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Remote Sensing in Support of Transforming Smallholder Agriculture

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 January 2019) | Viewed by 35531

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Special Issue Editors

Dr. Rolf A. De By

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Guest Editor

Departmment of Geo-information Processing, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7500 AA Enschede, The Netherlands
Interests: large-scale geodata applications; formal methods to system design; international development; food production systems; space-time applications

Dr. Inbal Becker-Reshef

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Guest Editor

1. Associate Research Professor and Co-Director , Center for Global Agricultural Monitoring Research, Department of Geographical Sciences, University of Maryland. Suite 410, 4321 Hartwick Rd., College Park, MD 20740, USA
2. Program Scientist, GEOGLAM Secretariat
Interests: application of satellite information for agricultural monitoring at national to global scales, with a focus on the transition of viable EO based research into operational systems

Dr. Urs Schulthess

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Guest Editor

PhD |Senior Scientist Remote Sensing, CIMMYT (International Maize and Wheat Improvement Center), Sustainable Intensification Program (SIP), CIMMYT-China Collaborative Innovation Center, Henan Agricultural University, Zhengzhou 450002, China

Special Issue Information

Dear Colleagues,

Among the 17 UN Sustainable Development Goals, the first three, No Poverty, Zero Hunger, and Good Health and Well-Being, are related to food production, the world’s largest sector, as measured by labor force. This sector is so large because, in many smallholder farming settings, agriculture is not mechanized and it is therefore labor-intensive. Most of the 570 million farms globally are family-operated; these families operate on three quarters of the planet’s agricultural lands. A vast 83% of these farms is run by smallholders, on lands less than two hectares in size. Moreover, longer-term demographic projections for Africa and Asia identify smallholder-dominated landscapes as the more food-insecure and indicate expected substantial dynamics in the food sector at the rural/urban interface.

Long-term food security in smallholder landscapes, thus, requires careful policy formulation and action planning at different levels of the food system governance pyramid. Reliable information in support of such is, however, scarce or comes late, and is often only qualitative in character. It has long been recognized that earth observation can help in defining and implementing the monitoring systems required, and much work has been done in that direction. Some of the information needs can be addressed through Earth observation approaches. The challenges, however, are huge: compared to industrial food production systems, smallholder farming is extremely diverse in appearance, due to variability in the use of inputs (sensu lato), in its goals, and in the use of applied farming methods. This high diversity makes monitoring from the skies, while utterly needed, also seriously problematic.

Recent technical and methodical developments are providing hope for breakthroughs. New sensor systems are providing new and richer data types (spatial, temporal, spectral, active/passive), the mobile-and-sensor era has resulted in novel ground data collection tools, and advances in computing (AI-based) are also allowing us to do more with that data. All of these developments present realistic options to identify and quantify crop types, crop acreage, cropping systems, farming practices, and crop calendars, yields and yield variability. Mapping crop stress factors has also become a more viable option, that may lead to operational systems at some stage.

We are thus inviting authors and research teams to publish their recent work in the area of Earth observation and smallholder farming systems, and we are especially excited to receive contributions that report on good examples of methods that work. Our specific interests are in the domains of multi-scale approaches (from farm to government), multi-sensor and data fusion methods (incl. satellite constellations), novel image analytics, portability of earth observation-based methods, and approaches to improve field delineations from image sources.

We invite papers on the following non-exhaustive list of topics around smallholder farming:

Remote sensing-based indicators in support of monitoring smallholder food production systems
Operational systems in support of monitoring smallholder farming
Transformational changes in Earth observation systems and their impact in smallholder monitoring
Multi-scale approaches (from farm to government), multi-sensor and data fusion methods (incl. satellite constellations)
Novel image analytics, especially in the domains of crop, cropping system and farm practices identification
Portability of Earth observation-based methods
Approaches for automated field boundary detection

Dr. Rolf A. de By
Dr. Inbal Becker-Reshef
Dr. Urs Schulthess
Guest Editors

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Published Papers (4 papers)

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20 pages, 3800 KiB

Open AccessArticle

Evaluating the Performance of a Random Forest Kernel for Land Cover Classification

by Azar Zafari, Raul Zurita-Milla and Emma Izquierdo-Verdiguier

Remote Sens. 2019, 11(5), 575; https://doi.org/10.3390/rs11050575 - 8 Mar 2019

Cited by 42 | Viewed by 9988 | Correction

Abstract

The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectral WorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of

81.34 %

81.08 %

and

82.08 %

for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of

82 %

80.82 %

and

77.96 %

. In Salinas, OAs are of

94.42 %

95.83 %

and

94.16 %

. These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2%. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods. Full article

(This article belongs to the Special Issue Remote Sensing in Support of Transforming Smallholder Agriculture)

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18 pages, 8112 KiB

Open AccessArticle

Crop Classification Based on a Novel Feature Filtering and Enhancement Method

by Limin Wang, Qinghan Dong, Lingbo Yang, Jianmeng Gao and Jia Liu

Remote Sens. 2019, 11(4), 455; https://doi.org/10.3390/rs11040455 - 22 Feb 2019

Cited by 24 | Viewed by 7015

Abstract

Vegetation indices, such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from remote sensing images, are widely used for crop classification. However, vegetation index profiles for different crops with a similar phenology lead to difficulties in discerning these crops both spectrally and temporally. This paper proposes a feature filtering and enhancement (FFE) method to map soybean and maize, two major crops widely cultivated during the summer season in Northeastern China. Different vegetation indices are first calculated and the probability density functions (PDFs) of these indices for the target classes are established based on the hypothesis of normal distribution; the vegetation index images are then filtered using the PDFs to obtain enhanced index images where the pixel values of the target classes are ”enhanced”. Subsequently, the minimum Gini index of each enhanced index image is computed, generating at the same time the weight for every index. A composite enhanced feature image is produced by summing all indices with their weights. Finally, a classification is made from the composite enhanced feature image by thresholding, which is derived automatically based on the samples. The efficiency of the proposed FFE method is compared with the maximum likelihood classification (MLC), support vector machine (SVM), and random forest (RF) in a mapping operation to determine the soybean and maize distribution in a county in Northeastern China. The classification accuracies resulting from this comparison show that the FFE method outperforms MLC, and its accuracies are similar to those of SVM and RF, with an overall accuracy of 0.902 and a kappa coefficient of 0.846. This indicates that the FFE method is an appropriate method for crop classification to distinguish crops with a similar phenology. Our research also shows that when the sample size reaches a certain level (e.g., 2000), the mean and standard deviation of the sample are very close to the actual values, which leads to high classification accuracy. In a case where the condition of normal distribution is not fulfilled, the PDF of the vegetation index can be created by a lookup table. Furthermore, as the method is rather simple and explicit, and convenient in terms of computing, it can be used as the backbone for automatic crop mapping operations. Full article

(This article belongs to the Special Issue Remote Sensing in Support of Transforming Smallholder Agriculture)

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15 pages, 3175 KiB

Open AccessEditor’s ChoiceArticle

Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield

by Kensuke Kawamura, Hiroshi Ikeura, Sengthong Phongchanmaixay and Phanthasin Khanthavong

Remote Sens. 2018, 10(8), 1249; https://doi.org/10.3390/rs10081249 - 8 Aug 2018

Cited by 38 | Viewed by 15268

Abstract

Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R²) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R² values and lower RMSECV values for all data sets. Overall, the highest R² values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R² = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model. Full article

(This article belongs to the Special Issue Remote Sensing in Support of Transforming Smallholder Agriculture)

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