> For the complete documentation index, see [llms.txt](https://docs.geopard.tech/geopard-tutorials/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.geopard.tech/geopard-tutorials/lva/api-dokumentacija/geopard-api-pieprasijumu-parskats/90.-vaicajums-sanemt-satelitattelu-datus-pec-geometrijas.md). # 90. Vaicājums: saņemt satelītattēlu datus pēc ģeometrijas Iegūt veģetācijas indeksa statistiku jebkurai ad‑hoc ģeometrijai - **nav nepieciešama reģistrēta lauka robeža**. Šis galapunkts aprēķina kopsavilkuma statistiku par pikseļiem jūsu ģeometrijas iekšienē, vienlaikus **ignorējot mākoņainos pikseļus**. Pašlaik atbalstīts pie **Sentinel2 (S2)** , **Landsat (L4/L5/L7/L8/L9)** un **Planet (PS)** piegādātājiem. {% hint style="danger" %} Šī lapa seko GeoPard API modeļiem un [autentifikāciju](https://docs.geopard.tech/geopard-tutorials/api-docs/authorization-apikey-credentials-or-oauth-2.0) kas tiek izmantota GraphQL galapunktos. {% endhint %} ## Ko jūs varat darīt * Vaicāt **veģetācijas indeksus** par **Point, MultiPoint, Polygon un MultiPolygon** ģeometrijas. * Izvēlēties attēlus **piegādātājus**: `S2`, `L4`, `L5`, `L7`, `L8`, `L9`, `PS`. * Saņemt **datētas** rezultātus par katru iegūšanu ar: * `bezmākonīgi` - frakcija no jūsu ģeometrijas, kas ir bezmākonīga, vērtību diapazons no 0 līdz 1, kur 1 nozīmē pilnīgi bez mākoniem. * `vērtība` - statistika par izvēlēto indeksu aprēķināta **tikai** uz bezmākonīgiem pikseļiem, iekļauj `min`, `max`, `avg`, `mdn`, `var`, `std`. * Pēc izvēles norādiet **diapazonus** masīvs ar indeksa vērtību diapazoniem, lai aprēķinātu seguma statistiku. Ja norādīts, atbilde iekļaus `diapazonus` lauku, kas parāda, kāds procentuālais apgabals ietilpst katrā norādītajā diapazonā. * **Katras krāsas/diapazona aizņemtais apgabals** - balstīts uz indeksa vērtību (zems => augsts) * **Seguma procents** - 0..1 vērtība katram diapazonam, kas attēlo virsmas procentu ## Atbalstītie indeksi `EVI2`, `NDVI`, `RVI`, `LAI`, `OSAVI`, `SAVI`, `GNDVI`, `IPVI`, `GCI`, `WDRVI`, `RCI`, `SBI`, `MCARI1`. Indeksa definīcijas seko GeoPard [veģetācijas indeksu katalogam](https://docs.geopard.tech/geopard-tutorials/product-tour-web-app/satellite-monitoring/indices-for-crops-and-soils#vegetation-indices) kas tiek izmantots visā API. ### Ievades parametri
LauksTipsObligātsPiezīmes
indexEnum (skatīt augstāk)✅Aprēķinājamais veģetācijas indekss.
piegādātājus[Provider!]!✅Kura no S2, L4, L5, L7, L8, L9, PS.
startDateString (ISO8601)✅Iekļaujošs sākums, piem. "2025-05-10T00:00:00.000Z".
endDateString (ISO8601)✅Ekskluzīvs beigu laiks vai iekļaujošs beigu laiks atkarībā no shēmas; saskaņojiet ar saviem citiem GeoPard vaicājumiem.
geojsonString✅A iestringots GeoJSON tipa Point / MultiPoint / Polygon / MultiPolygon (WGS84 [lon, lat]). Atcerieties izbēgt pēdiņas iekšpusē virknē.
diapazonus[[Float!]!]❌Masīvs ar [min, max] pāri indeksa vērtību diapazonu, lai aprēķinātu seguma statistiku
## **Veiktspējas vadlīnijas** * Poligona/MultiPoligona ģeometrijām turiet datumu diapazonu šauru (**\~10–15 dienas**) sakarā ar apstrādes laiku. * **Laika limits:** 30 s. Izmantojiet mazāk dienu un/vai mazāk piegādātāju, ja sasniedzat limitu. * Point/MultiPoint gadījumā parasti der lielāki diapazoni (punkti ir viegli apstrādājami). ## Atbildes struktūra ```graphql graphqlCopyEdit{ geojson # jūsu ievades ģeometrijas atspoguļojums (iestringots) data: [ { uuid # rastra scenas id provider # S2, L8, L9, ... acquisitionDate # ISO8601 cloudfree # 0..1 frakcija no bezmākonīgās platības value: { index # piem., NDVI min max avg mdn var std } ranges: { vērtība coverage } } ] } ``` ## Piemērs 1: MultiPolygon ar MCARI1 uz S2/L8/L9 5 dienu logā ### Pieprasīt ```graphql query GetSatelliteImageryDataByMultiPolygon { getSatelliteImageryDataByGeometry( ievade: { index: MCARI1 # norādiet indeksu šeit providers: [S2, L8, L9] # satelītu piegādātāji startDate: "2025-05-10T00:00:00.000Z" # poligoni: priekšroka 10–15 dienām; 30s laika limits endDate: "2025-05-15T00:00:00.000Z" geojson: "{\"type\": \"MultiPolygon\", \"coordinates\":[ [ [ [ 8.09058546, 50.21774961 ], [ 8.09291004, 50.21836077 ], [ 8.09328559, 50.21844893 ], [ 8.09341412, 50.21777585 ], [ 8.09342329, 50.21768228 ], [ 8.09342736, 50.21748691 ], [ 8.09342239, 50.21741988 ], [ 8.09331503, 50.2169872 ], [ 8.09327725, 50.21680672 ], [ 8.0932114, 50.21679383 ], [ 8.09307662, 50.216784 ], [ 8.09268795, 50.21672443 ], [ 8.09256905, 50.21670175 ], [ 8.09160986, 50.21647789 ], [ 8.09153097, 50.21647138 ], [ 8.09144745, 50.216479 ], [ 8.09128792, 50.21653615 ], [ 8.09124139, 50.21654808 ], [ 8.09119362, 50.21655407 ], [ 8.09085379, 50.21653536 ], [ 8.09039423, 50.21649463 ], [ 8.09006609, 50.21646137 ], [ 8.08994025, 50.21644261 ], [ 8.08956206, 50.21636986 ], [ 8.08937343, 50.21632184 ], [ 8.08881276, 50.21616093 ], [ 8.08860924, 50.21608589 ], [ 8.08848952, 50.21605007 ], [ 8.08841856, 50.21616403 ], [ 8.08836721, 50.2162167 ], [ 8.08830625, 50.21626762 ], [ 8.08801583, 50.21648103 ], [ 8.08783363, 50.21660479 ], [ 8.08757577, 50.21677569 ], [ 8.08741435, 50.21686857 ], [ 8.08769592, 50.21696741 ], [ 8.08853411, 50.21720846 ], [ 8.08965086, 50.2174967 ], [ 8.09058546, 50.21774961 ] ] ] ]}" } ) { geojson data { uuid provider acquisitionDate cloudfree # 0..1 value { # statistika aprēķināta tikai uz bezmākonīgiem pikseļiem index min max avg mdn var std } } } } ``` ### Atbilde ```json { "data": { "getSatelliteImageryDataByGeometry": { "geojson": "\"{\\\"type\\\": \\\"MultiPolygon\\\", \\\"coordinates\\\": [[[[8.09058546, 50.21774961], [8.09291004, 50.21836077], [8.09328559, 50.21844893], [8.09341412, 50.21777585], [8.09342329, 50.21768228], [8.09342736, 50.21748691], [8.09342239, 50.21741988], [8.09331503, 50.2169872], [8.09327725, 50.21680672], [8.0932114, 50.21679383], [8.09307662, 50.216784], [8.09268795, 50.21672443], [8.09256905, 50.21670175], [8.09160986, 50.21647789], [8.09153097, 50.21647138], [8.09144745, 50.216479], [8.09128792, 50.21653615], [8.09124139, 50.21654808], [8.09119362, 50.21655407], [8.09085379, 50.21653536], [8.09039423, 50.21649463], [8.09006609, 50.21646137], [8.08994025, 50.21644261], [8.08956206, 50.21636986], [8.08937343, 50.21632184], [8.08881276, 50.21616093], [8.08860924, 50.21608589], [8.08848952, 50.21605007], [8.08841856, 50.21616403], [8.08836721, 50.2162167], [8.08830625, 50.21626762], [8.08801583, 50.21648103], [8.08783363, 50.21660479], [8.08757577, 50.21677569], [8.08741435, 50.21686857], [8.08769592, 50.21696741], [8.08853411, 50.21720846], [8.08965086, 50.2174967], [8.09058546, 50.21774961]]]]}\"", "data": [ { "uuid": "16c45077-3138-5a9a-9947-d9e54ea77e38", "provider": "S2", "acquisitionDate": "2025-05-13T10:37:03.661000+00:00", "cloudfree": 1, "value": { "index": "MCARI1", "min": 3026.88, "max": 6452.881, "avg": 5020.917, "mdn": 5086.56, "var": 437905.281, "std": 661.744 } }, { "uuid": "cf5d9e51-c0db-5e02-bf97-35c3e7a0fe07", "provider": "S2", "acquisitionDate": "2025-05-13T10:47:13.154000+00:00", "cloudfree": 1, "value": { "index": "MCARI1", "min": 3441.96, "max": 6722.04, "avg": 5333.138, "mdn": 5403.36, "var": 372277.75, "std": 610.146 } }, { "uuid": "de9b6b8a-a195-5694-98a0-f17e8122855d", "provider": "S2", "acquisitionDate": "2025-05-11T10:47:17.500000+00:00", "cloudfree": "0.5547445255474452", "value": { "index": "MCARI1", "min": 3438.96, "max": 6495.96, "avg": 5176.501, "mdn": 5181, "var": 377633.031, "std": 614.519 } }, { "uuid": "917b3657-3ddc-5f43-b39f-5d8493282bde", "provider": "S2", "acquisitionDate": "2025-05-10T10:37:15.656000+00:00", "cloudfree": 1, "value": { "index": "MCARI1", "min": 3395.88, "max": 6272.881, "avg": 4975.816, "mdn": 4999.56, "var": 333405.031, "std": 577.412 } }, { "uuid": "3fc7ed30-677a-5430-a10b-3490d903616c", "provider": "L8", "acquisitionDate": "2025-05-12T10:21:17.302000+00:00", "cloudfree": 1, "value": { "index": "MCARI1", "min": 3171.24, "max": 5361, "avg": 4227.754, "mdn": 4267.68, "var": 261266, "std": 511.142 } }, { "uuid": "f3d1bd32-e3c8-5260-ba4c-d84c6a171986", "provider": "L9", "acquisitionDate": "2025-05-13T10:15:17.115000+00:00", "cloudfree": 1, "value": { "index": "MCARI1", "min": 3353.52, "max": 5451.72, "avg": 4428.534, "mdn": 4491.48, "var": 306042.719, "std": 553.211 } } ] } } } ``` ## Piemērs 2: MultiPoint ar NDVI uz S2/L8/L9 5 dienu logā ### Pieprasīt ```graphql query GetSatelliteImageryDataByMultiPoint { getSatelliteImageryDataByGeometry( ievade: { providers: [S2, L8, L9] indekss: NDVI startDate: "2025-05-10T00:00:00.000Z" # punkti ir viegli; lielāki logi OK; 30s laika limits endDate: "2025-05-15T00:00:00.000Z" geojson: "{\"type\": \"MultiPoint\", \"coordinates\": [[8.088754, 50.216859],[8.092651, 50.216905],[ 8.090799, 50.217442]]}" } ) { geojson data { uuid provider acquisitionDate bezmākonīgi value { index min max avg mdn var std } } } } ``` ### Atbilde ```json { "data": { "getSatelliteImageryDataByGeometry": { "geojson": "\"{\\\"type\\\": \\\"MultiPoint\\\", \\\"coordinates\\\": [[8.088754, 50.216859], [8.092651, 50.216905], [8.090799, 50.217442]]}\"", "data": [ { "uuid": "cf5d9e51-c0db-5e02-bf97-35c3e7a0fe07", "provider": "S2", "acquisitionDate": "2025-05-13T10:47:13.154000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.712, "max": 0.873, "avg": 0.815, "mdn": 0.824, "var": 0.002, "std": 0.041 } }, { "uuid": "16c45077-3138-5a9a-9947-d9e54ea77e38", "provider": "S2", "acquisitionDate": "2025-05-13T10:37:03.661000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.642, "max": 0.856, "avg": 0.779, "mdn": 0.794, "var": 0.003, "std": 0.055 } }, { "uuid": "de9b6b8a-a195-5694-98a0-f17e8122855d", "provider": "S2", "acquisitionDate": "2025-05-11T10:47:17.500000+00:00", "cloudfree": "0.6363636363636364", "value": { "index": "NDVI", "min": 0.762, "max": 0.857, "avg": 0.826, "mdn": 0.83, "var": 0.001, "std": 0.029 } }, { "uuid": "917b3657-3ddc-5f43-b39f-5d8493282bde", "provider": "S2", "acquisitionDate": "2025-05-10T10:37:15.656000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.667, "max": 0.854, "avg": 0.79, "mdn": 0.802, "var": 0.002, "std": 0.048 } }, { "uuid": "3fc7ed30-677a-5430-a10b-3490d903616c", "provider": "L8", "acquisitionDate": "2025-05-12T10:21:17.302000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.746, "max": 0.87, "avg": 0.814, "mdn": 0.833, "var": 0.002, "std": 0.039 } }, { "uuid": "f3d1bd32-e3c8-5260-ba4c-d84c6a171986", "provider": "L9", "acquisitionDate": "2025-05-13T10:15:17.115000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.707, "max": 0.85, "avg": 0.795, "mdn": 0.813, "var": 0.002, "std": 0.05 } } ] } } } ``` ## Piemērs 3: MultiPolygon ar NDVI uz S2/L8/L9 ar diapazoniem 5 dienu logā ### Pieprasīt ```graphql query GetSatelliteImageryDataByMultiPolygon { getSatelliteImageryDataByGeometry( ievade: { index: NDVI # norādiet indeksu šeit providers: [S2, L8, L9] # satelītu piegādātāji startDate: "2025-05-10T00:00:00.000Z" # poligoni: priekšroka 10–15 dienām; 30s laika limits endDate: "2025-05-15T00:00:00.000Z" geojson: "{\"type\": \"MultiPolygon\", \"coordinates\":[ [ [ [ 8.09058546, 50.21774961 ], [ 8.09291004, 50.21836077 ], [ 8.09328559, 50.21844893 ], [ 8.09341412, 50.21777585 ], [ 8.09342329, 50.21768228 ], [ 8.09342736, 50.21748691 ], [ 8.09342239, 50.21741988 ], [ 8.09331503, 50.2169872 ], [ 8.09327725, 50.21680672 ], [ 8.0932114, 50.21679383 ], [ 8.09307662, 50.216784 ], [ 8.09268795, 50.21672443 ], [ 8.09256905, 50.21670175 ], [ 8.09160986, 50.21647789 ], [ 8.09153097, 50.21647138 ], [ 8.09144745, 50.216479 ], [ 8.09128792, 50.21653615 ], [ 8.09124139, 50.21654808 ], [ 8.09119362, 50.21655407 ], [ 8.09085379, 50.21653536 ], [ 8.09039423, 50.21649463 ], [ 8.09006609, 50.21646137 ], [ 8.08994025, 50.21644261 ], [ 8.08956206, 50.21636986 ], [ 8.08937343, 50.21632184 ], [ 8.08881276, 50.21616093 ], [ 8.08860924, 50.21608589 ], [ 8.08848952, 50.21605007 ], [ 8.08841856, 50.21616403 ], [ 8.08836721, 50.2162167 ], [ 8.08830625, 50.21626762 ], [ 8.08801583, 50.21648103 ], [ 8.08783363, 50.21660479 ], [ 8.08757577, 50.21677569 ], [ 8.08741435, 50.21686857 ], [ 8.08769592, 50.21696741 ], [ 8.08853411, 50.21720846 ], [ 8.08965086, 50.2174967 ], [ 8.09058546, 50.21774961 ] ] ] ]}" ranges: [ [0.0, 0.5], [0.5, 0.55], [0.55, 0.6], [0.6, 0.65], [0.65, 0.7], [0.7, 0.75], [0.75, 0.8], [0.8, 0.85], [0.85, 0.9] ] } ) { geojson data { uuid provider acquisitionDate cloudfree # 0..1 value { # statistika aprēķināta tikai uz bezmākonīgiem pikseļiem index min max avg mdn var std } ranges { vērtība coverage # 0..1 } } } } ``` ### Atbilde ```json { "data": { "getSatelliteImageryDataByGeometry": { "geojson": "{\"type\":\"MultiPolygon\",\"coordinates\":[[[[8.09058546,50.21774961],[8.09291004,50.21836077],[8.09328559,50.21844893],[8.09341412,50.21777585],[8.09342329,50.21768228],[8.09342736,50.21748691],[8.09342239,50.21741988],[8.09331503,50.2169872],[8.09327725,50.21680672],[8.0932114,50.21679383],[8.09307662,50.216784],[8.09268795,50.21672443],[8.09256905,50.21670175],[8.09160986,50.21647789],[8.09153097,50.21647138],[8.09144745,50.216479],[8.09128792,50.21653615],[8.09124139,50.21654808],[8.09119362,50.21655407],[8.09085379,50.21653536],[8.09039423,50.21649463],[8.09006609,50.21646137],[8.08994025,50.21644261],[8.08956206,50.21636986],[8.08937343,50.21632184],[8.08881276,50.21616093],[8.08860924,50.21608589],[8.08848952,50.21605007],[8.08841856,50.21616403],[8.08836721,50.2162167],[8.08830625,50.21626762],[8.08801583,50.21648103],[8.08783363,50.21660479],[8.08757577,50.21677569],[8.08741435,50.21686857],[8.08769592,50.21696741],[8.08853411,50.21720846],[8.08965086,50.2174967],[8.09058546,50.21774961]]]]}", "data": [ { "uuid": "cf5d9e51-c0db-5e02-bf97-35c3e7a0fe07", "provider": "S2", "acquisitionDate": "2025-05-13T10:47:13.154000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.625, "max": 0.873, "avg": 0.795, "mdn": 0.808, "var": 0.003, "std": 0.051 }, "ranges": [ { "value": [0, 0.5], "coverage": 0 }, { "value": [0.5, 0.55], "coverage": 0 }, { "value": [0.55, 0.6], "coverage": 0 }, { "value": [0.6, 0.65], "coverage": 0.0089 }, { "value": [0.65, 0.7], "coverage": 0.0514 }, { "value": [0.7, 0.75], "coverage": 0.1288 }, { "value": [0.75, 0.8], "coverage": 0.2603 }, { "value": [0.8, 0.85], "coverage": 0.4575 }, { "value": [0.85, 0.9], "coverage": 0.0932 } ] }, { "uuid": "917b3657-3ddc-5f43-b39f-5d8493282bde", "provider": "S2", "acquisitionDate": "2025-05-10T10:37:15.656000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.585, "max": 0.854, "avg": 0.768, "mdn": 0.78, "var": 0.003, "std": 0.056 }, "ranges": [ { "value": [0, 0.5], "coverage": 0 }, { "value": [0.5, 0.55], "coverage": 0 }, { "value": [0.55, 0.6], "coverage": 0.0014 }, { "value": [0.6, 0.65], "coverage": 0.0342 }, { "value": [0.65, 0.7], "coverage": 0.1205 }, { "value": [0.7, 0.75], "coverage": 0.161 }, { "value": [0.75, 0.8], "coverage": 0.3233 }, { "value": [0.8, 0.85], "coverage": 0.3479 }, { "value": [0.85, 0.9], "coverage": 0.0116 } ] }, { "uuid": "16c45077-3138-5a9a-9947-d9e54ea77e38", "provider": "S2", "acquisitionDate": "2025-05-13T10:37:03.661000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.548, "max": 0.856, "avg": 0.756, "mdn": 0.769, "var": 0.004, "std": 0.061 }, "ranges": [ { "value": [0, 0.5], "coverage": 0 }, { "value": [0.5, 0.55], "coverage": 0.0021 }, { "value": [0.55, 0.6], "coverage": 0.011 }, { "value": [0.6, 0.65], "coverage": 0.05 }, { "value": [0.65, 0.7], "coverage": 0.1198 }, { "value": [0.7, 0.75], "coverage": 0.2231 }, { "value": [0.75, 0.8], "coverage": 0.295 }, { "value": [0.8, 0.85], "coverage": 0.295 }, { "value": [0.85, 0.9], "coverage": 0.0041 } ] }, { "uuid": "de9b6b8a-a195-5694-98a0-f17e8122855d", "provider": "S2", "acquisitionDate": "2025-05-11T10:47:17.500000+00:00", "cloudfree": 0.6067415730337079, "value": { "index": "NDVI", "min": 0.613, "max": 0.865, "avg": 0.782, "mdn": 0.793, "var": 0.003, "std": 0.054 }, "ranges": [ { "value": [0, 0.5], "coverage": 0 }, { "value": [0.5, 0.55], "coverage": 0 }, { "value": [0.55, 0.6], "coverage": 0 }, { "value": [0.6, 0.65], "coverage": 0.0311 }, { "value": [0.65, 0.7], "coverage": 0.0377 }, { "value": [0.7, 0.75], "coverage": 0.1964 }, { "value": [0.75, 0.8], "coverage": 0.3097 }, { "value": [0.8, 0.85], "coverage": 0.3541 }, { "value": [0.85, 0.9], "coverage": 0.071 } ] }, { "uuid": "3fc7ed30-677a-5430-a10b-3490d903616c", "provider": "L8", "acquisitionDate": "2025-05-12T10:21:17.302000+00:00", "cloudfree": 1, "value": { "index": "NDVI", "min": 0.619, "max": 0.87, "avg": 0.781, "mdn": 0.793, "var": 0.003, "std": 0.055 }, "ranges": [ { "value": [0, 0.5], "coverage": 0 }, { 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"coverage": 0.0099 } ] } ] } } } ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.geopard.tech/geopard-tutorials/lva/api-dokumentacija/geopard-api-pieprasijumu-parskats/90.-vaicajums-sanemt-satelitattelu-datus-pec-geometrijas.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. 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