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Monitoring Crops and Rangelands Using Remote Sensing

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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 (10 April 2023) | Viewed by 44703

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Special Issue Editors

Prof. Dr. Adriaan van Niekerk

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Guest Editor

Centre for Geographical Analysis, Department of Geography & Environmental Studies, Stellenbosch University, Stellenbosch 7600, South Africa
Interests: agricultural applications of remotely sensed data (mostly multispectral and multitemporal imagery); e.g., crop type mapping and monitoring of salt accumulation; water use and crop conditions; land cover mapping; object-based image analysis (OBIA); machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Caren Jarmain

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Guest Editor

1. Department of Biological Sciences, School of Natural Sciences, University of Limerick, V94 T9PX Limerick, Ireland
2. Centre for Geographical Analysis, Department of Geography & Environmental Studies, Stellenbosch University, Matieland, Stellenbosch 7602, South Africa
Interests: crop water requirements; surface energy balance; evapotranspiration and CO₂ fluxes; water use efficiency and water footprint of vegetative surfaces; applying and evaluating micrometeorological methods for various applications and conditions; using remote sensing derived data to support improved water use efficiency in agriculture and water management; translating (remote-sensing-derived) spatial data into practical uses; creative technology transfer to non-scientists

Special Issue Information

Dear Colleagues,

Remote sensing has emerged as an invaluable resource and technology for supporting agricultural decisions. Imagery captured by sensors mounted on satellites, aircraft, and unmanned aerial vehicles (UAVs) is routinely used by governments, agribusinesses, and producers to monitor crops and rangelands. Vegetation indices are particularly popular for characterizing crop and rangeland status and have become part of the day-to-day operations of many farmers and consultants. In fact, agriculture is arguably the industry that benefits most from the substantial investments that have been made in Earth observation infrastructure. Although the scientific community has played a very important role in transferring technological advances into practical solutions, much more needs to be done to fully exploit the potential of remotely sensed data for agricultural applications. In particular, there is a need to move beyond using (only) vegetation indices and to demonstrate the value of recent RS innovations for applications at global, national, regional, and local scales. This special edition aims to explore and expose such innovations, with a strong emphasis on how they can be operationalized to support agricultural decision making.

Some initial themes:

The value of RS in crop and rangeland management.
Crop water and/or nutrition status quantification and monitoring.
Monitoring irrigated fields and quantifying the volume of irrigation applied.
Grazing management (e.g., monitoring the carrying capacity, overgrazing, status, and degradation).
Monitoring salt accumulation and waterlogging in irrigated fields.
The integration (fusion) and comparison of different data sources/types, platforms and spatial, spectral, and temporal resolutions for crop and rangeland monitoring.

Prof. Dr. Adriaan van Niekerk
Dr. Caren Jarmain
Guest Editors

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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

agriculture
applications
crops
rangelands
irrigation
nutrient management
grazing management
data fusion

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Published Papers (12 papers)

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Research

22 pages, 8129 KiB

Open AccessArticle

Identification of Brush Species and Herbicide Effect Assessment in Southern Texas Using an Unoccupied Aerial System (UAS)

by Xiaoqing Shen, Megan K. Clayton, Michael J. Starek, Anjin Chang, Russell W. Jessup and Jamie L. Foster

Remote Sens. 2023, 15(13), 3211; https://doi.org/10.3390/rs15133211 - 21 Jun 2023

Viewed by 1293

Abstract

Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel- and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.] Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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17 pages, 6628 KiB

Open AccessArticle

Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms

by Juan I. Gargiulo, Nicolas A. Lyons, Fernando Masia, Peter Beale, Juan R. Insua, Martin Correa-Luna and Sergio C. Garcia

Remote Sens. 2023, 15(11), 2752; https://doi.org/10.3390/rs15112752 - 25 May 2023

Cited by 3 | Viewed by 3049

Abstract

Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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35 pages, 12709 KiB

Open AccessArticle

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

by Maninder Singh Dhillon, Carina Kübert-Flock, Thorsten Dahms, Thomas Rummler, Joel Arnault, Ingolf Steffan-Dewenter and Tobias Ullmann

Remote Sens. 2023, 15(7), 1830; https://doi.org/10.3390/rs15071830 - 29 Mar 2023

Cited by 10 | Viewed by 5354

Abstract

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km²) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R² = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R² = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R² = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R² = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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20 pages, 5566 KiB

Open AccessArticle

Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine

by Xingyuan Xiao, Linlong Jiang, Yaqun Liu and Guozhen Ren

Remote Sens. 2023, 15(4), 1112; https://doi.org/10.3390/rs15041112 - 17 Feb 2023

Cited by 6 | Viewed by 3019

Abstract

Reliable crop type classification supports the scientific basis for food security and sustainable agricultural development. However, it still lacks a limited-samples-based crop classification method which is labor- and time-efficient. To this end, we used the Google Earth Engine (GEE) and Sentinel-1A/B SAR time series to develop eight types of crop classification strategies based on different sampling methods of central and scattered, different perspectives of object-based and pixel-based, and different classifiers of the Time-Weighted Dynamic Time Warping (TWDTW) and Random Forest (RF). We carried out 30-times classifications with different samples for each strategy to classify the crop types at the North Dakota–Minnesota border in the U.S. We then compared their classification accuracies and assessed the accuracy sensitivity to sample size. The results found that the TWDTW generally performed better than RF, especially for small-sample classification. Object-based classifications had higher accuracies than pixel-based classifications, and the object-based TWDTW had the highest accuracy. RF performed better in scattered sampling than the central sampling strategy. TWDTW performed better than RF in distinguishing soybean and dry bean with similar curves. The accuracies improved for all eight classification strategies with increasing sample size, and TWDTW was more robust, while RF was more sensitive to sample size change. RF required many more samples than TWDTW to achieve satisfactory accuracy, and it performed better than TWDTW when the sample size exceeded 50. The accuracy comparisons indicated that the TWDTW has stronger temporal and spatial generalization capabilities and has high potential applications for early, historical, and limited-samples-based crop type classification. The findings of our research are worthwhile contributions to the methodology and practices of crop type classification as well as sustainable agricultural development. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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30 pages, 35019 KiB

Open AccessArticle

A Retrospective Analysis of National-Scale Agricultural Development in Saudi Arabia from 1990 to 2021

by Ting Li, Oliver Miguel López Valencia, Kasper Johansen and Matthew F. McCabe

Remote Sens. 2023, 15(3), 731; https://doi.org/10.3390/rs15030731 - 26 Jan 2023

Cited by 3 | Viewed by 4385

Abstract

Agricultural intensification has resulted in the depletion of groundwater resources in many regions of the world. A prime example is Saudi Arabia, which witnessed dramatic agricultural expansion since the 1970s. To explore the influence of policy interventions aimed to better manage water resources, accurate information on the changes in the number and acreage of center-pivot fields is required. To quantify these metrics, we apply a hybrid machine learning framework, consisting of Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering, to the annual maximum Normalized Differential Vegetation Index maps obtained from Landsat imagery collected between 1990 to 2021. When evaluated against more than 28,000 manually delineated fields, the approach demonstrated producer’s accuracies ranging from 83.7% to 94.8% and user’s accuracies ranging from 90.2% to 97.9%. The coefficient of determination (

R^{2}

) between framework-delineated and manually delineated fields was higher than 0.97. Nationally, we found that most fields pre-dated 1990 (covering 8841 km

^{2}

in that year) and were primarily located within the central regions covering Hail, Qassim, Riyadh, and Wadi ad-Dawasir. A small decreasing trend in field acreage was observed for the period 1990–2010. However, by 2015, the acreage had increased to approximately 33,000 fields covering 9310 km

^{2}

. While a maximum extent was achieved in 2016, recent decreases have seen levels return to pre-1990 levels. The gradual decrease between 1990 to 2010 was related to policy initiatives designed to phase-out wheat, while increases between 2010 to 2015 were linked to fodder crop expansion. There is evidence of an agricultural uptick starting in 2021, which is likely in response to global influences such as the COVID-19 pandemic or the conflict in Ukraine. Overall, this work offers the first detailed assessment of long-term agricultural development in Saudi Arabia, and provides important insights related to production metrics such as crop types, crop water consumption, and crop phenology and the overarching impacts of agricultural policy interventions. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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18 pages, 4876 KiB

Open AccessArticle

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

by Siqi Ma, Danyang Wang, Haichao Yang, Huagang Hou, Cheng Li and Zhaofu Li

Remote Sens. 2023, 15(3), 658; https://doi.org/10.3390/rs15030658 - 22 Jan 2023

Cited by 2 | Viewed by 1619

Abstract

Rice-crayfish field (i.e., RCF) distribution mapping is crucial for the adjustment of the local crop cultivation structure and agricultural development. The single-temporal images of two phenological periods in the year were classified separately, and then the areas where the water disappeared were identified as RCFs in previous studies. However, due to the differences in the segmentation of lakes and rivers between the two images, the incorrect extraction of RCFs is unavoidable. To solve this problem, a bi-temporal-feature-difference-coupling object-based (BTFDOB) algorithm was proposed in order to map RCFs in Sihong County. We mapped RCFs by segmenting the bi-temporal images simultaneously based on the object-based method and selecting appropriate feature differences as the classification features. To evaluate the applicability, the classification results of the previous two years obtained using the single-temporal- and object-based (STOB) method were compared with the results of the BTFDOB method. The results suggested that spectral feature differences showed high feature importance, which could effectively distinguish the RCFs from non-RCFs. Our method worked well, with an overall accuracy (OA) of 96.77%. Compared with the STOB method, OA was improved by up to 2.18% across three years of data. The RCFs were concentrated in the low-lying eastern and southern regions, and the cultivation scale was expanded in Sihong. These findings indicate that the BTFDOB method can accurately identify RCFs, providing scientific support for the dynamic monitoring and rational management of the pattern. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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32 pages, 16565 KiB

Open AccessArticle

Unlocking Large-Scale Crop Field Delineation in Smallholder Farming Systems with Transfer Learning and Weak Supervision

by Sherrie Wang, François Waldner and David B. Lobell

Remote Sens. 2022, 14(22), 5738; https://doi.org/10.3390/rs14225738 - 13 Nov 2022

Cited by 20 | Viewed by 9184

Abstract

Crop field boundaries aid in mapping crop types, predicting yields, and delivering field-scale analytics to farmers. Recent years have seen the successful application of deep learning to delineating field boundaries in industrial agricultural systems, but field boundary datasets remain missing in smallholder systems due to (1) small fields that require high resolution satellite imagery to delineate and (2) a lack of ground labels for model training and validation. In this work, we use newly-accessible high-resolution satellite imagery and combine transfer learning with weak supervision to address these challenges in India. Our best model uses 1.5 m resolution Airbus SPOT imagery as input, pre-trains a state-of-the-art neural network on France field boundaries, and fine-tunes on India labels to achieve a median Intersection over Union (mIoU) of 0.85 in India. When we decouple field delineation from cropland classification, a model trained in France and applied as-is to India Airbus SPOT imagery delineates fields with a mIoU of 0.74. If using 4.8 m resolution PlanetScope imagery instead, high average performance (mIoU > 0.8) is only achievable for fields larger than 1 hectare. Experiments also show that pre-training in France reduces the number of India field labels needed to achieve a given performance level by as much as 10× when datasets are small. These findings suggest our method is a scalable approach for delineating crop fields in regions of the world that currently lack field boundary datasets. We publicly release 10,000 Indian field boundary labels and our delineation model to facilitate the creation of field boundary maps and new methods by the community. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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22 pages, 5601 KiB

Open AccessArticle

Early-Season Crop Identification in the Shiyang River Basin Using a Deep Learning Algorithm and Time-Series Sentinel-2 Data

by Zhiwei Yi, Li Jia, Qiting Chen, Min Jiang, Dingwang Zhou and Yelong Zeng

Remote Sens. 2022, 14(21), 5625; https://doi.org/10.3390/rs14215625 - 7 Nov 2022

Cited by 17 | Viewed by 3013

Abstract

Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. In recent years, great strides have been made in the development of deep-learning algorithms, and the emergence of Sentinel-2 data with a higher temporal resolution has provided new opportunities for early-season crop identification. In this study, we aimed to fully exploit the potential of deep-learning algorithms and time-series Sentinel-2 data for early-season crop identification and early-season crop mapping. In this study, four classifiers, i.e., two deep-learning algorithms (one-dimensional convolutional networks and long and short-term memory networks) and two shallow machine-learning algorithms (a random forest algorithm and a support vector machine), were trained using early-season Sentinel-2 images and field samples collected in 2019. Then, these algorithms were applied to images and field samples for 2020 in the Shiyang River Basin. Twelve scenarios with different classifiers and time intervals were compared to determine the optimal combination for the earliest crop identification. The results show that: (1) the two deep-learning algorithms outperformed the two shallow machine-learning algorithms in early-season crop identification; (2) the combination of a one-dimensional convolutional network and 5-day interval time-series Sentinel-2 data outperformed the other schemes in obtaining the early-season crop identification time and achieving early mapping; and (3) the early-season crop identification mapping time in the Shiyang River Basin was identified as the end of July, and the overall classification accuracy reached 0.83. In addition, the early identification time for each crop was as follows: the wheat was in the flowering stage (mid-late June); the alfalfa was in the first harvest (mid-late June); the corn was in the early tassel stage (mid-July); the fennel and sunflower were in the flowering stage (late July); and the melons were in the fruiting stage (around late July). This study demonstrates the potential of using Sentinel-2 time-series data and deep-learning algorithms to achieve early-season crop identification, and this method is expected to provide new solutions and ideas for addressing early-season crop identification monitoring. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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17 pages, 3610 KiB

Open AccessArticle

Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data

by Matthias Wengert, Jayan Wijesingha, Damian Schulze-Brüninghoff, Michael Wachendorf and Thomas Astor

Remote Sens. 2022, 14(9), 2068; https://doi.org/10.3390/rs14092068 - 26 Apr 2022

Cited by 11 | Viewed by 2585

Abstract

Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of grassland with high spatial, temporal and radiometric resolution and coverage is available. The present study aimed at developing a robust model capable of estimating grassland biomass across a gradient of usage intensity throughout one growing season. Therefore, UAV-borne hyperspectral data from eight grassland sites in North Hesse, Germany, originating from different harvests, were utilized for the modeling of fresh matter yield (FMY) and DMY. Four machine learning (ML) algorithms were compared for their modeling performance. Among them, the rule-based ML method Cubist regression (CBR) performed best, delivering high prediction accuracies for both FMY (nRMSE_p 7.6%,

R_{p}^{2}

0.87) and DMY (nRMSE_p 12.9%,

R_{p}^{2}

0.75). The model showed a high robustness across sites and harvest dates. The best models were employed to produce maps for FMY and DMY, enabling the detailed analysis of spatial patterns. Although the complexity of the approach still restricts its practical application in agricultural management, the current study proved that biomass of grassland sites being subject to different management intensities can be modeled from UAV-borne hyperspectral data at high spatial resolution with high prediction accuracies. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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18 pages, 4308 KiB

Open AccessArticle

Evaluating Several Vegetation Indices Derived from Sentinel-2 Imagery for Quantifying Localized Overgrazing in a Semi-Arid Region of South Africa

by Christiaan J. Harmse, Hannes Gerber and Adriaan van Niekerk

Remote Sens. 2022, 14(7), 1720; https://doi.org/10.3390/rs14071720 - 2 Apr 2022

Cited by 9 | Viewed by 3525

Abstract

Rangeland monitoring aims to determine whether grazing management strategies meet the goals of sustainable resource utilization. The development of sustainable grazing management strategies requires an understanding of the manner in which grazing animals utilize available vegetation. In this study, we made use of livestock tracking, in situ observations and Sentinel-2 imagery to make rangeland scale observations of vegetation conditions in a semi-arid environment, to better understand the spatial relationships between vegetation conditions and sheep movement patterns. We hypothesized that sheep graze more selectively under low stocking rates—resulting in localized overgrazing. We also assessed the importance of image spatial resolution, as it was assumed localized effects of grazing will be best explained by higher resolution imagery. The results showed that livestock tend to congregate along drainage lines where soils are deeper. The findings demonstrate how the spatial analysis of remotely sensed data can provide a landscape-scale overview of livestock movement patterns. This study illustrates that high-resolution normalized difference vegetation index (NDVI) data can be used as a grazing management tool to determine the spatial variability of productive areas across the semi-arid Upper Karoo rangelands and identify preferred grazing areas. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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14 pages, 1388 KiB

Open AccessArticle

A Proposed Satellite-Based Crop Insurance System for Smallholder Maize Farming

by Wonga Masiza, Johannes George Chirima, Hamisai Hamandawana, Ahmed Mukalazi Kalumba and Hezekiel Bheki Magagula

Remote Sens. 2022, 14(6), 1512; https://doi.org/10.3390/rs14061512 - 21 Mar 2022

Cited by 3 | Viewed by 3548

Abstract

Crop farming in Sub-Saharan Africa is constantly confronted by extreme weather events. Researchers have been striving to develop different tools that can be used to reduce the impacts of adverse weather on agriculture. Index-based crop insurance (IBCI) has emerged to be one of the tools that could potentially hedge farmers against weather-related risks. However, IBCI is still constrained by poor product design and basis risk. This study complements the efforts to improve IBCI design by evaluating the performances of the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) in estimating rainfall at different spatial scales over the maize-growing season in a smallholder farming area in South Africa. Results show that CHIRPS outperforms TAMSAT and produces better results at 20-day and monthly time steps. The study then uses CHIRPS and a crop water requirements (CWR) model to derive IBCI thresholds and an IBCI payout model. Results of CWR modeling show that this proposed IBCI system can cover the development, mid-season, and late-season stages of maize growth in the study area. The study then uses this information to calculate the weight, trigger, exit, and tick for each of these growth stages. Although this approach is premised on the prevailing conditions in the study area, it can be applied in other areas with different growing conditions to improve IBCI design. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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14 pages, 4752 KiB

Open AccessArticle

Estimation of Parameters of Biomass State of Sowing Spring Wheat

by Ilya Mikhayilovich Mikhailenko

Remote Sens. 2022, 14(6), 1388; https://doi.org/10.3390/rs14061388 - 13 Mar 2022

Cited by 4 | Viewed by 1943

Abstract

The purpose of this work is to present a new method for estimating the parameters of the biomass of agricultural crops based on Earth remote sensing (ERS) data. The method includes mathematical models and algorithms estimation and has been tested on the example of spring wheat sowing. Sowing biomass parameters are the basis for making management decisions aimed at obtaining a given crop yield. Currently, for these purposes, vegetation indices are most widely used. It is impossible to estimate the physical parameters of the crop sowing biomass using these indices, due to their scalar form and lack of dimension. The paper develops a classical approach to the problem of estimating the parameters of the state of agricultural crops, in which remote sensing data are considered as an indirect measurement of the estimated parameters. The basis for the implementation of the estimation method is the dynamic model of biomass parameters and the remote sensing model, which reflects the relationship between the spectral reflection parameters and the estimated parameters of the crop biomass. The parameters of the dynamic model and the remote sensing model are refined by selective ground measurements in separate elementary sections of the field. The difference between this article and previous works of a similar nature lies in the fact that agricultural crops with a more complex morphological structure are considered as the object of evaluation. In addition, such an important feature of agricultural objects as their spatial distribution is considered here. To take it into account, a new type of mathematical models is used, in which spatial coordinates are introduced. Due to the significant complication of modeling and estimation algorithms based on such models, simpler approximation schemes are proposed. The advantage of the proposed approach is that the assessment is considered as a dynamic process that meets the content of the task of monitoring crops. Full article

(This article belongs to the Special Issue Monitoring Crops and Rangelands Using Remote Sensing)

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