Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island

Morel, Julien; Todoroff, Pierre; Bégué, Agnès; Bury, Aurore; Martiné, Jean-François; Petit, Michel

doi:10.3390/rs6076620

Cited by 81 publications

(45 citation statements)

References 32 publications

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“…It is possible that our ability to predict both sow date and yield would improve even further if we had increased access to imagery throughout the growing season, with more than one image per month. This is suggested by previous studies that have found high accuracies in predicting yield with multi-temporal imagery that spans the length of the growing season [14][15][16] as well as our Landsat analyses (Figure 8). These previous studies were able to predict yields with R 2 ranging from 0.21 to 0.64 (e.g., [14][15][16]), and our results are thus comparable to accuracies achieved in previous studies using other satellite data and on-the-ground calibration data.…”

Section: Discussionsupporting

confidence: 85%

“…This is suggested by previous studies that have found high accuracies in predicting yield with multi-temporal imagery that spans the length of the growing season [14][15][16] as well as our Landsat analyses (Figure 8). These previous studies were able to predict yields with R 2 ranging from 0.21 to 0.64 (e.g., [14][15][16]), and our results are thus comparable to accuracies achieved in previous studies using other satellite data and on-the-ground calibration data. As more high-resolution satellite data become available, future work will examine how much prediction accuracies further improve with near weekly or bi-weekly imagery.…”

Section: Discussionsupporting

confidence: 85%

“…This is because these high-resolution data are costly to acquire (typically $15-$50 per square kilometer), and thus are typically used at low temporal frequency (e.g., [11,12]). However, previous studies have shown that the accuracy of mapping yields can be improved by using multiple images throughout a growing season [13][14][15][16]. Over the last decade, new low-cost microsatellites (e.g., Terra Bella's SkySat, Planet's PlanetScope) have been developed and deployed that allow for the collection of both high spatial (<5 m) and temporal resolution (<2 weeks) imagery at lower cost.…”

Section: Background and Rationalementioning

confidence: 99%

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Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data

et al. 2016

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Abstract:Remote sensing offers a low-cost method for developing spatially continuous crop production statistics across large areas and through time. Nevertheless, it has been difficult to characterize the production of individual smallholder farms, given that the land-holding size in most areas of South Asia (<2 ha) is smaller than the spatial resolution of most freely available satellite imagery, like Landsat and MODIS. In addition, existing methods to map yield require field-level data to develop and parameterize predictive algorithms that translate satellite vegetation indices to yield, yet these data are costly or difficult to obtain in many smallholder systems. To overcome these challenges, this study explores two issues. First, we employ new high spatial (2 m) and temporal (bi-weekly) resolution micro-satellite SkySat data to map sowing dates and yields of smallholder wheat fields in Bihar, India in the 2014-2015 and 2015-2016 growing seasons. Second, we compare how well we predict sowing date and yield when using ground data, like crop cuts and self-reports, versus using crop models, which require no on-the-ground data, to develop and parameterize prediction models. Overall, sow dates were predicted well (R 2 = 0.41 in 2014-2015 and R 2 = 0.62 in 2015-2016), particularly when using models that were parameterized using self-report sow dates collected close to the time of planting and when using imagery that spanned the entire growing season. We were also able to map yields fairly well (R 2 = 0.27 in 2014-2015 and R 2 = 0.33 in 2015-2016), with crop cut parameterized models resulting in the highest accuracies. While less accurate, we were able to capture the large range in sow dates and yields across farms when using models parameterized with crop model data and these estimates were able to detect known relationships between management factors (e.g., sow date, fertilizer, and irrigation) and yield. While these results are specific to our study site in India, it is likely that the methods employed and the lessons learned are applicable to smallholder systems more generally across the globe. This is of particular interest given that similar high spatio-temporal resolution micro-satellite data will become increasingly available in the coming years.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionsupporting

confidence: 85%

Section: Background and Rationalementioning

confidence: 99%

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Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data

et al. 2016

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“…These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. To monitor large areas with remote sensing, high-density temporal series are necessary, at least one image every ten days (Morel et al 2014, O'Connor et al 2012. In Italy, the Parmigiano-Reggiano area was subjected to investigation, using a persistency index of the Normalized Difference Vegetation Index (NDVI) from Landsat scenes over 10 years; PP has a persistency index value of >0.04, whereas pastures that are not permanent cycle through a range between -0.02 e -0.04 (Bocci et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Vegetation Indices From Rpas and Sentinel-2 Imagery for Detecting Permanent Pastures

Piragnolo

Lusiani²,

Pirotti

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

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“…Main information requests relate to yield predictions [2,3], area estimates [4,5], cropping patterns and farming systems [6,7], indicators of environmental degradation [8], the provision of agricultural insurance products [9] as well as indicators detecting and quantifying agricultural intensification [10,11] and high value crops [12]. Reliable and up-to-date information is even more precious in times of unpredictable hazardous impacts caused by climate change and socio-economic perturbations.…”

Section: Introductionmentioning

confidence: 99%

Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria

et al. 2015

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Abstract:The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to coarser resolution data (e.g., PROBA-V at 300 m) for a 2250 km 2 test site in Bulgaria. The focus was on winter and summer crop mapping with three to five classes. For classification, single-and multi-date spectral data were used as well as NDVI time series. Our results demonstrate that crop identification using 100 m PROBA-V data performed significantly better in all experiments compared to the PROBA-V 300 m data. PROBA-V multispectral imagery, acquired in spring (March) was the most appropriate for winter crop identification, while satellite data acquired in summer (July) was superior for summer crop identification. The classification accuracy from PROBA-V 100 m compared to PROBA-V 300 m was improved by 5.8% to 14.8% depending on crop type. Stacked multi-date satellite images with three to four images gave overall classification accuracies of 74%-77% (PROBA-V 100 m data) and 66%-70% (PROBA-V 300 m data) with four OPEN ACCESSRemote Sens. 2015, 7 13844 classes (wheat, rapeseed, maize, and sunflower). This demonstrates that three to four image acquisitions, well distributed over the growing season, capture most of the spectral and temporal variability in our test site. Regarding the PROBA-V NDVI time series, useful results were only obtained if crops were grouped into two broader crop type classes (summer and winter crops). Mapping accuracies decreased significantly when mapping more classes. Again, a positive impact of the increased spatial resolution was noted.Together, the findings demonstrate the positive effect of the 100 m resolution PROBA-V data compared to the 300 m for crop mapping. This has important implications for future data provision and strengthens the arguments for a second generation of this mission originally designed solely as a "gap-filler mission".

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Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island

Cited by 81 publications

References 32 publications

Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data

Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data

Comparison of Vegetation Indices From Rpas and Sentinel-2 Imagery for Detecting Permanent Pastures

Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria

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