> <mi>SM</mi> <mo>¯</mo> </mover> <mrow> <mi mathvariant="normal">C</mi> <mn>4</mn> </mrow> <mo>′</mo> </msubsup> </semantics></math>) following Equation (<a href="#FD1-remotesensing-14-01223" class="html-disp-formula">1</a>). Observations are plotted with a solid line and predictions with a dotted line. See <a href="#app1-remotesensing-14-01223" class="html-app">Figures S12–S14</a> for analogous plots for maize, cassava, and sweet potatoes. Full article ">

22 pages, 3921 KiB

Open AccessArticle

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

by Shengwei Liu, Dailiang Peng, Bing Zhang, Zhengchao Chen, Le Yu, Junjie Chen, Yuhao Pan, Shijun Zheng, Jinkang Hu, Zihang Lou, Yue Chen and Songlin Yang

Remote Sens. 2022, 14(4), 893; https://doi.org/10.3390/rs14040893 - 13 Feb 2022

Cited by 26 | Viewed by 5308

Abstract

The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the [...] Read more.

The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding-tillering, overwintering, reviving, jointing-heading, and flowering-maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries-Matusita (J-M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing-heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing-heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing-heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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Figure 1
Geographical location of the study area: (a) Distribution of sample points. The main map is based on the 30-m global land cover product fine classification system for 2020, (b) Annual cumulative precipitation data in 2020, (c) Monthly average temperature data in 2020. Full article ">Figure 2
Results for the accuracy achieved using the random forest algorithm. S, O, R, J and M stand for the seeding-tillering, overwintering, reviving, joining-heading and flowering-maturing periods, respectively. The user accuracy (UA) (blue), producer accuracy (PA) (yellow) and overall accuracy (OA) (purple) are plotted on the left-hand vertical axis. The kappa coefficient (pink) is plotted on the right-hand vertical axis. Full article ">Figure 3
Winter wheat spatial distribution from five growth periods and their difference produced by the random forest classification. (a) The spatial distribution of winter wheat at the jointing-heading stage (reference image), it was subtracted from the winter wheat classification corresponding to other periods to generate the difference maps (b–e). In the bar charts, I, II and III represent the ratio of the number of correctly predicted, wrongly predicted and ‘missing’ winter wheat pixels to the union of the number of predicted winter wheat pixels and the true number, respectively. The red square, circle, triangle and pentagon represent the locations of the subsets shown in <a href="#remotesensing-14-00893-f004" class="html-fig">Figure 4</a>. Full article ">Figure 4
Results for the identification of winter wheat in four representative areas of the study area using random forest classification: (a,b) represent, respectively, winter wheat areas with dense planting, flat terrain; (c,d) represent, respectively, winter wheat areas with fragmented land use and complex terrain (Sentinel-2 false color composite images, bands 8/2/3). Parts (1) to (5) in each row show the pixels identified as winter wheat using the random forest classification during the seeding-tillering, overwintering, reviving and jointing-heading period, respectively. Full article ">Figure 5
Accuracy and loss functions of the training and validation sets for different growth periods. The black curve represents the training set accuracy, the red curve represents the validation set accuracy, the blue curve represents the loss function of the training set and the green curve represents the loss function of the validation set. The dotted line marks the number of training epochs corresponding to the optimal weight. Full article ">Figure 6
Accuracy of the identification of winter wheat using the test set. S, O, R, J and M represent the seeding-tillering, overwintering, reviving, jointing-heading and flowering-maturing periods, respectively. The numbers in bold indicate the best value for each evaluation index. Full article ">Figure 7
Results of the semantic segmentation of winter wheat using the test set. I-IV, respectively, represent winter wheat planting areas in an area of flat terrain, an area with multiple land-cover types, an area of fragmented terrain and an area of complex terrain. (a) True color Sentinel-2 composite images; (b) corresponding sample labels. (c–g), respectively, show the results of the semantic segmentation of the sample data for the seeding-tillering, overwintering, rejuvenation, jointing-heading and flowering-maturing periods. Full article ">Figure 8
Mapping of winter wheat based on deep learning classification. (a) U-Net classification map of winter wheat for the jointing-heading period (reference image); it was subtracted from the winter wheat classification map corresponding to other periods to generate difference maps (b–e). In the bar charts, I, II and III represents the ratio of the number of correctly predicted, wrongly predicted and ‘missing’ winter wheat pixels to the union of the number of predicted winter wheat pixels and the true number, respectively. Full article ">

22 pages, 11735 KiB

Open AccessArticle

Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1/2 Imagery in the Yangtze-Huai River Basin

by Miao Li, Tao Zhang, Ying Tu, Zhehao Ren and Bing Xu

Remote Sens. 2022, 14(3), 690; https://doi.org/10.3390/rs14030690 - 1 Feb 2022

Cited by 13 | Viewed by 4672

Abstract

The increasingly frequent flooding imposes tremendous and long-lasting damages to lives and properties in impoverished rural areas. Rapid, accurate, and large-scale flood mapping is urgently needed for flood management, and to date has been successfully implemented benefiting from the advancement in remote sensing [...] Read more.

The increasingly frequent flooding imposes tremendous and long-lasting damages to lives and properties in impoverished rural areas. Rapid, accurate, and large-scale flood mapping is urgently needed for flood management, and to date has been successfully implemented benefiting from the advancement in remote sensing and cloud computing technology. Yet, the effects of agricultural emergency response to floods have been limitedly evaluated by satellite-based remote sensing, resulting in biased post-flood loss assessments. Addressing this challenge, this study presents a method for monitoring post-flood agricultural recovery using Sentinel-1/2 imagery, tested in three flood-affected main grain production areas, in the middle and lower Yangtze and Huai River, China. Our results indicated that 33~72% of the affected croplands were replanted and avoided total crop failures in summer 2020. Elevation, flood duration, crop rotation scheme, and flooding emergency management affect the post-flood recovery performance. The findings also demonstrate rapid intervention measures adjusted to local conditions could reduce the agricultural failure cost from flood disasters to a great extent. This study provides a new alternative for comprehensive disaster loss assessment in flood-prone agricultural regions, which will be insightful for worldwide flood control and management. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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21 pages, 8892 KiB

Open AccessArticle

Radiative Transfer Image Simulation Using L-System Modeled Strawberry Canopies

by Zhen Guan, Amr Abd-Elrahman, Vance Whitaker, Shinsuke Agehara, Benjamin Wilkinson, Jean-Philippe Gastellu-Etchegorry and Bon Dewitt

Remote Sens. 2022, 14(3), 548; https://doi.org/10.3390/rs14030548 - 24 Jan 2022

Cited by 5 | Viewed by 3434

Abstract

The image-based modeling and simulation of plant growth have numerous and diverse applications. In this study, we used image-based and manual field measurements to develop and validate a methodology to simulate strawberry (Fragaria × ananassa Duch.) plant canopies throughout the Florida strawberry [...] Read more.

The image-based modeling and simulation of plant growth have numerous and diverse applications. In this study, we used image-based and manual field measurements to develop and validate a methodology to simulate strawberry (Fragaria × ananassa Duch.) plant canopies throughout the Florida strawberry growing season. The simulated plants were used to create a synthetic image using radiative transfer modeling. Observed canopy properties were incorporated into an L-system simulator, and a series of strawberry canopies corresponding to specific weekly observation dates were created. The simulated canopies were compared visually with actual plant images and quantitatively with in-situ leaf area throughout the strawberry season. A simple regression model with L-system-derived and in-situ total leaf areas had an

A d j R^{2}

value of 0.78. The L-system simulated canopies were used to derive information needed for image simulation, such as leaf area and leaf angle distribution. Spectral and plant canopy information were used to create synthetic high spatial resolution multispectral images using the Discrete Anisotropic Radiative Transfer (DART) software. Vegetation spectral indices were extracted from the simulated image and used to develop multiple regression models of in-situ biophysical parameters (leaf area and dry biomass), achieving

A d j R^{2}

values of 0.63 and 0.50, respectively. The Normalized Difference Vegetation Index (NDVI) and the Red Edge Simple Ratio (SRre) vegetation indices, which utilize the red, red edge, and near infrared bands of the spectrum, were identified as statistically significant variables (p < 0.10). This study showed that both geometric (canopy seize metrics) and spectral variables were successful in modeling in-situ biomass and leaf area. Combining the geometric and spectral variables, however, only slightly improved the prediction model. These results show the feasibility of simulating strawberry canopies and images with inherent geometrical, topological, and spectral properties of real strawberry plants. The simulated canopies and images can be used in applications beyond creating realistic computer graphics for quantitative applications requiring the depiction of vegetation biological processes, such as stress modeling and remote sensing mission planning. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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18 pages, 3427 KiB

Open AccessArticle

Classification of Daily Crop Phenology in PhenoCams Using Deep Learning and Hidden Markov Models

by Shawn D. Taylor and Dawn M. Browning

Remote Sens. 2022, 14(2), 286; https://doi.org/10.3390/rs14020286 - 9 Jan 2022

Cited by 17 | Viewed by 4378

Abstract

Near-surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near-surface cameras to track the unique phenology of croplands. [...] Read more.

Near-surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near-surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images, our workflow incorporates a hidden Markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83–0.85, which improved to 0.86–0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local-scale dataset of field states and phenological stages for agricultural research. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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16 pages, 4204 KiB

Open AccessArticle

Integrating Sentinel-1/2 Data and Machine Learning to Map Cotton Fields in Northern Xinjiang, China

by Tao Hu, Yina Hu, Jianquan Dong, Sijing Qiu and Jian Peng

Remote Sens. 2021, 13(23), 4819; https://doi.org/10.3390/rs13234819 - 27 Nov 2021

Cited by 10 | Viewed by 3507

Abstract

Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. [...] Read more.

Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. Here, we proposed a new framework for mapping cotton fields based on Sentinel-1/2 data for different phenological periods, random forest classifiers, and the multi-scale image segmentation method. A cotton field map for 2019 at a spatial resolution of 10 m was generated for northern Xinjiang, a dominant cotton planting region in China. The overall accuracy and kappa coefficient of the map were 0.932 and 0.813, respectively. The results showed that the boll opening stage was the best phenological phase for mapping cotton fields and the cotton fields was identified most accurately at the early boll opening stage, about 40 days before harvest. Additionally, Sentinel-1 and the red edge bands in Sentinel-2 are important for cotton field mapping, and there is great potential for the fusion of optical images and microwave images in crop mapping. This study provides an effective approach for high-resolution and high-accuracy cotton field mapping, which is vital for sustainable monitoring and management of cotton planting. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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16 pages, 4014 KiB

Open AccessArticle

High Resolution Distribution Dataset of Double-Season Paddy Rice in China

by Baihong Pan, Yi Zheng, Ruoque Shen, Tao Ye, Wenzhi Zhao, Jie Dong, Hanqing Ma and Wenping Yuan

Remote Sens. 2021, 13(22), 4609; https://doi.org/10.3390/rs13224609 - 16 Nov 2021

Cited by 49 | Viewed by 6477

Abstract

Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify [...] Read more.

Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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25 pages, 14079 KiB

Open AccessArticle

Evaluating the Farmland Use Intensity and Its Patterns in a Farming—Pastoral Ecotone of Northern China

by Xin Chen, Guoliang Zhang, Yuling Jin, Sicheng Mao, Kati Laakso, Arturo Sanchez-Azofeifa, Li Jiang, Yi Zhou, Haile Zhao, Le Yu, Rui Jiang, Zhihua Pan and Pingli An

Remote Sens. 2021, 13(21), 4304; https://doi.org/10.3390/rs13214304 - 26 Oct 2021

Cited by 3 | Viewed by 3172

Abstract

The growing population and northward shifts in the center of grain production collectively contribute to the arising farmland use intensity of the farming–pastoral ecotone of Northern China (FPENC). Consequently, it poses a great threat to the vulnerable ecosystem of FPENC. Thus, farmland use [...] Read more.

The growing population and northward shifts in the center of grain production collectively contribute to the arising farmland use intensity of the farming–pastoral ecotone of Northern China (FPENC). Consequently, it poses a great threat to the vulnerable ecosystem of FPENC. Thus, farmland use intensity monitoring is a top priority to practice sustainable farming. In this study, we establish an indicator system designed to evaluate farmland use intensity in Ulanqab, located in the central part of FPENC. This system includes three single-year indicators (the degree of coupling between effective rainfall and crop water requirement (Dcrr), irrigation intensity (Iri) and crop duration (Cd)) and two multi-year indicators (the frequency of adopting the green-depressing cropping system (Gf) and rotation frequency (Rf)). We mapped five farmland use intensity indicators in Ulanqab from 2010 to 2019 using satellite imagery and other ancillary data. Then, the farmland use patterns were recognized by applying the self-organizing map algorithm. Our results suggest that the mapping results of crop types, center pivot irrigation (CPI), and irrigated areas are reasonably accurate. Iri, Cd, and Rf experienced an increase of 31 m³/hm², 1 day, and 0.06 in Ulanqab from 2010 to 2019, respectively, while Dcrr and Gf witnessed a decrease of 0.002 and 0.004, respectively. That is, farmers are progressively inclined to higher farmland use intensity. Moreover, spatial heterogeneity analysis shows that Northern Ulanqab owned higher Dcrr, Iri, Cd, and Rf, and lower Gf than the southern part. We conclude the paper by discussing the implications of the results for areas with different farmland use intensity patterns. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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16 pages, 2717 KiB

Open AccessArticle

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?

by David M. Johnson, Arthur Rosales, Richard Mueller, Curt Reynolds, Ronald Frantz, Assaf Anyamba, Ed Pak and Compton Tucker

Remote Sens. 2021, 13(21), 4227; https://doi.org/10.3390/rs13214227 - 21 Oct 2021

Cited by 38 | Viewed by 7435

Abstract

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery [...] Read more.

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R² of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R² results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R² performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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Figure 1
Study area. USA states in dark grey represent those that were also focused on for state-level yield assessment in addition to national-level. Crops shown are from the 2020 USDA NASS Cropland Data Layer. Full article ">Figure 2
Average NDVI signal over corn area of the USA showing years with highest (2020: yellow), lowest (2012: orange), earliest (2018: green), and latest (2019: blue) profiles. Full article ">Figure 3
R2 optimization versus threshold in the accumulated NDVI methodology. The left side of the chart was bounded by 0.3 as that was the point at which NDVI typically reaches a minimum off season. The right end of each line represents the minimum NDVI maximum that occurred during the period. Full article ">Figure 4
Relationships of USA-level crop yield versus year, peak NDVI, and accumulated NDVI (crop by row: (a). corn, (b). soybeans, (c). spring wheat, (d). winter wheat, (e). cotton; model by column: i. year, ii. peak NDVI, iii. accumulated NDVI). The LSR line is in dotted red with the corresponding R2, SE, and CV values shown in <a href="#remotesensing-13-04227-t001" class="html-table">Table 1</a>. Full article ">Figure 5
MODIS NDVI estimated 2020 corn yields over the Central USA Corn Belt based on accumulated over 0.58 NDVI methodology (1 corn bu/ac = 0.0628 mt/ha). Full article ">

21 pages, 2853 KiB

Open AccessArticle

Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

by El houssaine Bouras, Lionel Jarlan, Salah Er-Raki, Riad Balaghi, Abdelhakim Amazirh, Bastien Richard and Saïd Khabba

Remote Sens. 2021, 13(16), 3101; https://doi.org/10.3390/rs13163101 - 6 Aug 2021

Cited by 62 | Viewed by 7975

Abstract

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields [...] Read more.

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R² = 0.88 and RMSE around 0.22 t. ha^?1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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Graphical abstract
Full article ">Figure 1
The study areas with the 15 provinces. Full article ">Figure 2
Schematic diagram presenting an overview of the main inputs data and the methodology proposed in this study. Full article ">Figure 3
Model performance (R2 -line- and RMSE -bar-) as a function of the lead time from 4 to 2 months before havers (from January to March) for the four methods (MLR, SVM, RF and XGBoost). All the available predictor variables at the time of prediction were used (see <a href="#remotesensing-13-03101-t002" class="html-table">Table 2</a>). Full article ">Figure 4
Importance of the different inputs datasets for yield prediction from January (a); February (b) and March (c). The considered predictors variables and their time span of the year for each model are reported in <a href="#remotesensing-13-03101-t002" class="html-table">Table 2</a>. Full article ">Figure 5
Average of observed and predicted yields using the “leave-one-year-out” technique at (a) January, (b) February and (c) March. Full article ">Figure 6
RMSE of global model at (a) January, (b) February, (c) March and regional models at (e) January, (f) February and (g) March. Full article ">

20 pages, 10302 KiB

Open AccessArticle

Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season

by Kaili Yang, Yan Gong, Shenghui Fang, Bo Duan, Ningge Yuan, Yi Peng, Xianting Wu and Renshan Zhu

Remote Sens. 2021, 13(15), 3001; https://doi.org/10.3390/rs13153001 - 30 Jul 2021

Cited by 47 | Viewed by 4860

Abstract

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) [...] Read more.

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R²) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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22 pages, 5111 KiB

Open AccessArticle

Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest

by Yansi Chen, Jinliang Hou, Chunlin Huang, Ying Zhang and Xianghua Li

Remote Sens. 2021, 13(15), 2988; https://doi.org/10.3390/rs13152988 - 29 Jul 2021

Cited by 30 | Viewed by 4780

Abstract

Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence [...] Read more.

Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence between crops and non-crops. The purpose of this study is to explore the combining application of high-resolution multi-temporal Sentinel-1 (S1) radar backscatter and Sentinel-2 (S2) optical reflectance images for maize mapping in highly complex and heterogeneous landscapes in the middle reaches of Heihe River, northwest China. We proposed a new two-step method of vegetation extraction and followed by maize extraction, that is, extract the vegetation-covered areas first to reduce the inter-class variance by using a Random Forest (RF) classifier based on S2 data, and then extract the maize distribution in the vegetation area by using another RF classifier based on S1 and/or S2 data. The results demonstrate that the vegetation extraction classifier successfully identified vegetation-covered regions with an overall accuracy above 96% in the study area, and the accuracy of the maize extraction classifier constructed by the combined multi-temporal S1 and S2 images is significantly improved compared with that S1 (alone) or S2 (alone), with an overall accuracy of 87.63%, F1_Score of 0.86, and Kappa coefficient of 0.75. In addition, with the introduction of multi-temporal S1 and/or S2 images in crop growing season, the constructed RF model is more beneficial to maize mapping. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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23 pages, 10099 KiB

Open AccessArticle

Quantifying Effects of Excess Water Stress at Early Soybean Growth Stages Using Unmanned Aerial Systems

by Stuart D. Smith, Laura C. Bowling, Katy M. Rainey and Keith A. Cherkauer

Remote Sens. 2021, 13(15), 2911; https://doi.org/10.3390/rs13152911 - 24 Jul 2021

Cited by 6 | Viewed by 2820

Abstract

Low-gradient agricultural areas prone to in-field flooding impact crop development and yield potential, resulting in financial losses. Early identification of the potential reduction in yield from excess water stress at the plot scale provides stakeholders with the high-throughput information needed to assess risk [...] Read more.

Low-gradient agricultural areas prone to in-field flooding impact crop development and yield potential, resulting in financial losses. Early identification of the potential reduction in yield from excess water stress at the plot scale provides stakeholders with the high-throughput information needed to assess risk and make responsive economic management decisions as well as future investments. The objective of this study is to analyze and evaluate the application of proximal remote sensing from unmanned aerial systems (UAS) to detect excess water stress in soybean and predict the potential reduction in yield due to this excess water stress. A high-throughput data processing pipeline is developed to analyze multispectral images captured at the early development stages (R4–R5) from a low-cost UAS over two radiation use efficiency experiments in West–Central Indiana, USA. Above-ground biomass is estimated remotely to assess the soybean development by considering soybean genotype classes (High Yielding, High Yielding under Drought, Diversity, all classes) and transferring estimated parameters to a replicate experiment. Digital terrain analysis using the Topographic Wetness Index (TWI) is used to objectively compare plots more susceptible to inundation with replicate plots less susceptible to inundation. The results of the study indicate that proximal remote sensing estimates above-ground biomass at the R4–R5 stage using adaptable and transferable methods, with a calculated percent bias between 0.8% and 14% and root mean square error between 72 g/m² and 77 g/m² across all genetic classes. The estimated biomass is sensitive to excess water stress with distinguishable differences identified between the R4 and R5 development stages; this translates into a reduction in the percent of expected yield corresponding with observations of in-field flooding and high TWI. This study demonstrates transferable methods to estimate yield loss due to excess water stress at the plot level and increased potential to provide crop status assessments to stakeholders prior to harvest using low-cost UAS and a high-throughput data processing pipeline. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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17 pages, 4357 KiB

Open AccessArticle

Oilseed Rape (Brassica napus L.) Phenology Estimation by Averaged Stokes-Related Parameters

by Wangfei Zhang, Yongxin Zhang, Yue Yang and Erxue Chen

Remote Sens. 2021, 13(14), 2652; https://doi.org/10.3390/rs13142652 - 6 Jul 2021

Cited by 7 | Viewed by 2893

Abstract

Accurate and timely knowledge of crop phenology assists in planning and/or triggering appropriate farming activities. The multiple Polarimetric Synthetic Aperture Radar (PolSAR) technique shows great potential in crop phenology retrieval for its characterizations, such as short revisit time, all-weather monitoring and sensitivity to [...] Read more.

Accurate and timely knowledge of crop phenology assists in planning and/or triggering appropriate farming activities. The multiple Polarimetric Synthetic Aperture Radar (PolSAR) technique shows great potential in crop phenology retrieval for its characterizations, such as short revisit time, all-weather monitoring and sensitivity to vegetation structure. This study aims to explore the potential of averaged Stokes-related parameters derived from multiple PolSAR data in oilseed rape phenology identification. In this study, the averaged Stokes-related parameters were first computed by two different wave polarimetric states. Then, the two groups of averaged Stokes-related parameters were generated and applied for analyzing averaged Stokes-related parameter sensitivity to oilseed rape phenology changes. At last, decision tree (DT) algorithms trained using 60% of the data were used for oilseed rape phenological stage classification. Four Stokes parameters (

g_{0}

,

g_{1}

,

g_{2}

and

g_{3}

) and eight sub parameters (degree of polarization

m

, entropy

H

, ellipticity angle

?

, orientation angle

?

, degree of linear polarization

D o l p

, degree of circular polarization

D o c p

, linear polarization ratio

L p r

and circular polarization ratio

C p r

) were extracted from a multi-temporal RADARSAT-2 dataset acquired during the whole oilseed rape growth cycle in 2013. Their sensitivities to oilseed rape phenology were analyzed versus five main rape phenology stages. In two groups (two different wave polarimetric states) of this study,

g_{0}

,

g_{1}

,

g_{2}

,

g_{3}

,

m

,

H

,

D o l p

and

L p r

showed high sensitivity to oilseed rape growth stages while

?

,

?

,

D o c p

and

C p r

showed good performance for phenology classification in previous studies, which were quite noisy during the whole oilseed rape growth circle and showed unobvious sensitivity to the crop’s phenology change. The DT algorithms performed well in oilseed rape phenological stage identification. The results were verified at the parcel level with left 40% of the point dataset. Five phenology intervals of oilseed rape were identified with no more than three parameters by simple but robust decision tree algorithm groups. The identified phenology stages agree well with the ground measurements; the overall identification accuracies were 71.18% and 79.71%, respectively. For each growth stage, the best performance occurred at stage S1 with the accuracy of 95.65% for Group 1 and 94.23% for Group 2, and the worst performance occurred at stage S3 and S5 with the values around 60%. Most of the classification errors may resulted from the indistinguishability of S3 and S5 using Stokes-related parameters. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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25 pages, 24220 KiB

Open AccessArticle

UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model

by Ningge Yuan, Yan Gong, Shenghui Fang, Yating Liu, Bo Duan, Kaili Yang, Xianting Wu and Renshan Zhu

Remote Sens. 2021, 13(11), 2190; https://doi.org/10.3390/rs13112190 - 4 Jun 2021

Cited by 25 | Viewed by 5312

Abstract

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs [...] Read more.

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VI_E (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VI_E and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VI_E incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VI_F (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VI_E and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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19 pages, 5064 KiB

Open AccessArticle

Predicting Table Beet Root Yield with Multispectral UAS Imagery

by Robert Chancia, Jan van Aardt, Sarah Pethybridge, Daniel Cross and John Henderson

Remote Sens. 2021, 13(11), 2180; https://doi.org/10.3390/rs13112180 - 2 Jun 2021

Cited by 6 | Viewed by 3131

Abstract

Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris [...] Read more.

Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root count and mass using imagery taken during emergence and canopy closure, respectively. We created augmented plots, composed of random combinations of the study plots, to further exploit the importance of early canopy growth area. We achieved a R² = 0.70 and root mean squared error (RMSE) of 84 roots (~24%) for root count, using 2018 emergence imagery. The same model resulted in a RMSE of 127 roots (~35%) when tested on the unseen 2019 data. Harvested root mass was best modeled with canopy closing imagery, with a R² = 0.89 and RMSE = 6700 kg/ha using 2018 data. We applied the model to the 2019 full-field imagery and found an average yield of 41,000 kg/ha (~40,000 kg/ha average for upstate New York). This study demonstrates the potential for table beet yield models using a combination of radiometric and canopy structure data obtained at early growth stages. Additional imagery of these early growth stages is vital to develop a robust and generalized model of table beet root yield that can handle imagery captured at slightly different growth stages between seasons. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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25 pages, 7006 KiB

Open AccessArticle

Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data

by Xiufang Zhu, Rui Guo, Tingting Liu and Kun Xu

Remote Sens. 2021, 13(10), 2016; https://doi.org/10.3390/rs13102016 - 20 May 2021

Cited by 17 | Viewed by 5394

Abstract

Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and [...] Read more.

Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and remote sensing vegetation parameters to estimate maize yield in Jilin and Liaoning Provinces of China. We applied two methods to select input variables, used the random forest method to establish yield estimation models, and verified the accuracy of the models in three disaster years (1997, 2000, and 2001). The results show that the R² values of the eight yield estimation models established in the two provinces were all above 0.7, Lin’s concordance correlation coefficients were all above 0.84, and the mean absolute relative errors were all below 0.14. The mean absolute relative error of the yield estimations in the three disaster years was 0.12 in Jilin Province and 0.13 in Liaoning Province. A model built using variables selected by a two-stage importance evaluation method can obtain a better accuracy with fewer variables. The final yield estimation model of Jilin province adopts eight independent variables, and the final yield estimation model of Liaoning Province adopts nine independent variables. Among the 11 adopted variables in two provinces, ATT (accumulated temperature above 10 °C) variables accounted for the highest proportion (54.54%). In addition, the GPP (gross primary production) anomaly in August, NDVI (Normalized Difference Vegetation Index) anomaly in August, and standardized precipitation index with a two-month scale in July were selected as important modeling variables by all methods in the two provinces. This study provides a reference method for the selection of modeling variables, and the results are helpful for understanding the impact of climate on potential yield. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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20 pages, 5212 KiB

Open AccessArticle

Spatiotemporal Changes of Winter Wheat Planted and Harvested Areas, Photosynthesis and Grain Production in the Contiguous United States from 2008–2018

by Xiaocui Wu, Xiangming Xiao, Jean Steiner, Zhengwei Yang, Yuanwei Qin and Jie Wang

Remote Sens. 2021, 13(9), 1735; https://doi.org/10.3390/rs13091735 - 29 Apr 2021

Cited by 12 | Viewed by 4087

Abstract

Winter wheat is a main cereal crop grown in the United States of America (USA), and the USA is the third largest wheat exporter globally. Timely and reliable in-season forecast and year-end estimation of winter wheat grain production in the USA are needed [...] Read more.

Winter wheat is a main cereal crop grown in the United States of America (USA), and the USA is the third largest wheat exporter globally. Timely and reliable in-season forecast and year-end estimation of winter wheat grain production in the USA are needed for regional and global food security. In this study, we assessed the consistency between the agricultural statistical reports and satellite-based data for winter wheat over the contiguous US (CONUS) at both the county and national scales. First, we compared the planted area estimates from the National Agricultural Statistics Service (NASS) and the Cropland Data Layer (CDL) from 2008–2018. Second, we investigated the relationship between gross primary production (GPP) estimated by the vegetation photosynthesis model (VPM) and grain production from the NASS. Lastly, we explored the in-season utility of GPP_VPM in monitoring seasonal production. Strong spatiotemporal consistency of planted areas was found between the NASS and CDL datasets. However, in the Southern Great Plains, both the CDL and NASS planted acreage were noticeable larger (>20%) than the NASS harvested area, where some winter wheat fields were used as forage for cattle grazing. County-level GPP_VPM was linearly related with grain production of winter wheat, with an R² value of 0.68 across the CONUS. The relationships between grain production and GPP_VPM in those counties without a substantial difference (<20%) between planted and harvested area were much stronger and their harvest index (HI_GPP) values ranged from 0.2–0.3. GPP_VPM in May could explain about 70–90% of the variance of winter wheat grain production. Our findings highlight the potential of GPP_VPM in winter wheat monitoring, especially for those high harvested/planted ratio, which could provide useful data to guide planning and marketing for decision makers, stakeholders, and the public. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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Review

Jump to: Research, Other

19 pages, 3674 KiB

Open AccessReview

Monitoring and Analyzing Yield Gap in Africa through Soil Attribute Best Management Using Remote Sensing Approaches: A Review

by Keltoum Khechba, Ahmed Laamrani, Driss Dhiba, Khalil Misbah and Abdelghani Chehbouni

Remote Sens. 2021, 13(22), 4602; https://doi.org/10.3390/rs13224602 - 16 Nov 2021

Cited by 18 | Viewed by 5890

Abstract

Africa has the largest population growth rate in the world and an agricultural system characterized by the predominance of smallholder farmers. Improving food security in Africa will require a good understanding of farming systems yields as well as reducing yield gaps (i.e., the [...] Read more.

Africa has the largest population growth rate in the world and an agricultural system characterized by the predominance of smallholder farmers. Improving food security in Africa will require a good understanding of farming systems yields as well as reducing yield gaps (i.e., the difference between potential yield and actual farmer yield). To this end, crop yield gap practices in African countries need to be understood to fill this gap while decreasing the environmental impacts of agricultural systems. For instance, the variability of yields has been demonstrated to be strongly controlled by soil fertilizer use, irrigation management, soil attribute, and the climate. Consequently, the quantitative assessment and mapping information of soil attributes such as nitrogen (N), phosphorus (P), potassium (K), soil organic carbon (SOC), moisture content (MC), and soil texture (i.e., clay, sand and silt contents) on the ground are essential to potentially reducing the yield gap. However, to assess, measure, and monitor these soil yield-related parameters in the field, there is a need for rapid, accurate, and inexpensive methods. Recent advances in remote sensing technologies and high computational performances offer a unique opportunity to implement cost-effective spatiotemporal methods for estimating crop yield with important levels of scalability. However, researchers and scientists in Africa are not taking advantage of the opportunity of increasingly available geospatial remote sensing technologies and data for yield studies. The objectives of this report are to (i) conduct a review of scientific literature on the current status of African yield gap analysis research and their variation in regard to soil properties management by using remote sensing techniques; (ii) review and describe optimal yield practices in Africa; and (iii) identify gaps and limitations to higher yields in African smallholder farms and propose possible improvements. Our literature reviewed 80 publications and covered a period of 22 years (1998-2020) over many selected African countries with a potential yield improvement. Our results found that (i) the number of agriculture yield-focused remote sensing studies has gradually increased, with the largest proportion of studies published during the last 15 years; (ii) most studies were conducted exclusively using multispectral Landsat and Sentinel sensors; and (iii) over the past decade, hyperspectral imagery has contributed to a better understanding of yield gap analysis compared to multispectral imagery; (iv) soil nutrients (i.e., NPK) are not the main factor influencing the studied crop productivity in Africa, whereas clay, SOC, and soil pH were the most examined soil properties in prior papers. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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46 pages, 49047 KiB

Open AccessReview

Remote Sensing Applications in Sugarcane Cultivation: A Review

by Jaturong Som-ard, Clement Atzberger, Emma Izquierdo-Verdiguier, Francesco Vuolo and Markus Immitzer

Remote Sens. 2021, 13(20), 4040; https://doi.org/10.3390/rs13204040 - 10 Oct 2021

Cited by 67 | Viewed by 21449

Abstract

A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive [...] Read more.

A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive review is provided which considers the progress made using novel data analysis techniques and improved data sources. To complement the available reviews, and to make the large body of research more easily accessible for both researchers and practitioners, in this review (i) we summarized remote sensing applications from 1981 to 2020, (ii) discussed key strengths and weaknesses of remote sensing approaches in the sugarcane context, and (iii) described the challenges and opportunities for future earth observation (EO)-based sugarcane monitoring and management. More than one hundred scientific studies were assessed regarding sugarcane mapping (52 papers), crop growth anomaly detection (11 papers), health monitoring (14 papers), and yield estimation (30 papers). The articles demonstrate that decametric satellite sensors such as Landsat and Sentinel-2 enable a reliable, cost-efficient, and timely mapping and monitoring of sugarcane by overcoming the ground sampling distance (GSD)-related limitations of coarser hectometric resolution data, while offering rich spectral information in the frequently recorded data. The Sentinel-2 constellation in particular provides fine spatial resolution at 10 m and high revisit frequency to support sugarcane management and other applications over large areas. For very small areas, and in particular for up-scaling and calibration purposes, unmanned aerial vehicles (UAV) are also useful. Multi-temporal and multi-source data, together with powerful machine learning approaches such as the random forest (RF) algorithm, are key to providing efficient monitoring and mapping of sugarcane growth, health, and yield. A number of difficulties for sugarcane monitoring and mapping were identified that are also well known for other crops. Those difficulties relate mainly to the often (i) time consuming pre-processing of optical time series to cope with atmospheric perturbations and cloud coverage, (ii) the still important lack of analysis-ready-data (ARD), (iii) the diversity of environmental and growth conditions—even for a given country—under which sugarcane is grown, superimposing non-crop related radiometric information on the observed sugarcane crop, and (iv) the general ill-posedness of retrieval and classification approaches which adds ambiguity to the derived information. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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Other

Jump to: Research, Review

14 pages, 2061 KiB

Open AccessTechnical Note

Differentiate Soybean Response to Off-Target Dicamba Damage Based on UAV Imagery and Machine Learning

by Caio Canella Vieira, Shagor Sarkar, Fengkai Tian, Jing Zhou, Diego Jarquin, Henry T. Nguyen, Jianfeng Zhou and Pengyin Chen

Remote Sens. 2022, 14(7), 1618; https://doi.org/10.3390/rs14071618 - 28 Mar 2022

Cited by 14 | Viewed by 3410

Abstract

The wide adoption of dicamba-tolerant (DT) soybean has led to numerous cases of off-target dicamba damage to non-DT soybean and dicot crops. This study aimed to develop a method to differentiate soybean response to dicamba using unmanned-aerial-vehicle-based imagery and machine learning models. Soybean [...] Read more.

The wide adoption of dicamba-tolerant (DT) soybean has led to numerous cases of off-target dicamba damage to non-DT soybean and dicot crops. This study aimed to develop a method to differentiate soybean response to dicamba using unmanned-aerial-vehicle-based imagery and machine learning models. Soybean lines were visually classified into three classes of injury, i.e., tolerant, moderate, and susceptible to off-target dicamba. A quadcopter with a built-in RGB camera was used to collect images of field plots at a height of 20 m above ground level. Seven image features were extracted for each plot, including canopy coverage, contrast, entropy, green leaf index, hue, saturation, and triangular greenness index. Classification models based on artificial neural network (ANN) and random forest (RF) algorithms were developed to differentiate the three classes of response to dicamba. Significant differences for each feature were observed among classes and no significant differences across fields were observed. The ANN and RF models were able to precisely distinguish tolerant and susceptible lines with an overall accuracy of 0.74 and 0.75, respectively. The imagery-based classification model can be implemented in a breeding program to effectively differentiate phenotypic dicamba response and identify soybean lines with tolerance to off-target dicamba damage. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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17 pages, 3896 KiB

Open AccessTechnical Note

Optimization of Topdressing for Winter Wheat by Accurate Growth Monitoring and Improved Production Estimation

by Jingchun Ji, Jianli Liu, Jingjing Chen, Yujie Niu, Kefan Xuan, Yifei Jiang, Renhao Jia, Can Wang and Xiaopeng Li

Remote Sens. 2021, 13(12), 2349; https://doi.org/10.3390/rs13122349 - 16 Jun 2021

Cited by 2 | Viewed by 2422

Abstract

Topdressing accounts for approximately 40% of the total nitrogen (N) application of winter wheat on the Huang-Huai-Hai Plain in China. However, N use efficiency of topdressing is low due to the inadaptable topdressing method used by local farmers. To improve the N use [...] Read more.

Topdressing accounts for approximately 40% of the total nitrogen (N) application of winter wheat on the Huang-Huai-Hai Plain in China. However, N use efficiency of topdressing is low due to the inadaptable topdressing method used by local farmers. To improve the N use efficiency of winter wheat, an optimization method for topdressing (T_HP) is proposed that uses unmanned aerial vehicle (UAV)-based remote sensing to accurately acquire the growth status and an improved model for growth potential estimation and optimization of N fertilizer amount for topdressing (NFT). The method was validated and compared with three other methods by a field experiment: the conventional local farmer’s method (T_LF), a nitrogen fertilization optimization algorithm (NFOA) proposed by Raun and Lukina (T_RL) and a simplification introduced by Li and Zhang (T_LZ). It shows that when insufficient basal fertilizer was provided, the proposed method provided as much NFT as the T_LF method, i.e., 25.05% or 11.88% more than the T_RL and T_LZ methods and increased the yields by 4.62% or 2.27%, respectively; and when sufficient basal fertilizer was provided, the T_HP method followed the T_RL and T_LZ methods to reduce NFT but maintained as much yield as the T_LF method with a decrease of NFT by 4.20%. The results prove that T_HP could enhance crop production under insufficient N preceding conditions by prescribing more fertilizer and increase nitrogen use efficiency (NUE) by lowering the fertilizer amount when enough basal fertilizer is provided. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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