A Spatial Database of Cropping System and its Characteristics to Aid Climate Change Impact Assessment Studies

Panigrahy, Sushma; Ray, S. S.; Manjunath, K. R.; Pandey, Pragya; Sood, Anil K.; Yadav, Manoj Kumar; Gupta, Priyanka; Kundu, N.; Parihar, J. S.

doi:10.1007/s12524-011-0093-3

Cited by 17 publications

(9 citation statements)

References 12 publications

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“…Also, the insignificant differences among the performance of our classification scenarios indicate the novelty and robustness of our variable selection Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 26 October 2020 doi:10.20944/preprints202010.0517.v1 experiment using GRRF, in such a way that a few selected numbers of variables performance were not statistically different to all combined variables. The mapping of cropping patterns is of profound importance in understanding the sustainability of food systems and how they are affected by climate and also for modeling the abundance and spread of crop insect pests and disease and pollinators [19,79]. Furthermore, cropping patterns are valuable parameters in many crop modeling frameworks that estimate crop production as a function of many biotic and abiotic factors [80].…”

Section: Discussionmentioning

confidence: 99%

Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data

Aduvukha¹,

Abdel‐Rahman²,

Sichangi³

et al. 2020

Preprint

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The proportion of area under various crops at a given point in time, known as a cropping pattern, plays an essential role in determining the level of agricultural production. In this study, cropping patterns of three sub-counties in Murang’a County, a typical African smallholder farming area in Kenya, were mapped. Specifically, we compared the performance of eight classification scenarios for mapping cropping patterns; namely using (i) only Sentinel-2 reflectance bands (S2), (ii) S2 and S2 derived vegetation indices (VIs); (iii) S2 and S2 vegetation phenology (VP); (iv) S2 and Sentinel-1 radar backscatter data (S1); (v) S2, VIs, and S1; (vi) S2, VP, and S1; (vii) S2, VIs and VP, and (viii) S2, VIs, VP and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The guided regularized random forest (GRRF) algorithm was used to select the optimum variables and to perform the respective classification for each scenario. The most accurate result of the overall accuracy of 93.16% was attained from the scenario (viii) S2, VIs, VP, and S1. The McNemar’s test of significance did not show significant differences (p≤0.05) among the tested scenarios. Our study demonstrated the strength of GRRF and the synergetic advantage of S2 and S1 derivatives to map cropping patterns in a heterogeneous landscape where high resolution imagery are inaccessible. Our cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions.

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Section: Discussionmentioning

confidence: 99%

Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data

Aduvukha¹,

Abdel‐Rahman²,

Sichangi³

et al. 2020

Preprint

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“…They showed that the landscape in the agricultural lowlands was characterized by connectedness (high values of Patch Cohesion Index) and simple geometries (low values of fractal dimension index), whereas the landscape pattern of the pastoral uplands was found to be highly diverse (high Shannon Diversity index). Panigrahy et al (2005) and Panigrahy et al (2011) used landscape composition metrics to assess and evaluate the efficiency and sustainability of the agricultural systems in India. They proposed and calculated three indices, namely the Multiple Cropping Index (MCI), Area Diversity Index (ADI), and Cultivated Land Utilization Index (CLUI), using three satellite-derived seasonal land cover maps.…”

Section: Landscape Approachmentioning

confidence: 99%

Agricultural Systems Studies Using Remote Sensing

Bégué

Arvor²,

Lelong

et al. 2018

Remote Sensing Handbook - Three Volume Set

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“…In addition, adequate characterization will improve understanding of the sustainability of food systems and how they are affected by climate. Their characterization is equally important for modeling and managing the abundance and spread of crop insect pests, diseases, and pollinators [10,11]. Nonetheless, small-scale farms (≤1.25 ha) and fragmentation of these cropping patterns in Africa, triggered by their high intra-and inter-seasonal variability, prohibit their accurate detection and characterization [10,12].…”

Section: Introductionmentioning

confidence: 99%

Cropping Pattern Mapping in an Agro-Natural Heterogeneous Landscape Using Sentinel-2 and Sentinel-1 Satellite Datasets

et al. 2021

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The quantity of land covered by various crops in a specific time span, referred to as a cropping pattern, dictates the level of agricultural production. However, retrieval of this information at a landscape scale can be challenging, especially when high spatial resolution imagery is not available. This study hypothesized that utilizing the unique advantages of multi-date and medium spatial resolution freely available Sentinel-2 (S2) reflectance bands (S2 bands), their vegetation indices (VIs) and vegetation phenology (VP) derivatives, and Sentinel-1 (S1) backscatter data would improve cropping pattern mapping in heterogeneous landscapes using robust machine learning algorithms, i.e., the guided regularized random forest (GRRF) for variable selection and the random forest (RF) for classification. This study’s objective was to map cropping patterns within three sub-counties in Murang’a County, a typical African smallholder heterogeneous farming area, in Kenya. Specifically, the performance of eight classification scenarios for mapping cropping patterns was compared, namely: (i) only S2 bands; (ii) S2 bands and VIs; (iii) S2 bands and VP; (iv) S2 bands and S1; (v) S2 bands, VIs, and S1; (vi) S2 bands, VP, and S1; (vii) S2 bands, VIs, and VP; and (viii) S2 bands, VIs, VP, and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The GRRF algorithm was used to select the optimum variables in each scenario, and the RF was used to perform the classification for each scenario. The highest overall accuracy was 94.33% with Kappa of 0.93, attained using the GRRF-selected variables of scenario (v) S2, VIs, and S1. Furthermore, McNemar’s test of significance did not show significant differences (p ≤ 0.05) among the tested scenarios. This study demonstrated the strength of GRRF in selecting the most important variables and the synergetic advantage of S2 and S1 derivatives to accurately map cropping patterns in small-scale farming-dominated landscapes. Consequently, the cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions. Additionally, these results can be used to understand the sustainability of food systems and to model the abundance and spread of crop insect pests, diseases, and pollinators.

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A Spatial Database of Cropping System and its Characteristics to Aid Climate Change Impact Assessment Studies

Cited by 17 publications

References 12 publications

Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data

Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data

Agricultural Systems Studies Using Remote Sensing

Cropping Pattern Mapping in an Agro-Natural Heterogeneous Landscape Using Sentinel-2 and Sentinel-1 Satellite Datasets

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